1. 1 Lecture 34 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

2. 2 Lecture 34 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

3. 3 Lecture 34 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

4. 4 Lecture 34 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

5. 5 Lecture 34 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)

6. 6 Lecture 34 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)]

7. 7 Lecture 34 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)]

8. 8 Lecture 34 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.

9. 9 Lecture 34 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

10. 10 Lecture 34 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.

11. 11 Lecture 34 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.