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241-320 Design Architecture and Engineeringfor Intelligent System

Suntorn Witosurapot

Contact Address:Phone: 074 287369 or

Email: [email protected]

December 2010

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Lecture 12:

Knowledge Representation

and Reasoning Part 3

(Rule based Systems)

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241-320 Design Architecture &Engineering for Intelligent System Knowledge Representation andReasoning - part 3 (Rule-based) 3

Preview:

KR can be classified into 3 categories :

1. Logic-based representation () Propositional Logic

2. Object-based representation () Semantic Networks, Frames

3. Rule-based representation ()

So far, we have learned how to represent knowledgein a relatively declarative, static way (as a bunch ofthings that are true)via PL, Semantic Nets or Frames.

Here, we will learn how to represent knowledge interms of a bunch of rules that tell you what you shoulddo or what you could conclude in different situations.

This lecture

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Preview: (cont.)

These rules can be called Production Rules

Expert systems that use a knowledge base consistingof production rules are called rule-based systemsthat

1) can express heuristic knowledge

Heuristics vs. rigid rules

Heuristic: A rule of thumb; a guideline.

Most domain knowledge is in the form of heuristics,rather than rigid rules

Ex: ,rather than: > 10 =

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Preview: (cont.)

2) Can capture a domain experts knowledge inpropositional, qualitative form

Procedural vs. propositional

Most programming languages are procedural:first do this, then this, then this

Most human expert knowledge is propositional:If A is true, then B is true

So, conventional programming languages are notnecessarily the most intuitive way to representdomain knowledge.

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Preview: (cont.)

3) Can capture a domain experts knowledge inpropositional, qualitative form

Quantitative vs. qualitative

Humans tend to represent their knowledge qualitatively,e.g. John is tall, but Jim is taller.

As opposed to quantitative:

Johns height is 65. Jims height is 68.

So, probably a good idea to represent expert knowledgethat way.

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Preview: (cont.)

4) Can separate the knowledge from the reasoning(inference) and from the specific facts

5) easy to understand (i.e. `knowledge' is visible).

Reasoning + knowledge + facts

Human expertise typically breaks down into: Ability to reason Knowledge about the domain (e.g. migraines)

Facts about the particular situation (e.g. this patientssymptoms)

=> Able to Explan the reasoning(e.g.Why do you think I have amigraine? Well, because you have a frequent, intense pain in ..)

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Outline

Rule-Based Systems

Production Rules vs. Logic

Advantages & disadvantages

Visualising the rule base

Reasoning with a rule base

Forward chaining

Backward chaining

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Rule-Based Systems

In Logic (and semantic nets) we represent knowledgein a declarative, static way - as some facts and rulesthat are true.

Rules in Logic say what is TRUE when someconditions are given.

Rule-based systems are based on rules that saywhat to do, given various conditions.

IF THEN

A special engine will take control when rules areinvoked.

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Production Rules vs Logic

In simple examples, rules are very similar to the relationsyntax ( ) in logic (meaning imply), but less formal

IF THEN

E.g. IF fish is floating THEN fish is dead

Alternate syntax: Fish is floating fish is dead

Less concern about precise semantics & sound inference

More about programmed operations based onthe knowledge

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Production Rules vs Logic (cont.)

The antecedent part can be multiple in rules

Rules: IF THEN

A rule that contains two clauses in the antecedentconnected by logical OR can re-written as two rules

IF weather is hot OR temperature is high THENice-cream sales are high

could be re-written asIF weather is hot THEN ice-cream sales are high

IF temperature is high THEN ice-cream sales are high

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Check your understanding

Q: Can you explain how the if then pattern in theproduction rules and in the conditional statementsfound in conventional programming languages are

different?A: Rules in programming language tell you what is TRUE

when some conditions are given, but

Rules in the production rules tell you what TO DO,

given various conditions, since they are just simplyrepresent the knowledgebased on heuristics orexperiential reasoning.

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Simple Example: Small KBS for weather prediction

R1 IF the ambient temperature is above 35 C

THEN the weather is hot

R2 IF the relative humidity is greater than 65%

THEN the atmosphere is humidR3 IF the weather is hot AND the atmosphere is humid

THEN thunderstorms are likely to happen

An alternative is to write one rule (R1 & R2 are placed

directly in the premise of R3). However we may wish to use the statements, the weather is

hot and the atmosphere is humid, for other purposes in theknowledge base.

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Advantages

easy to understand the knowledge `content'.

explanation for the reasoning is easily shown i.e. a list ofwhich rules fired (and in which order) during reasoning.

maintenance (or modification) is easy, provided the rulebase structured well.

uncertainty can incorporated into the knowledge base.

Disadvantages (or limitations) rule bases can be very large (thousands of rules)

rules may not reflect the actual decision making

the only structure in the KB is through the rule chaining

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Outline

Rule-Based Systems





Forward chaining

Backward chaining

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An and/or graphvisualises the rule base as a treestructure

The nodesare facts [as expressed by the rule clauses]and the lines (or arcs) represent condition-actionpairs [i.e. where one node appears in the antecedent (LHS)and the other in the consequent (RHS) of a rule].

A logical AND between any two or more ruleclauses is shown byjoining the arcs. If there is no

join then a logical OR is assumed.

To simplify the structure of the graph, the nodes willbe represented abstractly [using letters etc].

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Visualising the rule base: Example 1

The and/or graph for the following rule base(1) IF A and B and C THEN E

(2) IF D THEN F

(3) IF E and F and G THEN H

goal

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Visualising the rule base: Example 2

(1) IF A and B and C THEN D

(2) IF A1 and A2 and A3 THEN A

(3) IF A11 and A12 THEN A1

(4) IF A21 and A22 THEN A2(5) IF C1 and C2 and C3 THEN C

(6) IF B1 and B2 THEN B

(7) IF C21 and C22 THEN C2

Goal D

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Outline

Rule-Based Systems





Forward chaining

Backward chaining

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How are the rules processed? In general, the pattern-matching cycles is performed

For each cycle ....

the left-hand side(s) of the rules are compared with

the current facts. when all conditions of a rule is met, (called it is

'fired) and the value of the conclusion (action part)is added to working memory.

on the next cycle, further rules may fire and so theprocess goes on.

The issue becomes What strategies and controlsshould be in place to guide rule selection?

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Reasoning with a rule base (cont.)

Two main control strategies:

Forward chaining () Data Driven Search

starts with the facts, and sees what rules apply(and hence what should be done) given the facts.

Backward chaining ()

Goal Driven Search starts with something to find out, and looks for

rules that will help in answering it given currentfact (or requiring new inputs, eg. Diagnostic tests).

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Forward chaining: Example visualization

An example

(1) IF A THEN C

(2) IF A and B THEN D

(3) IF D and B THEN G

(4) IF D and C THEN F(5) IF C THEN E

(6) IF E and F THEN H

(7) IF F and G and K THEN J

(8) IF F and G THEN I

Note:

H, I and J are root nodes

A, B and K are `inputs'

A

D

E

H

I

J

F

GB

K

C

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Forward chaining:Advantages & DisAdvantage

Good for answering What is the situation?kind of questions (e.g. What kind of animal is this?)

Fires a lot of rules, and generates a lot of facts that

might be irrelevant to the problem

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Backward Chaining (goal driven search)

Same rules/facts may be processed differently, usingbackward chainingengine.

Start with possible hypothesis (e.g. The patient has

type I diabetes) and tries to prove it Find rules that consequents that match the goal.

If antecedents match the facts, stop.

If not, make the antecedents the new subgoals,

and repeat.

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Backward chaining: Example visualization

Using example 2

(1) IF A and B and C THEN D

(2) IF A1 and A2 and A3 THEN A



(5) IF C1 and C2 and C3 THEN C

(6) IF B1 and B2 THEN B(7) IF C21 and C22 THEN C2

The graph clearly shows that fact D isroot of the tree. Hence, the objective isto determine this fact.

Note also that the leaf nodes (A11, A12,A21, A22, A3, B1, B2, C1, C21, C22, C3)represent `facts' that cannot be inferredfrom the rule base but instead have to be'asked for' (in other words they are inputs)

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Applications

Backward chaining systems have been fairly widelyused in E.g., medical expert systems, where

start with set of hypotheses on possible diseases

- try to prove each one,

asking additional diagnostic questions when fact isunknown.

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Backward chaining:Advantages & DisAdvantage

Efficient way to prove or disprove a particularhypothesis.

Sometimesmore efficient with a small set of

hypotheses Less efficient than forward chaining if large number of

hypotheses

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Forward vs. backward chaining

Forward chaining best for analysis andinterpretation (e.g. Determination ofmolecular structure of soil sample).

Backward chaining best for diagnosis (e.g.Diagnoses of infectious blood diseases).

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Forward + backward chaining

Most real expert systems use both

Primary inference is backward chaining, and switchesto forward chaining when new data is added

This aims to minimizes pointless queries to user(backward chaining), but exploits any facts that areacquired

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241-320 Design Architecture &Engineering for Intelligent System Knowledge Representation andReasoning part 3 (Rule based) 30

Reading

6 (Resoning andInference)

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