what is knowledge representation in expert system

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Expert Systems in AI

Types of expert systems in ai.

• Rule-Based Expert Systems: These systems use a collection of rules to make decisions. Rules are created by human experts and guide the system’s reasoning process. An example is Mycin, an expert system for diagnosing bacterial infections.
• Frame-Based Expert Systems: Frame-based expert systems use frame representation to organize knowledge. Frames capture structured information about entities and their attributes, allowing the system to reason about specific instances. For example, an expert system for car insurance might use frames to represent different types of coverage and associated costs.
• Fuzzy Expert Systems: Fuzzy expert systems handle imprecise or uncertain data using fuzzy logic. This allows the system to reason with degrees of truth rather than binary values. Fuzzy expert systems are useful in domains where precise measurements are difficult or subjective, such as weather forecasting or risk assessment.
• Neural Expert Systems: Neural expert systems utilize neural networks to learn from data through training processes. Neural networks can recognize patterns and make predictions based on input data. They are particularly effective in areas such as image recognition and natural language processing.
• Neuro-Fuzzy Expert Systems: Neuro-fuzzy expert systems combine elements of fuzzy logic and neural networks to make decisions based on both numerical and linguistic information. These systems excel in complex domains where uncertainty and imprecision are prevalent, such as financial forecasting or traffic management.

Advantages and Benefits of Expert Systems

• Expert systems in AI provide increased accuracy by leveraging expert knowledge stored in their knowledge bases. This ensures consistent decision-making even in complex scenarios.
• They are highly scalable as they can handle large amounts of information efficiently. This makes them suitable for managing complex domains with vast amounts of data.
• Moreover, expert systems in AI can be cost-effective by reducing the need for human experts, resulting in significant cost savings.
• It enhances decision-making by providing relevant data and expertise to support informed choices.

Applications of Expert Systems

Expert systems in ai examples.

Expert systems in AI leverage advanced knowledge representation and rule-based reasoning to emulate human expertise, finding applications in diverse domains.

MYCIN: An early expert system, utilised rule-based reasoning to diagnose bacterial infections. It analysed patient symptoms and medical history, providing recommendations for antibiotic treatments based on expert knowledge encoded in the system.

Dendral: Dendral, one of the earliest expert systems, focused on organic chemistry analysis. In troubleshooting scenarios, similar rule-based reasoning is applied where expert systems analyse complex systems to identify and resolve issues.

R1/XCON: Another early expert system, showcased the ability to choose specific software components to generate a customised computer system based on user preferences. It utilised rule-based reasoning to make software selections tailored to individual requirements.

PXDES: This expert system in medical diagnostics could accurately determine the type and severity of lung cancer in patients based on limited data. Its rule-based approach enabled precise diagnoses, showcasing the power of expert systems in healthcare.

CaDet: A clinical support expert system, that specialises in identifying cancer in its early stages. By employing rule-based reasoning, CaDet assists healthcare professionals in early detection, significantly improving the chances of successful intervention and treatment.

DXplain: This clinical support expert system goes beyond specific conditions. It suggests a variety of diseases based on a doctor’s findings, showcasing the system’s capacity for comprehensive disease identification. DXplain’s rule-based approach enhances diagnostic accuracy in healthcare settings

Knowledge Representation Techniques in AI

• Logical Representation involves using formal languages such as propositional logic, first-order logic, and predicate calculus to represent facts and relationships. This allows the system to apply logical reasoning to arrive at conclusions. For example, an expert system in medical diagnosis might use logical representation to infer a specific disease based on symptoms and medical history.
• Semantic Networks provide a graphical representation of concepts and their relationships. Nodes represent concepts, while links depict relationships between concepts. This technique is useful for representing hierarchical structures and complex relationships. For instance, an expert system for natural language processing might use a semantic network to represent the relationships between words in a sentence.
• Frame Representation involves organizing knowledge into frames that represent objects, concepts, or situations with attributes and slots. Frames capture structured information about entities in a domain and allow reasoning based on these attributes and slots. An example of frame-based representation is the use of frames to represent different car models, where each frame contains attributes like colour, engine type, and price range.
• Production rules are used in expert systems to represent knowledge in the form of IF-THEN statements. These rules guide the system’s reasoning process by specifying conditions and corresponding actions. For example, an expert system for troubleshooting computer issues might have a production rule that states: IF the computer does not start, THEN check the power supply.

Knowledge Representation in AI Examples

Knowledge representation in AI involves encoding information about the world in a format that a computer system can utilise to solve complex tasks. Here are some examples of knowledge representation in AI :

Semantic Networks: In a graphical structure, relationships between objects are represented. For example, a semantic network may illustrate that “cat” falls under the category of “animal,” and “animal” is categorised as a subclass of “living things.”

Frames: Objects or concepts, along with their attributes and relationships, are represented using frames. Consider a car, where the frame encompasses attributes such as “color,” “manufacturer,” and “fuel type.”

Rule-Based Systems: Knowledge is represented using a set of rules. For medical diagnosis, a rule could state, “if a patient exhibits a high temperature and cough, then they may have the flu.”

Ontologies: To represent relationships between concepts in a specific domain, ontologies are developed. In biology, for instance, an ontology might delineate relationships between different species and their characteristics.

Knowledge Graphs: Entities and their relationships are represented through the construction of a knowledge graph. Google’s Knowledge Graph, for instance, links entities like people, places, and things, offering context-aware information in search results.

First-Order Logic: Logical expressions are employed to represent knowledge. For example, the statement “All humans are mortal” can be expressed as ∀x (Human(x) → Mortal(x)).

Probabilistic Graphical Models: Probabilistic relationships between variables are represented using Bayesian networks. In medical diagnosis, a Bayesian network might articulate the probability of various symptoms given a specific disease.

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expert system

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• Purdue University - College of Engineering - Building Expert Systems

expert system , a computer program that uses artificial-intelligence methods to solve problems within a specialized domain that ordinarily requires human expertise. The first expert system was developed in 1965 by Edward Feigenbaum and Joshua Lederberg of Stanford University in California, U.S. Dendral, as their expert system was later known, was designed to analyze chemical compounds . Expert systems now have commercial applications in fields as diverse as medical diagnosis , petroleum engineering , and financial investing.

In order to accomplish feats of apparent intelligence, an expert system relies on two components: a knowledge base and an inference engine . A knowledge base is an organized collection of facts about the system’s domain. An inference engine interprets and evaluates the facts in the knowledge base in order to provide an answer. Typical tasks for expert systems involve classification, diagnosis, monitoring, design, scheduling, and planning for specialized endeavours.

Facts for a knowledge base must be acquired from human experts through interviews and observations. This knowledge is then usually represented in the form of “if-then” rules (production rules): “If some condition is true, then the following inference can be made (or some action taken).” The knowledge base of a major expert system includes thousands of rules. A probability factor is often attached to the conclusion of each production rule and to the ultimate recommendation, because the conclusion is not a certainty. For example, a system for the diagnosis of eye diseases might indicate, based on information supplied to it, a 90 percent probability that a person has glaucoma , and it might also list conclusions with lower probabilities. An expert system may display the sequence of rules through which it arrived at its conclusion; tracing this flow helps the user to appraise the credibility of its recommendation and is useful as a learning tool for students.

Human experts frequently employ heuristic rules, or “rules of thumb,” in addition to simple production rules, such as those gleaned from engineering handbooks. Thus, a credit manager might know that an applicant with a poor credit history, but a clean record since acquiring a new job, might actually be a good credit risk. Expert systems have incorporated such heuristic rules and increasingly have the ability to learn from experience. Expert systems remain aids to, rather than replacements for, human experts.

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02 intermediate, 03 advanced, 04 training programs, knowledge representation in ai - types, issues, & techniques, artificial intelligence certification course, knowledge representation in ai - overview, what is knowledge representation in ai, types of knowledge in ai, 1.) declarative knowledge, 2.) procedural knowledge, 3.) meta knowledge, the knowledge cycle in ai, approaches to knowledge representation in ai, techniques of knowledge representation in ai, advantages and limitations of different techniques of knowledge representation, logic-based methods, semantic networks, scripts and frames, real-world applications of knowledge representation in ai, challenges and future directions in knowledge representation in ai, best practices of knowledge representation in ai, resources for further learning and practice, live classes schedule.

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KNOWLEDGE REPRESENTATION IN EXPERT SYSTEMS

Journal title, journal issn, volume title, description, collections, libraries & cultural resources.

• 403.220.8895

Knowledge Representation in AI: The Foundation of Intelligent Systems

Knowledge representation plays a crucial role in the field of artificial intelligence (AI), serving as the bedrock for the development of intelligent systems. By allowing AI systems to organize and process information in a way that imitates human cognition, knowledge representation bridges the gap between raw data and meaningful insights. This article explores the various techniques and approaches used in AI knowledge representation , highlighting its importance and applications in different domains.

Key Takeaways:

• AI knowledge representation is essential for organizing and processing information in a way that mimics human cognition.
• Techniques like semantic networks , frames , and ontologies enable AI systems to capture and reason with hierarchical, associative, and formal relationships.
• Knowledge representation is crucial for AI reasoning, decision-making, and providing valuable recommendations in various domains.
• Challenges in knowledge representation include ambiguity and vagueness in natural language and handling complex and scalable data.
• Applications of knowledge representation span expert systems , natural language processing , and robotics , enhancing capabilities in decision-making, language understanding, and physical interaction.

Understanding Knowledge Representation

Knowledge representation is a fundamental process in artificial intelligence (AI) that enables AI systems to comprehend and manipulate information. By structuring and organizing data in a way that mirrors human understanding, knowledge representation facilitates the reasoning and decision-making capabilities of AI systems. Various techniques, such as semantic networks , frames , and ontologies , are employed to capture the hierarchical, associative, and formal relationships between concepts, enhancing the AI system’s ability to generate intelligent responses.

One common technique used in knowledge representation is semantic networks . These networks utilize interconnected nodes and edges to represent concepts and the relationships between them. This approach allows for the capture of hierarchical knowledge structures, enabling AI systems to understand the context and associations between different concepts. Frames , on the other hand, involve organizing knowledge into predefined categories known as frames, which contain attributes, properties, and relationships. This technique allows for the structured representation of knowledge, making it easier for AI systems to process and reason with information.

“Knowledge representation enables AI systems to capture the essence of human cognition, allowing them to mimic our thought processes and generate intelligent responses.” – AI Expert

Ontologies provide a formal structure for knowledge representation and are often used in domains where precise classification and categorization are required. These hierarchical representations allow for efficient retrieval and inference by AI systems, enabling them to navigate complex knowledge domains effectively. By employing these techniques, AI systems can understand and interpret information in a manner similar to human cognition, facilitating problem-solving, decision-making, and generating valuable insights.

Table: Comparison of Knowledge Representation Techniques

The choice of knowledge representation technique depends on the specific requirements and characteristics of the AI application. Each technique has its strengths and limitations, and the selection should be based on the nature of the knowledge and the intended use of the AI system.

Importance of Knowledge Representation in AI

Knowledge representation plays a crucial role in the field of artificial intelligence (AI) by enabling reasoning and decision-making. AI systems need a structured format to process and understand information, allowing them to derive valuable insights and make informed choices. By representing knowledge in a way that mirrors human cognition, AI systems can effectively solve complex problems and provide relevant recommendations in various domains.

AI reasoning and decision-making heavily rely on the structured representation of knowledge. Without a well-organized framework, AI systems would struggle to comprehend and analyze vast amounts of information. Knowledge representation allows AI systems to capture the hierarchical, associative, and formal relationships among concepts, providing a foundation for intelligent reasoning.

Additionally, knowledge representation empowers AI systems to handle uncertainty and ambiguity inherent in human language and real-world data. By encapsulating knowledge in structured formats such as semantic networks, frames, and ontologies, AI systems can better understand context, disambiguate meaning, and make accurate interpretations. This enhanced comprehension enables AI systems to generate intelligent responses and contribute to effective decision-making processes.

The importance of knowledge representation in AI cannot be overstated. It forms the basis for AI systems to reason, learn, and make informed decisions. By representing knowledge in a structured and meaningful way, AI systems can bridge the gap between raw data and valuable insights, leading to advancements in various domains and contributing to the development of more intelligent systems.

Techniques of Knowledge Representation

In the field of artificial intelligence (AI), several techniques are commonly used for knowledge representation. These techniques play a vital role in enabling AI systems to understand and process information effectively. Some of the prominent techniques are semantic networks, frames, and ontologies.

1. Semantic Networks:

Semantic networks are graphical representations that depict the relationships between different concepts. In a semantic network, nodes represent individual concepts, while edges illustrate the connections or associations between these concepts. This technique allows AI systems to capture hierarchical and associative knowledge, enabling them to understand complex relationships and make informed decisions. Semantic networks provide a structured framework for organizing information, making it easier for AI systems to retrieve relevant data and draw meaningful insights.

Frames involve the organization of knowledge into predefined categories known as frames. Each frame consists of attributes, properties, and relationships that define a particular concept or object. By using frames, AI systems can represent knowledge in a structured manner, allowing for efficient reasoning and decision-making. Frames provide a way to capture important characteristics and associations related to specific concepts, enhancing the AI system’s understanding and ability to generate intelligent responses.

3. Ontologies:

Ontologies provide a formal and explicit representation of knowledge. They establish a hierarchy of concepts and their relationships, similar to a taxonomy. Ontologies not only define the concepts but also specify the properties, attributes, and constraints associated with each concept. This technique enables AI systems to reason and infer based on the defined rules and relationships within the ontology. Ontologies offer a systematic approach to knowledge representation, facilitating efficient knowledge acquisition, storage, and retrieval.

These techniques of knowledge representation, namely semantic networks, frames, and ontologies, provide AI systems with the necessary tools to organize, understand, and reason with information effectively. Each technique has its unique advantages and applications, allowing AI systems to tackle complex problems and generate intelligent responses. The choice of technique depends on the specific requirements and characteristics of the AI system and the domain in which it operates.

Challenges in Knowledge Representation

Knowledge representation in AI faces various challenges that impact its effectiveness and efficiency. Two major challenges in knowledge representation are Ambiguity and Vagueness , and Scalability and Complexity .

Ambiguity and Vagueness

Ambiguity refers to the presence of multiple interpretations or meanings for a given piece of information. In natural language, words and phrases may have different meanings depending on the context, making it challenging to represent knowledge accurately. Vagueness, on the other hand, refers to the lack of clarity or precision in defining concepts or boundaries.

The representation of ambiguous and vague information requires AI systems to handle uncertainty and make probabilistic inferences. Techniques such as fuzzy logic and probabilistic reasoning can be used to manage ambiguity and vagueness , allowing AI systems to make informed decisions even when dealing with imprecise or uncertain knowledge.

Scalability and Complexity

As knowledge bases grow in size and complexity, maintaining a coherent and efficient representation becomes a significant challenge. The scalability challenge arises due to the need to handle vast amounts of data and ensure quick and accurate retrieval. AI systems must efficiently organize and index knowledge for efficient search and retrieval, which becomes increasingly difficult as the volume of data increases.

Furthermore, the complexity of knowledge representation stems from the need to capture diverse and intricate relationships between concepts and entities. Representing highly interconnected knowledge requires sophisticated techniques such as ontologies and semantic networks to maintain the integrity and richness of the information.

By addressing these challenges, AI researchers and developers can enhance the quality and effectiveness of knowledge representation, thereby improving the overall performance of AI systems in reasoning, decision-making, and problem-solving tasks.

Applications of Knowledge Representation

Knowledge representation plays a vital role in various domains, including expert systems , natural language processing , and robotics . These applications leverage the power of knowledge representation techniques to enhance AI capabilities and enable intelligent interactions with humans and the physical world.

Expert Systems

In the field of expert systems , knowledge representation is instrumental in replicating the decision-making abilities of human experts. By utilizing techniques such as semantic networks, frames, and ontologies, AI systems can capture and organize domain-specific knowledge. This structured representation allows expert systems to provide valuable advice, recommendations, and solutions in various industries such as healthcare, finance, and engineering.

Natural Language Processing

Natural language processing (NLP) involves the understanding and generation of human language by AI systems. Knowledge representation plays a crucial role in NLP, enabling machines to comprehend the context of a conversation, identify relationships between words and phrases, and generate coherent responses. By representing knowledge in a structured format, AI systems can analyze and interpret language more effectively, leading to improved communication and interaction with humans.

Knowledge representation is essential in the field of robotics as it enables machines to navigate and interact with the physical world. By representing knowledge about the environment, objects, and actions, AI systems can understand their surroundings and make informed decisions. This allows robots to perform tasks efficiently, ensure safe movements, and adapt to dynamic situations. Knowledge representation empowers robots to learn from previous experiences and apply that knowledge in real-time, enhancing their overall functionality and autonomy.

As AI continues to advance, knowledge representation will play an even more significant role in shaping intelligent systems. Its applications in expert systems, natural language processing, and robotics demonstrate the wide-reaching impact of structured knowledge on enhancing AI capabilities. By refining knowledge representation techniques , AI researchers and practitioners are working towards developing more intelligent, adaptable, and autonomous systems that can revolutionize various industries and improve the lives of individuals worldwide.

The Future of Knowledge Representation

As artificial intelligence (AI) continues to advance and transform various industries, the field of knowledge representation is also evolving to keep pace with the growing complexity of data. Advancements in knowledge representation are crucial for AI systems to effectively handle dynamic and unstructured data, enabling them to make more intelligent decisions and generate meaningful insights.

One of the key areas of focus for future advancements in knowledge representation is the handling of dynamic data. Traditional knowledge representation techniques are often based on static knowledge bases, which can limit the ability of AI systems to adapt and learn from real-time data. By developing techniques that can handle dynamic data, AI systems will be able to continuously update and refine their knowledge, leading to more accurate and up-to-date decision-making.

Another important aspect of future knowledge representation is the handling of unstructured data. Unstructured data, such as text, images, and videos, poses a significant challenge for AI systems, as it lacks a predefined format and structure. Advancements in natural language processing and computer vision are enabling AI systems to better understand and interpret unstructured data, allowing for more effective knowledge representation and analysis.

In summary, the future of knowledge representation in AI holds immense potential for advancements in handling dynamic and unstructured data. By developing techniques that can adapt to real-time data and effectively interpret unstructured information, AI systems will be able to achieve greater levels of understanding and cognition. These advancements will pave the way for more intelligent AI systems that can make informed decisions, provide valuable insights, and revolutionize various industries.

Knowledge representation forms the very essence of AI intelligence . With a structured framework to organize and process information, AI systems gain the ability to think, learn, and reason – mirroring human cognition. The techniques and approaches employed in knowledge representation enable AI systems to decipher intricate data, solve complex problems, and make informed decisions.

As technology continues to advance, optimizing knowledge representation techniques will unlock new frontiers for AI systems to achieve unprecedented levels of understanding and cognition. These advancements empower AI to handle dynamic and unstructured data, paving the way for even smarter systems.

The future of knowledge representation holds immense potential, propelling AI intelligence to greater heights. By continuously refining these techniques, AI systems will transcend their current capabilities, bridging the gap between raw data and meaningful insights. This progress will steer AI towards enhanced cognitive abilities and pave the path for a future where intelligent systems become an indispensable part of our lives.

What is knowledge representation in AI?

Knowledge representation is the process of structuring and organizing information in a format that AI systems can comprehend and manipulate.

Why is knowledge representation important in AI?

Knowledge representation is crucial in AI because it enables reasoning, decision-making, and the generation of meaningful insights from data.

What are the techniques used in knowledge representation?

The techniques used in knowledge representation include semantic networks, frames, and ontologies.

What challenges does knowledge representation face?

Knowledge representation faces challenges related to ambiguity and vagueness in natural language, as well as scalability and complexity as knowledge bases grow in size.

Where is knowledge representation applied?

Knowledge representation finds applications in expert systems, natural language processing, and robotics, among other domains.

How is knowledge representation advancing in the future?

Efforts are being made to develop more sophisticated approaches to handle dynamic and unstructured data, allowing for even smarter AI systems.

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Expert Systems in AI

Expert systems are a crucial subset of artificial intelligence (AI) that simulate the decision-making ability of a human expert. These systems use a knowledge base filled with domain-specific information and rules to interpret and solve complex problems. Expert systems are widely used in fields such as medical diagnosis, accounting, coding, and even in games.

The article aims to provide an in-depth understanding of expert systems in AI, including their components, types, applications, and benefits.

Table of Content

Understanding Expert Systems in AI

Types of expert systems in ai, 1. rule-based expert systems, 2. frame-based expert systems, 3. fuzzy logic systems, 4. neural network-based expert systems, 5. neuro-fuzzy expert systems, examples of expert systems in ai, components and architecture of an expert system, how expert systems work, reasoning strategies used by inference engine, 1. forward chaining, 2. backward chaining, applications of expert systems, benefits of expert systems, limitations of expert systems, faqs : expert systems in ai.

An expert system is AI software that uses knowledge stored in a knowledge base to solve problems that would usually require a human expert thus preserving a human expert’s knowledge in its knowledge base. They can advise users as well as provide explanations to them about how they reached a particular conclusion or advice.

Knowledge Engineering is the term used to define the process of building an Expert System and its practitioners are called Knowledge Engineers . The primary role of a knowledge engineer is to make sure that the computer possesses all the knowledge required to solve a problem. The knowledge engineer must choose one or more forms in which to represent the required knowledge as a symbolic pattern in the memory of the computer.

In AI, expert systems are designed to emulate the decision-making abilities of human experts. They are categorized based on their underlying technology and application areas. Here are the primary types of expert systems in AI:

• Description : Use a set of “if-then” rules to process data and make decisions. These rules are typically written by human experts and capture domain-specific knowledge.
• Example : MYCIN , an early system for diagnosing bacterial infections.
• Description : Represent knowledge using frames, which are data structures similar to objects in programming. Each frame contains attributes and values related to a particular concept.
• Example : Systems used for knowledge representation in areas like natural language processing.
• Description : Handle uncertain or imprecise information using fuzzy logic , which allows for partial truths rather than binary true/false values.
• Example : Fuzzy control systems for managing household appliances like washing machines and air conditioners.
• Description : Use artificial neural networks to learn from data and make predictions or decisions based on learned patterns. They are often used for tasks involving pattern recognition and classification.
• Example : Deep learning models for image and speech recognition.
• Description : Integrate neural networks and fuzzy logic to combine the learning capabilities of neural networks with the handling of uncertainty and imprecision offered by fuzzy logic. This hybrid approach helps in dealing with complex problems where both pattern recognition and uncertain reasoning are required.
• Example: Automated control systems that adjust based on uncertain environmental conditions or financial forecasting models that handle both quantitative data and fuzzy inputs.

There are many examples of an expert system. Some of them are given below:

• Overview : MYCIN is one of the earliest and most influential expert systems developed in the 1970s. It was specifically designed for medical diagnosis.
• Functionality : MYCIN uses backward chaining to diagnose bacterial infections, such as meningitis and bacteremia. It identifies the bacteria causing the infection by asking the doctor a series of questions about the patient’s symptoms and test results.
• Significance : Although not used clinically, MYCIN greatly influenced the development of medical expert systems.
• Overview : DENDRAL is another pioneering expert system, developed in the 1960s, and is regarded as one of the first successful AI systems in the field of chemistry.
• Functionality : DENDRAL was designed to analyze chemical compounds. It uses spectrographic data (data obtained from spectroscopy) to predict the molecular structure of a substance.
• Significance : DENDRAL revolutionized chemical research by automating the analysis of mass spectrometry data.
• Overview : R1, also known as XCON, was developed in the late 1970s by Digital Equipment Corporation (DEC) and is one of the most commercially successful expert systems.
• Functionality : R1/XCON was used to configure orders for new computer systems. It would select the appropriate hardware and software components based on the customer’s requirements.
• Significance : R1/XCON streamlined system configuration, saving DEC millions by reducing errors and improving efficiency.
• Overview : PXDES is an expert system designed for the medical field, particularly in the diagnosis of lung cancer.
• Functionality : PXDES could analyze patient data, including imaging results, to determine both the type and the stage of lung cancer. It helps in deciding the best course of treatment based on the patient’s specific condition.
• Significance: PXDES aids in accurate, timely diagnoses, improving treatment decisions in oncology.
• Overview : CaDet is a clinical support system developed to assist in the early detection of cancer.
• Functionality : CaDet can identify potential signs of cancer in its early stages by analyzing patient data and symptoms. It works by comparing patient data with known patterns and indicators of cancer.
• Significance : Early detection by CaDet enhances survival rates by enabling prompt treatment.
• Overview : DXplain is a medical expert system developed at Massachusetts General Hospital, used as a clinical decision support tool.
• Functionality : DXplain suggests possible diseases based on the symptoms and findings provided by a doctor. It acts as a reference tool, offering a differential diagnosis list that doctors can use to check their own diagnoses.
• Significance: DXplain broadens diagnostic possibilities, helping medical professionals consider rare conditions.
• Knowledge Base: The knowledge base represents facts and rules. It consists of knowledge in a particular domain as well as rules to solve a problem, procedures and intrinsic data relevant to the domain.
• Inference Engine: The function of the inference engine is to fetch the relevant knowledge from the knowledge base, interpret it and to find a solution relevant to the user’s problem. The inference engine acquires the rules from its knowledge base and applies them to the known facts to infer new facts. Inference engines can also include an explanation and debugging abilities.
• Knowledge Acquisition and Learning Module: The function of this component is to allow the expert system to acquire more and more knowledge from various sources and store it in the knowledge base.
• User Interface: This module makes it possible for a non-expert user to interact with the expert system and find a solution to the problem.
• Explanation Module: This module helps the expert system to give the user an explanation about how the expert system reached a particular conclusion.

Architecture of an Expert System

Expert systems operate by following a structured approach:

• Input Data : Users provide data or queries related to a specific problem or scenario.
• Processing : The inference engine processes the input data using the rules in the knowledge base to generate conclusions or recommendations.
• Output : The system presents the results or solutions to the user through the user interface.
• Explanation : If applicable, the system explains how the conclusions were reached, providing insights into the reasoning process.

Forward Chaining and Backward Chaining , which are two fundamental methods for processing information and solving problems in an expert system:

This is a data-driven reasoning approach where the system starts with the available facts and applies rules to infer new facts or conclusions. It’s typically used to predict outcomes or determine what will happen next. An example given is predicting stock market movements.

Forward Chaining

This is a goal-driven reasoning approach where the system starts with a hypothesis or a goal (something to prove) and works backward to determine which facts or conditions would support that conclusion. It’s often used to diagnose issues by determining the cause of an observed effect. The examples provided include diagnosing medical conditions like stomach pain, blood cancer, or dengue.

Backward Chaining

• Medical Diagnosis : Expert systems assist doctors by analyzing symptoms and medical history to suggest possible diagnoses or treatment options. For example, MYCIN, an early expert system, helped identify bacterial infections and recommend antibiotics.
• Financial Services : In finance, expert systems are used for credit scoring, fraud detection, and investment advice. They analyze financial data and patterns to make informed decisions.
• Technical Support : Expert systems can troubleshoot and provide solutions for technical issues. They guide users through problem-solving steps based on pre-defined rules and knowledge.
• Manufacturing : In manufacturing, expert systems help optimize production processes, perform quality control, and manage inventory by analyzing data and making recommendations.
• Consistency : Expert systems provide consistent and reliable recommendations, reducing the variability that can occur with human decision-making.
• Availability : They are available 24/7 and can handle multiple queries simultaneously, providing timely assistance and support.
• Cost-Effectiveness : By automating expert-level decision-making, organizations can save on the costs associated with hiring and training human experts.
• Knowledge Preservation : Expert systems preserve valuable knowledge and expertise, making it accessible even if the original experts are no longer available.
• Knowledge Limitation : The effectiveness of an expert system depends on the completeness and accuracy of the knowledge base. If the knowledge is outdated or incomplete, the system’s performance may be compromised.
• Lack of Flexibility : Expert systems are limited to the rules and knowledge they are programmed with. They may struggle with novel or ambiguous situations that fall outside their predefined rules.
• Maintenance : Regular updates and maintenance are required to keep the knowledge base current and relevant, which can be resource-intensive.

Expert systems are a crucial aspect of AI, providing intelligent decision-making capabilities across various domains. By emulating human expertise, they offer valuable insights, consistent solutions, and efficiency. Despite their limitations, expert systems continue to evolve and play a significant role in advancing AI technologies.

What are the 5 expert systems?

Five notable expert systems are rule based, frame based, fuzzy, neural and neuro fuzzy expert systems.

What is expert tasks in AI?

Expert tasks in AI involve solving complex problems or making decisions in specific domains, typically requiring human expertise, such as medical diagnosis or financial forecasting.

Is ChatGPT an expert system?

No, ChatGPT is not an expert system; it’s a language model based on deep learning, designed to generate text rather than relying on a fixed knowledge base and rules.

What are the layers of expert system?

The layers of an expert system typically include the knowledge base, inference engine, user interface, and explanation module.

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Expert Systems and Applied Artificial Intelligence

11.1 What is Artificial Intelligence?

The field of artificial intelligence (AI) is concerned with methods of developing systems that display aspects of intelligent behaviour. These systems are designed to imitate the human capabilities of thinking and sensing.

Characteristics of AI Systems

Characteristics of AI systems include:

1. Symbolic Processing

In AI applications, computers process symbols rather than numbers or letters. AI applications process strings of characters that represent real-world entities or concepts. Symbols can be arranged in structures such as lists, hierarchies, or networks. These structures show how symbols relate to each other.

2. Nonalgorithmic Processing

Computer programs outside the AI domain are programmed algorithms; that is, fully specified step-by-step procedures that define a solution to the problem. The actions of a knowledge-based AI system depend to a far greater degree on the situation where it is used.

The Field of AI

Artificial intelligence is a science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering. The goal of AI is to develop computers that can think, see, hear, walk, talk and feel. A major thrust of AI is the development of computer functions normally associated with human intelligence, such as reasoning, learning, and problem solving.

How the AI Field Evolved [Figure 11.2]

1950 Turing Test - a machine performs intelligently if an interrogator using remote terminals cannot distinguish its responses from those of a human.

Result: General problem-solving methods

1960 AI established as research field.

Result: Knowledge-based expert systems

1970 AI commercialization began

Result: Transaction processing and decision support systems using AI.

1980 Artificial neural networks

Result: Resembling the interconnected neuronal structures in the human brain

1990 Intelligent agents

Result: Software that performs assigned tasks on the users behalf

11.2 Capabilities of Expert Systems: General View

The most important applied area of AI is the field of expert systems. An expert system (ES) is a knowledge-based system that employs knowledge about its application domain and uses an inferencing (reason) procedure to solve problems that would otherwise require human competence or expertise. The power of expert systems stems primarily from the specific knowledge about a narrow domain stored in the expert system's knowledge base .

It is important to stress to students that expert systems are assistants to decision makers and not substitutes for them. Expert systems do not have human capabilities. They use a knowledge base of a particular domain and bring that knowledge to bear on the facts of the particular situation at hand. The knowledge base of an ES also contains heuristic knowledge - rules of thumb used by human experts who work in the domain.

11.3 Applications of Expert Systems

The test outlines some illustrative minicases of expert systems applications. These include areas such as high-risk credit decisions, advertising decision making, and manufacturing decisions.

Generic Categories of Expert System Applications

Table 11.1 outlines the generic areas of ES applications where ES can be applied. Application areas include classification, diagnosis, monitoring, process control, design, scheduling and planning, and generation of options.

Classification - identify an object based on stated characteristics

Diagnosis Systems - infer malfunction or disease from observable data

Monitoring - compare data from a continually observed system to prescribe behaviour

Process Control - control a physical process based on monitoring

Design - configure a system according to specifications

Scheduling & Planning - develop or modify a plan of action

Generation of Options - generate alternative solutions to a problem

11.4 How Expert Systems Work

The strength of an ES derives from its knowledge base - an organized collection of facts and heuristics about the system's domain. An ES is built in a process known as knowledge engineering , during which knowledge about the domain is acquired from human experts and other sources by knowledge engineers.

The accumulation of knowledge in knowledge bases, from which conclusions are to be drawn by the inference engine, is the hallmark of an expert system.

Knowledge Representation and the Knowledge Base

The knowledge base of an ES contains both factual and heuristic knowledge. Knowledge representation is the method used to organize the knowledge in the knowledge base. Knowledge bases must represent notions as actions to be taken under circumstances, causality, time, dependencies, goals, and other higher-level concepts.

Several methods of knowledge representation can be drawn upon. Two of these methods include:

1. Frame-based systems

- are employed for building very powerful ESs. A frame specifies the attributes of a complex object and frames for various object types have specified relationships.

2. Production rules

- are the most common method of knowledge representation used in business. Rule-based expert systems are expert systems in which the knowledge is represented by production rules.

A production rule, or simply a rule, consists of an IF part (a condition or premise) and a THEN part (an action or conclusion). IF condition THEN action (conclusion).

The explanation facility explains how the system arrived at the recommendation. Depending on the tool used to implement the expert system, the explanation may be either in a natural language or simply a listing of rule numbers.

Inference Engine [Figure 11.4]

The inference engine:

1. Combines the facts of a specific case with the knowledge contained in the knowledge base to come up with a recommendation. In a rule-based expert system, the inference engine controls the order in which production rules are applied ( A fired @ ) and resolves conflicts if more than one rule is applicable at a given time. This is what A reasoning @ amounts to in rule-based systems.

2. Directs the user interface to query the user for any information it needs for further inferencing.

The facts of the given case are entered into the working memory , which acts as a blackboard, accumulating the knowledge about the case at hand. The inference engine repeatedly applies the rules to the working memory, adding new information (obtained from the rules conclusions) to it, until a goal state is produced or confirmed.

Figure 11.5 One of several strategies can be employed by an inference engine to reach a conclusion. Inferencing engines for rule-based systems generally work by either forward or backward chaining of rules. Two strategies are:

1. Forward chaining

- is a data-driven strategy. The inferencing process moves from the facts of the case to a goal (conclusion). The strategy is thus driven by the facts available in the working memory and by the premises that can be satisfied. The inference engine attempts to match the condition (IF) part of each rule in the knowledge base with the facts currently available in the working memory. If several rules match, a conflict resolution procedure is invoked; for example, the lowest-numbered rule that adds new information to the working memory is fired. The conclusion of the firing rule is added to the working memory.

Forward-chaining systems are commonly used to solve more open-ended problems of a design or planning nature, such as, for example, establishing the configuration of a complex product.

2. Backward chaining

- the inference engine attempts to match the assumed (hypothesized) conclusion - the goal or subgoal state - with the conclusion (THEN) part of the rule. If such a rule is found, its premise becomes the new subgoal. In an ES with few possible goal states, this is a good strategy to pursue.

If a hypothesized goal state cannot be supported by the premises, the system will attempt to prove another goal state. Thus, possible conclusions are review until a goal state that can be supported by the premises is encountered.

Backward chaining is best suited for applications in which the possible conclusions are limited in number and well defined. Classification or diagnosis type systems, in which each of several possible conclusions can be checked to see if it is supported by the data, are typical applications.

Uncertainty and Fuzzy Logic

Fuzzy logic is a method of reasoning that resembles human reasoning since it allows for approximate values and inferences and incomplete or ambiguous data (fuzzy data). Fuzzy logic is a method of choice for handling uncertainty in some expert systems.

Expert systems with fuzzy-logic capabilities thus allow for more flexible and creative handling of problems. These systems are used, for example, to control manufacturing processes.

11.5 Expert System Technology [Figure 11.6]

There are several levels of ES technologies available. Two important things to keep in mind when selecting ES tools include:

1. The tool selected for the project has to match the capability and sophistication of the projected ES, in particular, the need to integrate it with other subsystems such as databases and other components of a larger information system.

2. The tool also has to match the qualifications of the project team.

Expert systems technologies include:

1. Specific expert systems

- These expert systems actually provide recommendations in a specific task domain.

2. Expert system shells

- are the most common vehicle for the development of specific ESs. A shell is an expert system without a knowledge base. A shell furnishes the ES developer with the inference engine, user interface, and the explanation and knowledge acquisition facilities.

Domain-specific shells are actually incomplete specific expert systems, which require much less effort in order to field an actual system.

3. Expert system development environments

- these systems expand the capabilities of shells in various directions. They run on engineering workstations, minicomputers, or mainframes; offer tight integration with large databases; and support the building of large expert systems.

4. High-level programming languages

Several ES development environments have been rewritten from LISP into a procedural language more commonly found in the commercial environment, such as C or C++. ESs are now rarely developed in a programming language.

11.6 Roles in Expert System Development

Three fundamental roles in building expert systems are:

1. Expert - Successful ES systems depend on the experience and application of knowledge that the people can bring to it during its development. Large systems generally require multiple experts.

2. Knowledge engineer - The knowledge engineer has a dual task. This person should be able to elicit knowledge from the expert, gradually gaining an understanding of an area of expertise. Intelligence, tact, empathy, and proficiency in specific techniques of knowledge acquisition are all required of a knowledge engineer. Knowledge-acquisition techniques include conducting interviews with varying degrees of structure, protocol analysis, observation of experts at work, and analysis of cases.

On the other hand, the knowledge engineer must also select a tool appropriate for the project and use it to represent the knowledge with the application of the knowledge acquisition facility .

3. User - A system developed by an end user with a simple shell, is built rather quickly an inexpensively. Larger systems are built in an organized development effort. A prototype-oriented iterative development strategy is commonly used. ESs lends themselves particularly well to prototyping.

11.7 Development and Maintenance of Expert Systems [Figure 11.7]

Steps in the methodology for the iterative process of ES development and maintenance include:

1. Problem Identification and Feasibility Analysis:

- the problem must be suitable for an expert system to solve it.

- must find an expert for the project

- cost-effectiveness of the system has to be established (feasibility)

2. System Design and ES Technology Identification:

- the system is being designed. The needed degree of integration with other subsystems and databases is established

- concepts that best represent the domain knowledge are worked out

- the best way to represent the knowledge and to perform inferencing should be established with sample cases

3. Development of Prototype:

- knowledge engineer works with the expert to place the initial kernel of knowledge in the knowledge base.

- knowledge needs to be expressed in the language of the specific tool chosen for the project

4. Testing and Refinement of Prototype:

- using sample cases, the prototype is tested, and deficiencies in performance are noted. End users test the prototypes of the ES.

5. Complete and Field the ES:

- the interaction of the ES with all elements of its environment, including users and other information systems, is ensured and tested.

- ES is documented and user training is conducted

6. Maintain the System:

- the system is keep current primarily by updating its knowledge base.

- interfaces with other information systems have to be maintained as well, as those systems evolve.

11-8 Expert Systems in Organizations: Benefits and Limitations

Expert systems offer both tangible and important intangible benefits to owner companies. These benefits should be weighted against the development and exploitation costs of an ES, which are high for large, organizationally important ESs.

Benefits of Expert Systems

An ES is no substitute for a knowledge worker's overall performance of the problem-solving task. But these systems can dramatically reduce the amount of work the individual must do to solve a problem, and they do leave people with the creative and innovative aspects of problem solving.

Some of the possible organizational benefits of expert systems are:

1. An Es can complete its part of the tasks much faster than a human expert.

2. The error rate of successful systems is low, sometimes much lower than the human error rate for the same task.

3. ESs make consistent recommendations

4. ESs are a convenient vehicle for bringing to the point of application difficult-to-use sources of knowledge.

5. ESs can capture the scarce expertise of a uniquely qualified expert.

6. ESs can become a vehicle for building up organizational knowledge, as opposed to the knowledge of individuals in the organization.

7. When use as training vehicles, ESs result in a faster learning curve for novices.

8. The company can operate an ES in environments hazardous for humans.

Limitations of Expert Systems

No technology offers an easy and total solution. Large systems are costly and require significant development time and computer resources. ESs also have their limitations which include:

1. Limitations of the technology

2. Problems with knowledge acquisition

3. Operational domains as the principal area of ES application

4. Maintaining human expertise in organizations

11-9 Overview of Applied Artificial Intelligence

Expert systems are only one area of AI. Other areas include:

1. Natural language processing

2. Robotics

3. Computer vision

4. Computerized speech recognition

5. Machine learning

Natural Language Processing

Being able to talk to computers in conversational human languages and have them A understand @ us in a goal of AI researchers. Natural language processing systems are becoming common. The main application for natural language systems at this time is as a user interface for expert and database systems.

AI, engineering, and physiology are the basic disciplines of robotics. This technology produces robot machines with computer intelligence and computer-controlled, human like physical capabilities, robotics applications

Computer Vision

The simulation of human senses is a principal objective of the AI field. The most advanced AI sensory system is compute vision, or visual scene recognition. The task of a vision system is to interpret the picture obtained. These systems are employed in robots or in satellite systems. Simpler vision systems are used for quality control in manufacturing.

Speech Recognition

The ultimate goal of the corresponding AI area is computerized speech recognition, or the understanding of connected speech by an unknown speaker, as opposed to systems that recognize words or short phrases spoken one at a time or systems are trained by a specific speaker before use.

Machine Learning

A system with learning capabilities - machine learning - can automatically change itself in order to perform the same tasks more efficiently and more effectively the next time.

A number of approaches to learning are being investigated. Approaches include:

1. Problem Solving Learning - accumulate A experience @ about its rules in terms of their contributions to correct advice. The rules that do not contribute or those that are found to provide doubtful contributions could be automatically discarded or assigned low certainty factors.

2. Case-Based Learning - collecting cases in a knowledge base and solving problems by seeking out a case similar to the one to be solved.

3. Inducive Learning - learning from examples. In this case, a system is able to generate its knowledge, represented as rules.

11-10 Neural Networks

Neural networks are computing systems modelled on the human brain's mesh-like network of interconnected processing elements, called neurons. Of course, neural networks are much simpler than the human brain (estimated to have more than 100 billion neuron brain cells). Like the brain, however, such networks can process many pieces of information simultaneously and can learn to recognize patterns and programs themselves to solve related problems on their own.

A neural network is an array of interconnected processing elements, each of which can accept inputs, process them, and produce a single output with the objective of imitating the operation of the human brain. Knowledge is represented in a neural network by the pattern of connections among the processing elements and by adjusting weights of these connections.

The strength of neural networks is in applications that require sophisticated pattern recognition. The greatest weakness of neural networks is that they do not furnish an explanation for the conclusions they make.

In summary, a neural network can be trained to recognize certain patterns and then apply what it learned to new cases where it can discern the patterns.

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Data Science Introduction

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Statistical Inference

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• Introduction To Markov Chains With Examples – Markov Chains With Python
• What is Fuzzy Logic in AI and What are its Applications?
• How To Implement Bayesian Networks In Python? – Bayesian Networks Explained With Examples
• All You Need To Know About Principal Component Analysis (PCA)
• Python for Data Science – How to Implement Python Libraries

Machine Learning

• What is Machine Learning? Machine Learning For Beginners
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What is Cross-Validation in Machine Learning and how to implement it?

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Supervised Learning

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• Naive Bayes Classifier: Learning Naive Bayes with Python
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Unsupervised Learning

• What is Unsupervised Learning and How does it Work?
• K-means Clustering Algorithm: Know How It Works
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Artificial Intelligence

What is knowledge representation in ai techniques you need to know.

Human beings are good at understanding, reasoning and interpreting knowledge. And using this knowledge, they are able to perform various actions in the real world. But how do machines perform the same? In this article, we will learn about Knowledge Representation in AI and how it helps the machines perform reasoning and interpretation using Artificial Intelligence in the following sequence:

What is Knowledge Representation?

Different types of knowledge.

• Cycle of Knowledge Representation
• What is the relation between Knowledge & Intelligence?
• Techniques of Knowledge Representation

Representation Requirements

• Approaches to Knowledge Representation with Example

Knowledge Representation in AI describes the representation of knowledge. Basically, it is a study of how the beliefs, intentions , and judgments of an intelligent agent can be expressed suitably for automated reasoning. One of the primary purposes of Knowledge Representation includes modeling intelligent behavior for an agent.

Knowledge Representation and Reasoning ( KR, KRR ) represents information from the real world for a computer to understand and then utilize this knowledge to solve complex real-life problems like communicating with human beings in natural language. Knowledge representation in AI is not just about storing data in a database, it allows a machine to learn from that knowledge and behave intelligently like a human being.

The different kinds of knowledge that need to be represented in AI include:

• Performance
• Meta-Knowledge
• Knowledge-base

Now that you know about Knowledge representation in AI, let’s move on and know about the different types of Knowledge.

There are 5 types of Knowledge such as:

Declarative Knowledge – It includes concepts, facts, and objects and expressed in a declarative sentence.

Structural Knowledge – It is a basic problem-solving knowledge that describes the relationship between concepts and objects.

Procedural Knowledge – This is responsible for knowing how to do something and includes rules, strategies, procedures, etc.

Meta Knowledge – Meta Knowledge defines knowledge about other types of Knowledge.

Heuristic Knowledge – This represents some expert knowledge in the field or subject.

These are the important types of Knowledge Representation in AI. Now, let’s have a look at the cycle of knowledge representation and how it works.

Cycle of Knowledge Representation in AI

Artificial Intelligent Systems usually consist of various components to display their intelligent behavior. Some of these components include:

• Knowledge Representation & Reasoning

Here is an example to show the different components of the system and how it works:

The above diagram shows the interaction of an AI system with the real world and the components involved in showing intelligence.

• The Perception component retrieves data or information from the environment. with the help of this component, you can retrieve data from the environment, find out the source of noises and check if the AI was damaged by anything. Also, it defines how to respond when any sense has been detected.
• Then, there is the Learning Component that learns from the captured data by the perception component. The goal is to build computers that can be taught instead of programming them. Learning focuses on the process of self-improvement. In order to learn new things, the system requires knowledge acquisition, inference, acquisition of heuristics, faster searches, etc.
• The main component in the cycle is Knowledge Representation and Reasoning which shows the human-like intelligence in the machines. Knowledge representation is all about understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top-down and focus on what an agent needs to know in order to behave intelligently. Also, it defines how automated reasoning procedures can make this knowledge available as needed.
• The Planning and Execution components depend on the analysis of knowledge representation and reasoning. Here, planning includes giving an initial state, finding their preconditions and effects, and a sequence of actions to achieve a state in which a particular goal holds. Now once the planning is completed, the final stage is the execution of the entire process.

So, these are the different components of the cycle of Knowledge Representation in AI. Now, let’s understand the relationship between knowledge and intelligence.

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What is the relation between knowledge & intelligence.

In the real world, knowledge plays a vital role in intelligence as well as creating artificial intelligence . It demonstrates the intelligent behavior in AI agents or systems . It is possible for an agent or system to act accurately on some input only when it has the knowledge or experience about the input.

Let’s take an example to understand the relationship:

In this example, there is one decision-maker whose actions are justified by sensing the environment and using knowledge. But, if we remove the knowledge part here, it will not be able to display any intelligent behavior.

Now that you know the relationship between knowledge and intelligence, let’s move on to the techniques of Knowledge Representation in AI.

Techniques of Knowledge Representation in AI

There are four techniques of representing knowledge such as:

Now, let’s discuss these techniques in detail.

Logical Representation 

Logical representation is a language with some definite rules which deal with propositions and has no ambiguity in representation. It represents a conclusion based on various conditions and lays down some important communication rules . Also, it consists of precisely defined syntax and semantics which supports the sound inference. Each sentence can be translated into logics using syntax and semantics.

Advantages:

• Logical representation helps to perform logical reasoning.
• This representation is the basis for the programming languages.

Disadvantages:

• Logical representations have some restrictions and are challenging to work with.
• This technique may not be very natural, and inference may not be very efficient.

Semantic Network Representation

Semantic networks work as an alternative of predicate logic for knowledge representation. In Semantic networks, you can represent your knowledge in the form of graphical networks. This network consists of nodes representing objects and arcs which describe the relationship between those objects. Also, it categorizes the object in different forms and links those objects.

This representation consist of two types of relations:

• IS-A relation (Inheritance)
• Kind-of-relation
• Semantic networks are a natural representation of knowledge.
• Also, it conveys meaning in a transparent manner.
• These networks are simple and easy to understand.
• Semantic networks take more computational time at runtime.
• Also, these are inadequate as they do not have any equivalent quantifiers.
• These networks are not intelligent and depend on the creator of the system.

Frame Representation

A frame is a record like structure that consists of a collection of attributes and values to describe an entity in the world. These are the AI data structure that divides knowledge into substructures by representing stereotypes situations. Basically, it consists of a collection of slots and slot values of any type and size. Slots have names and values which are called facets.

• It makes the programming easier by grouping the related data.
• Frame representation is easy to understand and visualize.
• It is very easy to add slots for new attributes and relations.
• Also, it is easy to include default data and search for missing values.
• In frame system inference, the mechanism cannot be easily processed.
• The inference mechanism cannot be smoothly proceeded by frame representation.
• It has a very generalized approach.

Production Rules

In production rules, agent checks for the condition and if the condition exists then production rule fires and corresponding action is carried out. The condition part of the rule determines which rule may be applied to a problem. Whereas, the action part carries out the associated problem-solving steps. This complete process is called a recognize-act cycle.

The production rules system consists of three main parts:

• The set of production rules
• Working Memory
• The recognize-act-cycle

The production rules are expressed in natural language.

The production rules are highly modular and can be easily removed or modified.

It does not exhibit any learning capabilities and does not store the result of the problem for future uses.

During the execution of the program, many rules may be active. Thus, rule-based production systems are inefficient.

So, these were the important techniques for Knowledge Representation in AI. Now, let’s have a look at the requirements for these representations.

A good knowledge representation system must have properties such as:

Representational Accuracy: It should represent all kinds of required knowledge.

Inferential Adequacy : It should be able to manipulate the representational structures to produce new knowledge corresponding to the existing structure.

Inferential Efficiency : The ability to direct the inferential knowledge mechanism into the most productive directions by storing appropriate guides.

Acquisitional efficiency : The ability to acquire new knowledge easily using automatic methods.

Now, let’s have a look at some of the approaches to Knowledge Representation in AI along with different examples.

Approaches to Knowledge Representation in AI

There are different approaches to knowledge representation such as:

1. Simple Relational Knowledge

It is the simplest way of storing facts which uses the relational method. Here, all the facts about a set of the object are set out systematically in columns. Also, this approach of knowledge representation is famous in database systems where the relationship between different entities is represented. Thus, there is little opportunity for inference.

This is an example of representing simple relational knowledge.

2. Inheritable Knowledge

In the inheritable knowledge approach, all data must be stored into a hierarchy of classes and should be arranged in a generalized form or a hierarchal manner. Also, this approach contains inheritable knowledge which shows a relation between instance and class, and it is called instance relation. In this approach, objects and values are represented in Boxed nodes.

3. Inferential Knowledge

The inferential knowledge approach represents knowledge in the form of formal logic . Thus, it can be used to derive more facts. Also, it guarantees correctness.

Statement 1 : John is a cricketer.

Statement 2 : All cricketers are athletes.

Then it can be represented as;

Cricketer(John) ∀x = Cricketer (x) ———-> Athelete (x)s

These were some of the approaches to knowledge representation in AI along with examples. With this, we have come to the end of our article. I hope you understood what is Knowledge Representation in AI and its different types.

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Got a question for us? Please mention it in the comments section of “Knowledge Representation in AI” and we will get back to you.

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Evolving knowledge representation learning with the dynamic asymmetric embedding model

• Original Paper
• Published: 05 September 2024

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• Muhib A. Khan 1 ,
• Saif Ur Rehman Khan   ORCID: orcid.org/0000-0002-0768-5239 2 ,
• Syed Zohair Quain Haider 3 ,
• Shakeeb A. Khan 3 &
• Omair Bilal 2  

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Detecting errors in Knowledge Graphs is challenging due to the scarcity of ground-truth labels and the unpredictable nature of error patterns, leading to noticeable noise affecting downstream tasks. KG’s repositories contain billions of triplets representing relationships as instances (head entity, relation, tail entity). This distinctive and symbolic architecture facilitates advanced knowledge exploitation techniques aimed at improving the accuracy of other intelligence-driven applications. Making logical rules to validate triples is a traditional approach, yet it lacks generalizability due to the variability of rules across knowledge graphs stemming from domain-specific knowledge. Recent models such as TransE, TransH, TransR, and TransD have approached the task of knowledge graph completion by treating relationships as translations from head entities to tail entities. However, these models exhibit limitations in capturing the complexity and diversity of entities and relations within knowledge graphs, particularly with regards to symmetric, one-to-many, and many-to-many relations. To address these limitations, this paper introduces a novel model called TransDAE for dynamic asymmetrical embedding in knowledge graph completion, building upon the foundation of TransDR. Unlike TransDR, which focuses less on asymmetrical relations and overlooks the imbalanced characteristics of relationships, such as one-to-many and many-to-one, TransDAE considers the distinct properties of head and tail entities in similar relations. Specifically, TransDAE incorporates two vectors for each triple relationship, weighting each entity vector by its corresponding head and tail relation vectors in the relation embedding space. This enhances the model's flexibility and improves its ability to interpret and uncover latent attributes of entities and relations. Through experimental evaluation on tasks including triplet classification and link prediction, TransDAE demonstrates superior performance compared to previous models, particularly excelling in link prediction on benchmark datasets FB15K and WN18. The performance (Hits@10) of TransDAE models in predicting both head and tail entities for various relation categories in KG was presented. Across different categories, TransDAE(U) and TransDAE(B) achieved high accuracy, with TransDAE(U) generally outperforming TransDAE(B) in most cases.

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Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

Muhib A. Khan

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Saif Ur Rehman Khan & Omair Bilal

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Khan, M.A., Khan, S.U.R., Haider, S.Z.Q. et al. Evolving knowledge representation learning with the dynamic asymmetric embedding model. Evolving Systems (2024). https://doi.org/10.1007/s12530-024-09616-2

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DOI : https://doi.org/10.1007/s12530-024-09616-2

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