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Introduction to Knowledge Representation & Expert Systems

Introduction to Knowledge Representation & Expert Systems. Overview Lecture Objective Introduction to Knowledge Representation Knowledge Representation Languages Introduction to Expert Systems Preview: Brief Introduction the other AI Areas. Lecture Objective.

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Introduction to Knowledge Representation & Expert Systems

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  1. Introduction to Knowledge Representation & Expert Systems Overview • Lecture Objective • Introduction to Knowledge Representation • Knowledge Representation Languages • Introduction to Expert Systems • Preview: Brief Introduction the other AI Areas Lecture 34

  2. Lecture Objective • Present an overview of Knowledge Representation and what is entails • Outline the different knowledge representation languages • Motivate, discuss, exemplify pointing out the limitations of expert systems Lecture 34

  3. Introduction to Knowledge Representation • AI is based on the computer’s ability to learn and to improve performance, based on past errors. This exists to some extent in all areas of AI. • Two key components are needed to make machine intelligent, • 1. Knowledge Representation: How to store knowledge and relationships... • knowledge base - set of facts and rules • Inference: How to make new facts from already available knowledge. • inference engine - computer that accesses, selects and interprets rules to make new facts • E.g., Fact: Ali is shorter than Ahmad • Rule: If X is shorter than Y, then Y is taller than X Lecture 34

  4. Knowledge Representation • To represent knowledge we need a representation language. • Each representation language has syntactic and semantic conventions that makes it possible to describe things. • The syntax of a representation specifies the symbols that maybe used and the way this symbols may be arranged or used. • The semantics of a representation specifies how meaning is embodied in the symbols. • Examples of Knowledge Representation Languages • Prepositional Logic • Predicate Logic • IF THEN Rules (Production Rules) • Semantic Nets Lecture 34

  5. Knowledge Representation using Logic Predicate Logic Syntax • Constant symbols ( Khalid, Ali, car, 3, ..) • Function symbols (mapping to a constant) Father(khalid)=Ali • Predicate symbols (mapping to truth values) greater(5,3) • Variable symbols. E.g., x, y • Connectives. not (~), and (^), or (v), implies (=>), if and only if (<=>) • Quantifiers: Universal (A) and Existential (E) Lecture 34

  6. Knowledge Representation using Logic (cont’d) • Not all cars are BMWs. ~(forall x)[Car(x) ==> BMW(x)] (exists x)[Car(x) ^ ~BMW(x)] • Some numbers are not real. ~(forall x)[Number(x) ==> Real(x)] (exists x)[Number(x) ^ ~Real(x)] • Every number is either negative or has a square root. (forall x)[Number(x) ==> (negative(x) v has-sqrt(x))] ~(exists x)[Number(x) ^ ~negative(x) ^ ~has-sqrt(x)] Lecture 34

  7. Knowledge Representation using Logic (cont’d) Example Statements Khalid is a Student Khalid is Muslim Every Muslim Prays Every Student has ID Any person that doesn't pray is not a Muslim Representation using Predicate Logic Student(Khalid) Muslim(Khalid) (for all x) [muslim(x) -> prays(x)] (for all x) [~pray(x)->~Muslim(x)] Lecture 34

  8. Knowledge Representation using Symantec Networks • Semantic networks (Entities and Relationships) • Entities are the basic objects of the system and relationships indicates how these objects are related. • Is-a relationship means inheritance and can help us to infer the properties of an entity from another entity. This inference is done through searching. Lecture 34

  9. Expert System (ES) • An ES is computer application that performs a task that would otherwise be performed by a human expert. Knowledge from human experts in a specific field is encoded to be accessible. It is essentially a specialized DBMS. • It consists of a knowledge base of facts and inference engine, combining rules of fact and rules with which to discover new facts. • Expert System examples… • 1. Medical diagnosis (MYCIN) • 2. Geological data interpretation • 3. Tax preparation • 4. Configuring/troubleshooting PCs • A typical ES architecture consists of: • knowledge base module • working memory module (for the current data) • inference engine • forward chaining (inductive, data driven) • backward chaining (deductive, goal driven) • user interface (possibly a NLI, menu, windows, etc) • explanation module Lecture 34

  10. Expert System Design • To design an expert system, one needs a knowledge engineer, an individual who studies how human experts make decisions and translates the rules into terms that a computer can understand. Lecture 34

  11. Expert Systems: Strengths & Limitations • Problems... • Not all cases are the same; exceptions • Hard to explain “how” an expert works • Have to quantify qualitative data • Knowledge could be difficult to extract and represent • Regardless of these problems, Expert Systems have the following advantages: • Can produce results much faster than a human expert. • Error rate in a successful ES is low and can be lower than in the case of a human expert. • ES can make consistent recommendations. • An ES can operate in an environment that is hazardous to human. • ES can capture the scarce expertise of a uniquely qualified human expert. Lecture 34

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