comp 4200: expert systems

© C. Kemke

1Reasoning - Introduction

COMP 4200: Expert SystemsCOMP 4200:

Expert Systems

Dr. Christel Kemke

Department of Computer Science

University of Manitoba

© C. Kemke

2Reasoning - Introduction

Reasoning in Expert SystemsReasoning in Expert Systems

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

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3Reasoning - Introduction

Expert performance depends on expert knowledge!

Experts and Expert SystemsExperts and Expert Systems

Human Experts achieve high performance because of extensive knowledge concerning their field

Generally developed over many years

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4Reasoning - Introduction

Types of KnowledgeTypes of Knowledge

Knowledge Representation in XPS can include: conceptual knowledge

terminology, domain-specific terms derivative knowledge

conclusions between facts causal connections

causal model of domain procedural knowledge

guidelines for actions

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5Reasoning - Introduction

Knowledge Modeling in XPSKnowledge Modeling in XPS

Knowledge Modeling Technique in XPS mostly rule-based systems (RBS) rule system models elements of knowledge

formulated independently as rules rule set is easy to expand often only limited ‘deep’ knowledge, i.e. no

explicit coherent causal or functional model of the domain

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6Reasoning - Introduction

Shallow and Deep ReasoningShallow and Deep Reasoning

shallow reasoning also called “experiential reasoning” aims at describing aspects of the world heuristically short inference chains complex rules

deep reasoning also called causal reasoning aims at building a model that behaves like the “real thing” long inference chains simple rules that describe cause and effect relationships

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7Reasoning - Introduction

Dilbert on Reasoning 1Dilbert on Reasoning 1

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8Reasoning - Introduction

Dilbert on Reasoning 2Dilbert on Reasoning 2

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Dilbert on Reasoning 3Dilbert on Reasoning 3

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General Technology of XPSGeneral Technology of XPS

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

Reasoning

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11Reasoning - Introduction

Rule-Based Expert Systems Rule-Based Expert Systems

Work with a set of facts describing the current world

state a set of rules describing the expert

knowledge inference mechanisms for combining facts

and rules in reasoning

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12Reasoning - Introduction

Inference Engine

AgendaKnowledge Base

(rules)

ExplanationFacility

User Interface

KnowledgeAcquisition

Facility

Working Memory (facts)

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Architecture of Rule-Based XPS 1

Architecture of Rule-Based XPS 1

Knowledge-Base / Rule-Base 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.

Working Memory 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

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14Reasoning - Introduction

Architecture of Rule-Based XPS 2

Architecture of Rule-Based XPS 2

Inference Engine 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; 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

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15Reasoning - Introduction

Architecture of Rule-Based XPS 3

Architecture of Rule-Based XPS 3

Inference Engine + additional components

might be necessary for other functions, like calculation of certainty values, determination of priorities of rules and conflict resolution mechanisms, a truth maintenance system (TMS) if reasoning with

defaults and beliefs is requested

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16Reasoning - Introduction

Rule-Based Systems- Example ‘Grades’ -

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

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17Reasoning - Introduction

Example ‘Grades’ 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’:

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18Reasoning - Introduction

Forward and Backward Reasoning

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.

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19Reasoning - Introduction

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’ Example ‘Grades’

forward reasoning rule chaingiven fact: awful_weather 6,5,1

backward reasoninghypothesis/goal: good_grade 1,5,6

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good grade

Example ‘Grades’ – Reasoning Tree

Example ‘Grades’ – Reasoning Tree

bad grade

not studystudy

go outstay home

sun shinesawful weather

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Example – GradesExample – Grades

Working Memory Agenda

awful weather Rule 6

Select and apply Rule 6

awful weatherstay home

Rule 5

Select and apply Rule 5

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Example – GradesExample – Grades

Working Memory Agenda

Select and apply Rule 1

awful weatherstay homestudy

Rule 1

awful weatherstay homestudygood grade

empty

DONE!

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23Reasoning - Introduction

forward reasoning: Shield AND Pistol Policebackward reasoning: Police Badge AND gun

Police

Badge Gun

Shield PistolRevolver

AND

OR

Bad Boy

Example ‘Police’ – Reasoning TreeExample ‘Police’ – Reasoning Tree

Q: What if only ‘Gun’ is known?

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24Reasoning - Introduction

Police

Badge Gun

Shield PistolRevolver

AND

OR

Bad Boy

Example ‘Police’ – Reasoning Tree

Example ‘Police’ – Reasoning Tree

Q: What if only ‘Pistol’ is known as ground fact?

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25Reasoning - Introduction

Police

Badge Gun

Shield PistolRevolver

AND

OR

Bad Boy

Example ‘Police’ – Reasoning Tree

Example ‘Police’ – Reasoning Tree

Task: Write down the Rule-Base for this example!

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26Reasoning - Introduction

Forward vs. Backward ChainingForward vs. Backward Chaining

Forward Chaining Backward Chainingdiagnosis construction

data-driven goal-driven (hypothesis)

bottom-up reasoning top-down reasoning

find possible conclusions supported by given facts

find facts that support a given hypothesis

antecedents (LHS) control evaluation

consequents (RHS) control evaluation

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27Reasoning - Introduction

Alternative Reasoning MethodsAlternative Reasoning Methods

Theorem Proving emphasis on mathematical proofs and correctness,

not so much on performance and ease of use

Probabilistic Reasoning integrates probabilities into the reasoning process

Certainty Factors Express subjective assessment of truth of fact or rule

Fuzzy Reasoning allows the use of vaguely defined predicates and rules

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28Reasoning - Introduction

MetaknowledgeMetaknowledge

deals with “knowledge about knowledge” e.g. reasoning about properties of knowledge

representation schemes, or inference mechanisms usually relies on higher order logic

in (first order) predicate logic, quantifiers are applied to variables second-order predicate logic allows the use of quantifiers for

function and predicate symbols may result in substantial performance problems

CLIPS uses meta-knowledge to define itself, i.e. CLIPS constructs, classes, etc. - in a bootstrapping form