Rule Based System

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Rule-based expert systems

Based on Ch 2 (and parts of Ch 9) in Negnevitsky

The world knowledge problem

General purpose intelligence requires vast amounts of knowledge about the world.

What do you need to know in order to understand the following sentences?

“Sorry, but the bank was closed. Would you get this round?”

“I dropped the carton. I guess we‟re having an omelet!”

„General purpose‟ intelligence

Ability to:

Learn from new situations

Apply solutions from old problems to new ones

Communicate about any topic, even if not familiar

Apply abstract reasoning to a range of specific problems

Understand how the world works in general

Domain-specific intelligence

Restricted to a particular domain (e.g. migraines, oil prospecting, etc.)

Knowledge is deep, but not wide (e.g. migraines only, no other medical problems)

Knowledge is tied directly to problem solving (e.g. diagnosis of migraines, not their social repercussions)

à Avoids the world knowledge problem, and is much more feasible for implementation.

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 not necessarily the most intuitive way to represent domain knowledge.

Quantitative vs. qualitative

Humans tend to represent their knowledge qualitatively, e.g.:

John is tall, but Jim is taller. As opposed to quantitative:

John‟s height is 6‟5”. Jim‟s height is 6‟8”.

So, probably a good idea to represent expert knowledge that way.

1

Heuristics vs. rigid rules

• Heuristic: A rule of thumb; a guideline.

Most domain knowledge is in the form of heuristics, rather than rigid rules, e.g.:

“Frequent coughing might mean bronchitis”

Rather than:

> 10 coughs per hour = bronchitis

Explanation

A human domain expert is usually able to explain the reasoning, e.g.:

Why do you think I have a migraine?

Well, you have frequent, intense pain in the temple area, associated with nausea. Also, you aren’t taking any medications that are likely to produce these symptoms.

Production rules

IF THEN For example:

IF fish is floating THEN fish is dead Alternate syntax:

Fish is floating à fish is dead

Note: looks similar to logical “implies”, but less formal – can‟t necessarily apply logical transformations.

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 patient‟s symptoms)

Rule-based expert systems

Designed to:

Capture a domain expert‟s knowledge in propositional, qualitative form

Separate the knowledge from the reasoning (inference) and from the specific facts

Allow heuristic reasoning and explanation

Multiple antecedents

Multiple antecedents may be connected by conjunction (AND) and/or disjunction (OR).

IF animal is horse-shaped

AND animal has stripes

THEN animal is zebra

IF animal is hippo

OR animal is lion

THEN animal is dangerous

2

Multiple consequents

Multiple consequents are possible, and are connected by conjunctions, e.g.

IF tsunami alarm is sounding

AND date is not first Monday in month THEN

Condition is dangerous AND

Advice is „move away from the ocean‟

Structure of antecedents and consequents

Object is related to value by operator. For example:

IF „taxable income‟ > 13914

THEN „medicare levy‟ = „taxable income‟ * 1.5 /

100 [Negnevitsky p 27]

Can get arbitrarily complex, but best to keep it as close...
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