Expert Systems: Architecture, Knowledge Representation, and Applications, Study notes of Artificial Intelligence

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Expert System

Lecture Module-

Expert Systems (ES) ● (^) Expert systems are knowledge based programs which provide expert quality solutions to the problems in specific domain of applications. ● (^) The core components of expert system are − (^) knowledge base and − (^) navigational capability (inference engine) ● (^) Generally its knowledge is extracted from human experts in the domain of application by knowledge Engineer. − (^) Often based on useful thumb rules and experience rather than absolute certainties. ● (^) A process of gathering knowledge from domain expert and codifying it according to the formalism is called knowledge engineering.

Cont… ● (^) Formalization Phase : − (^) It involves expressing the key concepts and relations in some framework supported by ES building tools. − (^) Formalized knowledge consists of data structures, inference rules, control strategies and languages for implementation. ● (^) Implementation Phase : − (^) During this phase, formalized knowledge is converted to working computer program initially called prototype of the whole system. ● (^) Testing Phase: − (^) It involves evaluating the performance and utility of prototype systems and revising it if need be. Domain expert evaluates the prototype system and his feedback help knowledge engineer to revise it.

Expert System Architecture

Inference Engine  (^) It consists of inference mechanism and control strategy.  (^) Inference means search through knowledge base and derive new knowledge.  (^) It involve formal reasoning involving matching and unification similar to the one performed by human expert to solve problems in a specific area of knowledge.  (^) Inference operates by using modus ponen rule.  (^) Control strategy determines the order in which rules are applied.  (^) There are mainly two types of control mechanism viz., forward chaining and backward chaining.

Knowledge Acquisition ● (^) Knowledge acquisition module allows system to acquire knowledge about the problem domain. ● (^) Sources of Knowledge for ES − (^) text books, reports, case studies, − (^) empirical data and − (^) domain expert experience. ● (^) Updation of Knowledge can be done using knowledge acquisition module of the system. − (^) insertion, − (^) deletion and − (^) updation of existing knowledge

Explanation module ● (^) Most expert systems have explanation facilities that allow the user to ask the system why it asked some question, and how it reached to conclusion. ● (^) It contains 'How' and 'Why' modules attached to it. − (^) The sub-module ‘How’ tells the user about the process through which system has reached to a particular solution − (^) ‘Why' sub-module tells that why is that particular solution offered. ● (^) It explains user about the reasoning behind any particular problem solution. ● (^) Questions are answered by referring to the system goals, the rules being used, and any existing problem data.

Explanation Modules Responses

Special interfaces ● (^) It may be used for specialized activities such as handling uncertainty in knowledge. ● (^) This is a major area of expert systems research that involves methods for reasoning with uncertain data and uncertain knowledge. ● (^) Knowledge is generally incomplete and uncertain. ● (^) To deal with uncertain knowledge, a rule may have associated with it a confidence factor or a weight. ● (^) The set of methods for using uncertain knowledge in combination with uncertain data in the reasoning process is called reasoning with uncertainty.

Rule Based Expert Systems ● (^) A rule based expert system is one in which knowledge base is in the form of rules and facts. − (^) Knowledge in the form of rules and facts is most popular way in designing expert systems. ● (^) It is also called production system. ● (^) Example: Suppose doctor gives a rule for measles as follows: "If symptoms are fever, cough, running_nose, rash and conjunctivitis then patient probably has measles". ● (^) Prolog is most suitable for implementing such systems. hypothesis(measles) :- symptom(fever), symptom(cough), symptom(running_nose), symptom(conjunctivitis), symptom(rash).

Medical Consultation System consultation :- writeln(‘Welcome to MC System’), writeln(‘Input your name), readln(Name), hypothesis(Dis), !, writeln(Name, ‘probably has’, Dis), clear_consult_facts. consultation :- writeln(‘Sorry, not able to diagnose’), clear_consult_facts.

Cont… hypothesis(flu) :- symptom(fever), symptom(headache), symptom(body_ache), symptom(sore_throat), symptom(cough), symptom(chills), symptom(running_nose), symptom(conjunctivitis). hypothesis(cold) :- ----------. hypothesis(measles):- ----------. hypothesis(mumps):- ----------. hypothesis(cough) :- ----------. hypothesis(chicken_pox):- ----------.

Cont… positive_ symp(, X) :- positive(X), !. positive symp(Q, X) :- not(negative(X)), query(Q, X, R), R = ‘y’. query(Q, X, R) :- writeln(Q), readln(R), store(X, R). store(X, ‘y’) :- asserta(positive(X)). store(X, ‘n’) :- asserta(negative(X)). clear_consult_facts :- retractall(positive()). clear_consult_facts :- retractall(negative()).

Forward Chaining ● (^) Prolog uses backward chaining as a control strategy, but forward chaining can be implemented in Prolog. ● (^) In forward chaining, the facts from static and dynamic knowledge bases are taken and are used to test the rules through the process of unification. ● (^) The rule is said to be fired and the conclusion (head of the rule) is added to the dynamic database when a rule succeeds. ● (^) Prolog rules are coded as facts with two arguments, first argument be left side of rule and second is the list of sub goals in the right side of the rule.