Classification and Abduction

Expert Systems: Matching Techniques to Tasks

B. Chandrasekaran

The Ohio State University

Laboratory for Artificial Intelligence Research

LAIR - Toolset

Classification Knowledge

Organization & Form

Specialists:

- hierarchy of specialists mirrors diagnostic classification hierarchy

- each node in hierarchy corresponds to state

of the situation under consideration (hypothesis)

- domain knowledge distributed across specialists

Advantages:

* divides problem into managable pieces

* organizes knowledge into pieces which become relevant when corresponding concepts become relevant.

- confirmatory rules

- exclusionary rules

- recommendation rules

(* note: these could be rules, could be logic, could be frames, could be pattern recognition, etc.

The organization is important, not the lower level implementation choice)

- nodes higher in the hierarchy represent general hypothesis; lower represent more specific hypothesis (top-down)

Classification Control:

Establish/Refine

Establish: specialist establishes because relevant (high confidence)

Refines by invoking subs

(which then perform establish/refine)

Reject: confidence low - performs no further actions - prunes hierarchy

Suspend: moderate confidence - may later refine if requested (blackboard - secondary to...)

* non establish-set lower ; ask for more info

Recommendation: pathognomonic rules

Abduction

Generation of explanation

(if a b)

b .

a

(If ( drunk ?person) (not (walk-straight ?person)

(not (walk-straight Jack) .

(drunk Jack)

Only a guess...Not a valid logical implication

Jack could have just got off a roller coaster.

Abduction is only plausible inference

Careful : Our notion of explanation => causality

causality and logical implication are not the same thing

(if (in ?patient ward5) (have ?patient cancer))

(have Eliza cancer) .

(in Eliza ward5)

(cause ?x ?y) : x causes y

?y . : and y is true

?x :so hypothesize ?x as explanation

Diagnosis:

D is a collection of data (facts, observations)

Hypothesis H is plausible under the circumstance

H explains D (would, if true, explain D)

No other hypothesis explains D as well as H does

Therefore, H is correct

Abductions go from data describing something, to an explanatory hypothesis that best accounts for that data. Other names

inference to the best explanation

the explanatory inference

The mechanism:

a) a classification machine for selecting plausible hypotheses

b) a specialized means-ends machine for assempling plausible hypotheses into a best explanation.

The assembler is used repeatedly by an overview critic which

* produces an initial "best explanation" for the case

*investigates the space of alternative explanations to critique the initial one

* produces a final "best explanation"

Hypothesis interaction:

* A and B are mutually compatible - explanatory capabilities overlap

* Hypothesis A is a subhypothesis of B

(a more detailed refinement)

* A and B are mutually incompatible

* A and B cooperate additively where they overlap

Want composite hypothesis which is

- as complete as possible

- maximally plausible

- parsimonious

Each plausible hypothesis delivered by the classifier comes with:

* a description of which findings it explains

* a plausibility value (confidence in this hyp)

* significance

* interactions with other hypotheses

Means-ends

One knows what they want to achieve

and what they have. Means-ends centers around the detection of differences between the current state and the goal state...and what means can be used to get to the goal state