Expert System Seyed Hashem Davarpanah University of Science and Culture

Reasoning in Expert Systems knowledge representation in Expert Systems knowledge representation in Expert Systems shallow and deep reasoning shallow and deep reasoning forward and backward reasoning forward and backward reasoning alternative inference methods alternative inference methods metaknowledge metaknowledge

Expert performance depends on expert knowledge! Experts and Expert Systems Human Experts achieve high performance because of extensive knowledge concerning their field Human Experts achieve high performance because of extensive knowledge concerning their field Generally developed over many years Generally developed over many years

Types of Knowledge Knowledge Representation in XPS can include: conceptual knowledge conceptual knowledge terminology, domain-specific terms terminology, domain-specific terms derivative knowledge derivative knowledge conclusions between facts conclusions between facts causal connections causal connections causal model of domain causal model of domain procedural knowledge procedural knowledge guidelines for actions guidelines for actions

Knowledge Modeling in XPS Knowledge Modeling Technique in XPS mostly rule-based systems (RBS) mostly rule-based systems (RBS) rule system models elements of knowledge formulated independently as rules rule system models elements of knowledge formulated independently as rules rule set is easy to expand rule set is easy to expand often only limited ‘deep’ knowledge, i.e. no explicit coherent causal or functional model of the domain often only limited ‘deep’ knowledge, i.e. no explicit coherent causal or functional model of the domain

Shallow and Deep Reasoning shallow reasoning shallow reasoning also called “experiential reasoning” also called “experiential reasoning” aims at describing aspects of the world heuristically aims at describing aspects of the world heuristically short inference chains short inference chains complex rules complex rules deep reasoning deep reasoning also called causal reasoning also called causal reasoning aims at building a model that behaves like the “real thing” aims at building a model that behaves like the “real thing” long inference chains long inference chains simple rules that describe cause and effect relationships simple rules that describe cause and effect relationships

General Technology of XPS Knowledge + Inference core of XPS core of XPS Most often Rule-Based Systems (RBS) Most often Rule-Based Systems (RBS) other forms: Neural Networks, Case-Based Reasoning other forms: Neural Networks, Case-Based Reasoning

General Technology of XPS Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning.mechanicenginecarmechanicenginecar

Rule-Based Expert Systems Work with a set of facts describing the current world state a set of facts describing the current world state a set of rules describing the expert knowledge a set of rules describing the expert knowledge inference mechanisms for combining facts and rules in reasoning inference mechanisms for combining facts and rules in reasoning

Inference Engine Agenda Knowledge Base (rules) Explanation Facility User Interface Knowledge Acquisition Facility Working Memory (facts)

Architecture of Rule-Based XPS 1 Knowledge-Base / Rule-Base stores expert knowledge as “condition-action-rules” (or: if-then- or premise-consequence-rules) stores expert knowledge as “condition-action-rules” (or: if-then- or premise-consequence-rules) objects or frame structures are often used to represent concepts in the domain of expertise, e.g. “club” in the golf domain. objects or frame structures are often used to represent concepts in the domain of expertise, e.g. “club” in the golf domain. Working Memory stores initial facts and generated facts derived by the inference engine stores initial facts and generated facts derived by the inference engine additional parameters like the “degree of trust” in the truth of a fact or a rule (  certainty factors) or probabilistic measurements can be added additional parameters like the “degree of trust” in the truth of a fact or a rule (  certainty factors) or probabilistic measurements can be added

Architecture of Rule-Based XPS 2 Inference Engine matches condition-part of rules against facts stored in Working Memory (pattern matching); matches condition-part of rules against facts stored in Working Memory (pattern matching); rules with satisfied condition are active rules and are placed on the agenda; rules with satisfied condition are active rules and are placed on the agenda; among the active rules on the agenda, one is selected (see conflict resolution, priorities of rules) as next rule for among the active rules on the agenda, one is selected (see conflict resolution, priorities of rules) as next rule for execution (“firing”) – consequence of rule can add new facts to Working Memory, modify facts, retract facts, and more execution (“firing”) – consequence of rule can add new facts to Working Memory, modify facts, retract facts, and more

Architecture of Rule-Based XPS 3 Inference Engine + additional components might be necessary for other functions, like calculation of certainty values, calculation of certainty values, determination of priorities of rules determination of priorities of rules and conflict resolution mechanisms, and conflict resolution mechanisms, a truth maintenance system (TMS) if reasoning with defaults and beliefs is requested a truth maintenance system (TMS) if reasoning with defaults and beliefs is requested

Architecture of Rule-Based XPS 3 A truth maintenance system, or TMS, is a knowledge representation method for representing both beliefs and their dependencies. A truth maintenance system, or TMS, is a knowledge representation method for representing both beliefs and their dependencies.knowledge representationknowledge representation The name truth maintenance is due to the ability of these systems to restore consistency. The name truth maintenance is due to the ability of these systems to restore consistency. It is also termed as a belief revision system, a truth maintenance system maintains consistency between old believed knowledge and current believed knowledge in the knowledge base (KB) through revision. It is also termed as a belief revision system, a truth maintenance system maintains consistency between old believed knowledge and current believed knowledge in the knowledge base (KB) through revision. If the current believed statements contradict the knowledge in KB, then the KB is updated with the new knowledge. If the current believed statements contradict the knowledge in KB, then the KB is updated with the new knowledge.

Rule-Based Systems - Example ‘Grades’ - Rules to determine ‘grade’ 1. study  good_grade 2. not_study  bad_grade 3. sun_shines  go_out 4. go_out  not_study 5. stay_home  study 6. awful_weather  stay_home

Example ‘Grades’ 1. study  good_grade 2. not_study  bad_grade 3. sun_shines  go_out 4. go_out  not_study 5. stay_home  study 6. awful_weather  stay_home Q1: If the weather is awful, do you get a good or bad grade? Q2: When do you get a good grade? Rule-Base to determine the ‘grade’:

Forward and Backward Reasoning forward reasoning  Facts are given. What is the conclusion? A set of known facts is given (in WM); apply rules to derive new facts as conclusions (forward chaining of rules) until you come up with a requested final goal fact. backward reasoning  Hypothesis (goal) is given. Is it supported by facts? A hypothesis (goal fact) is given; try to derive it based on a set of given initial facts using sub-goals (backward chaining of rules) until goal is grounded in initial facts.

1.study  good_grade 2.not_study  bad_grade 3.sun_shines  go_out 4.go_out  not_study 5.stay_home  study 6.awful_weather  stay_home Example ‘Grades’ forward reasoningrule chain given fact: awful_weather 6,5,1 backward reasoning hypothesis/goal: good_grade 1,5,6

good grade Example ‘Grades’ – Reasoning Tree bad grade not studystudy go outstay home sun shinesawful weather

Example – Grades Working MemoryAgenda awful weatherRule 6 Select and apply Rule 6 awful weather stay home Rule 5 Select and apply Rule 5

Example – Grades Working MemoryAgenda Select and apply Rule 1 awful weather stay home study Rule 1 awful weather stay home study good grade empty DONE!

Forward vs. Backward Chaining Forward ChainingBackward Chaining diagnosisconstruction data-drivengoal-driven (hypothesis) bottom-up reasoningtop-down reasoning find possible conclusions supported by given facts find facts that support a given hypothesis antecedents (LHS) control evaluation consequents (RHS) control evaluation

Alternative Reasoning Methods Theorem Proving Theorem Proving emphasis on mathematical proofs and correctness, not so much on performance and ease of use emphasis on mathematical proofs and correctness, not so much on performance and ease of use Probabilistic Reasoning Probabilistic Reasoning integrates probabilities into the reasoning process integrates probabilities into the reasoning process Certainty Factors Certainty Factors Express subjective assessment of truth of fact or rule Express subjective assessment of truth of fact or rule Fuzzy Reasoning Fuzzy Reasoning allows the use of vaguely defined predicates and rules allows the use of vaguely defined predicates and rules

Metaknowledge deals with “knowledge about knowledge” deals with “knowledge about knowledge” e.g. reasoning about properties of knowledge representation schemes, or inference mechanisms e.g. reasoning about properties of knowledge representation schemes, or inference mechanisms usually relies on higher order logic usually relies on higher order logic in (first order) predicate logic, quantifiers are applied to variables in (first order) predicate logic, quantifiers are applied to variables second-order predicate logic allows the use of quantifiers for function and predicate symbols second-order predicate logic allows the use of quantifiers for function and predicate symbols may result in substantial performance problems may result in substantial performance problems CLIPS uses meta-knowledge to define itself, i.e. CLIPS constructs, classes, etc. - in a bootstrapping form CLIPS uses meta-knowledge to define itself, i.e. CLIPS constructs, classes, etc. - in a bootstrapping form