Rule based representation M Sasikumar 1 Overview Background - - PowerPoint PPT Presentation

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Rule based representation

M Sasikumar

Sasikumar M, IIT Bombay

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Overview

• Background
• What/Why RBR
• Forms of rules
• Uncertainty

– Certainty factor

• Issues

Sasikumar M, IIT Bombay

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Background

• One of the most well known representation

mechanisms.

– Model for how human brain works?!

• Easy to externalise and interpret.
• Good inferencing mechanisms.
• Theoretical basis available to some extent.

Sasikumar M, IIT Bombay

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What is a rule

• If arrival time > 9.30 and arrival time < 10.00

then insert casual_leave(0.5)

• If symptoms include headache and

headache_duration > 7 days and fever not present then investigate disease_x

• If value_1 > value_2 and ans = value_2-

value_1 then misconception = order_problem

Sasikumar M, IIT Bombay

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Form of a rule

• If <condition> then <conclusion>
• If <condition> is known to be true then

<conclusion> can be claimed to hold

• Many variations possible on this format.
• Very popular with expert systems literature.

– Can be used for capturing domain knowledge and

student/tutor models in ATS.

Sasikumar M, IIT Bombay

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Why rules?

• They can be read like English (usually!).
• And hence with minor rewrite makes sense to

the user.

• (Usually) rules are independent of each other.

– Can insert and delete without worrying about other

rules (!)

– No control flow embedded.

• Can be built from outside

Sasikumar M, IIT Bombay

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Using rules

• Choose a rule whose conditions match with

the current problem state, and perform the action.

– Forward chaining

• Set a goal. Choose a rule whose conclusion

matches with our goal, and check if the conditions can be made true.

– Each condition becomes a sub-goal – Backward chaining

• Can mix them in a system.

Sasikumar M, IIT Bombay

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Using rules

Rule Interpreter Working Memory Rule Base

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Using rules...

• What if there are multiple rules that match?
• Some sense of priority to be used

– Rule order in the system – Manually assigned priority numbers – Specificity

• If A and B then C1
• If A then C2

– Semantic analysis of the candidates

• Who uses what parameters, dependencies, etc

Sasikumar M, IIT Bombay

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Forms of rules

• Around the basic theme, many variations

proposed and used.

• Variation on

– Structure of a condition – Combining conditions in to a rule – Structure of conclusion – Multiple conclusions – Annotations

Sasikumar M, IIT Bombay

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Condition

• Matches with information collected in the working

memory.

• Model-1: atomic condition

– heavy_rain, take_umbrella, top_higher_than_bottom

• Model-2: attribute operator value

– Age > 40, fever is true, current_column is “col1” – What kind of operators, and their meaning?

• Model-3: value can also be an attribute

– Top > bottom

• Model-4: add context also

– P1.age > 40, thisvisit.fever is true, column1.top >

column1.bottom

Sasikumar M, IIT Bombay

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…

• More logic based formations

– Richer operations in the form of predicates – severe(P,pain_intensity) and

high(bloodpressure,P)

– parent(P,P1), cardiac(P1) →

• Richer operations (e.g. OPS5)

– (student ^age >40 ^name “%iar”) – ((student ^name n1 ^mark m),

• (student ^name n2 ^mark >m)) → (winner ^name

n1)

–

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Combining conditions

• Single condition is too restrictive
• Conjunction supported by most

– If A and B then ….

• Disjunction also possible.

– If A or B then … – Can be done by two separate rules.

• Negation permitted with some restriction

– Negation may raise semantic problems – student(X) and not passed(X,s1) – not student(X)

Sasikumar M, IIT Bombay

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Structure of conclusion

• Purpose

– Just infer something is true or false

• Disease includes meningitis

– Compute something

• Increase severity by 0.4
• No side effect...

– Perform an action

• Display message 13
• Side effects?
• Multiple pieces

– Conjunction (usual meaning) – Disjunction

• Complexities of managing multiple paths

Sasikumar M, IIT Bombay

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Annotations

• A number of annotations can add value to the

simple rule structure.

• Rule groups are often used to manage

interactions and provide structure.

• Rule ID, and priority tags.
• Attributes can be linked to ontologies.
• Applicability context as annotation vs as

additional clauses (Guidon story).

• Translation mappings for display, etc.

Sasikumar M, IIT Bombay

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Explaining with rules

• Keeping track of which rules were used when,

we can produce meaningful traces to user.

• is(fever,true), less(age,40), is(headache,false)

=> set(disease,abc)

• If fever is true and age less 40 and headache

is false then infer that disease is abc

• If patient has fever and his age is less than 40

and he is not having head ache, then it is possible he may be having abc disease.

• ….

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….

• Attach translation to attributes, operators,

conclusions, and so on.

• Translation can be simple mapping, or

contextual mapping.

• Possible to generate fairly good readable

paraphrasing of the rule automatically.

• Similarly, the control structure can also be

translated...

Sasikumar M, IIT Bombay

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….

• Where are you? Why do you want to know?

– I am trying to check if you have disease abc, and

there is a rule R1 which can be used for this. I am verifying its conditions.

• How did you know...

– This information was provided by you earlier for

my question …

– This information was arrived at from rules r1, r14,

and r20.

Sasikumar M, IIT Bombay

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Rules in an uncertain world..

• Symptom disease mapping, behaviour error

mapping, etc usually not fully certain.

• Uncertainty part of life in these cases.
• Rules need to factor this in.
• Certainty factor based model is common.
• Probability based systems, fuzzy rules etc also

used.

Sasikumar M, IIT Bombay

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Certainty factor in rules

• If A then B with confidence cf
• What does cf mean?

– Maximum confidence B can get from here

• A itself may be uncertain

– Should reflect in the confidence of B

• Multiple rules may suggest B

– Some way to aggregate support from multiple

sources.

Sasikumar M, IIT Bombay

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Certainty factor

• A bounded number: 0 to 1, -1 to 1, 0 to 100,

etc

– -1 to +1: negative indicates false, positive

indicates true.

– 0 to 1: rules of probability can be used.

• Increasing CF means increasing belief in its

truth, and reverse for decrease.

Sasikumar M, IIT Bombay

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CF calculus

If A then B with CF=x1. A has CF = y1.

• CF of B (b1) = x1 * y1

If C then B with CF= x2. B gets CF = b2 Net: b1+b2? b1+b2*(1-b1)? max(b1,b2)? etc

Sasikumar M, IIT Bombay

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The rule!

• If a1 and a2 or a3 then c1 and c2 with

confidence cf1 and cf2 respectively, priority class B.

• Various syntactic frameworks possible

– Usually well delimited to make parsing easy – OPS-5 rich production rules

• Almost like a programming language

– Prolog format

• Logic programming model

Sasikumar M, IIT Bombay

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The rule

Context: col = 1, top = 5, bottom = 3, ans = ?, carry = ? current_focus(col) and greater(top, bottom) and first_column(col) => ans = top – bottom, carry = 0 (rule (and (current_focus ^col) (values ^col ^top ^bottom) (> ^top ^bottom) (first_column ^col)) (insert (result ^col (- ^top ^bottom) /* ans */ 0 /* carry */))

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….

In a procedural language, this could be: get_values(current_focus, &top, &bot); if (top > bot) && (current_focus == 1) ans = top – bottom; carry = 0; set_values(current_focus, top, bot, ans, carry); current_focus++;

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Rules in ATS

• Rules as knowledge units provide a lot of

flexibility.

• Useful for domain model, student model and

tutor model.

• Eg.: can build student model around this.

– Does he know rule x?

• The simple and uniform structure facilitates

– Malrules, error diagnosis, corrective action, etc

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More on rules

• Store rules externally, and build a parser to build an

internal representation.

– For languages like python, can do as part of program

itself!

• Sample python rulebase:

rules = { "r1":([("age",">",40),("fever","is","true")], ("disease","abc")), "r2":(2,2), "r3":(3,3) }

• Dictionary → tuple → list → tuple
• Inspectable, and executable.

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….

def display(rid): rule = rules[rid]; (ante,conse) = rule; print “Rule “+rid+”: “; print “IF “; for i in ante: /* each condition */ (attr, opr, value) = I; print attr+” “+opr+” “+value+” and”; print “Then conclude that ..”; …..

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...

• Can perform automated analysis for

consistency, completeness, subsumption, etc

– Common problems with knowledge bases

• Can also be induced from examples

– Rule induction in machine learning

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Why not rules?

• Independence is not realistic in general.
• Managing control flow in large rulebases is a

problem.

• Procedural knowledge is difficult to capture and use.
• Different types of knowledge and one type of

representation

– Good or bad.

• Cognitive fidelity is difficult to achieve in case of

ATS/ITS

– ACT-R?

Sasikumar M, IIT Bombay

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Wrapping up

• The simple if-then rules provide a rich and

powerful representation for knowledge representation (in ITS).

• Choose an appropriate structure for syntax to

limit the parsing and interpreting complexity.

• There are frameworks like JESS available, if

your requirement matches.

• Building one is also possible.

Sasikumar M, IIT Bombay

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Thank you

sasi@cdac.in, thelittlesasi@gmail.com