”Representing Temporal Knowledge for Case-Based

Prediction”

Martha Dørum Jære, Agnar Aamodt, Pål Skalle

Introduction

Current CBR: snap-shots in time, temporal relations ignored or handeled explisit within reasoning algorithms

Real world context (more interactive and user-transparent)

Creek

integrates cases with general domain konwledge within a single semantic network

feature and feature value -> concept in semantic network

Interliked with other consept, semantic relations specified in general domain model

General domain knowledge : model based reasoning support to the CBR processes Retrieve, Reuse and Retain

Overview

Related researchSummary of James Allen’s temporal

intervalsIntroduces problem of predicting unwanted

events in an industiral processTemporal representation in systemHow representation is utilized for matching

of temporal intervals

Overview

Related researchSummary of James Allen’s temporal

intervalsIntroduces problem of predicting unwanted

events in an industiral processTemporal representation in systemHow representation is utilized for matching

of temporal intervals

Related research

Early AI research on temporal reasoning make distinction between point-based (instans-based) and interval-based (periode-based)(Allen)

Jaczynski and Trousse: Time-extended situations

Mendelez: supervicing and controlling sequencing of process steps that have to fulfill certain conditions

Related research (2)

Hansen: weather predictionBranting and Hastings: pest management,

”temporal projection”

McLaren & Ashley: temporal intervals, engineering ethics system

Hypothesis

Large and complex dataExplanatory reasoning methodes

underlying the CBR apporachStrongly indicate that a qualitative,

interval-based framework for temporal reasoning is preferrable

?

Overview

Related researchSummary of James Allen’s temporal

intervalsIntroduces problem of predicting unwanted

events in an industiral processTemporal representation in systemHow representation is utilized for matching

of temporal intervals

Allen’s temporal intervals

Interval-based temporal logicIntervals decomposableIntervals may be open or closedIntervals: hierarchy connected by temporal

relations ”During” hierachy propostions inhereted13 ways ordered pair of intervals can be

related (mutually exclusive temporal rel.)

Allen’s 13 ways

Allen’s temporal intervals(2)

Temporal network, transitivity ruleGeneralization method using reference

intervals

Overview

Related researchSummary of James Allen’s temporal

intervalsIntroduces problem of predicting unwanted

events in an industiral processTemporal representation in systemHow representation is utilized for matching

of temporal intervals

Prediction of unwanted events

Oil drilling domainStuck pipe situation

Alert stateAlarm state

Overview

Related researchSummary of James Allen’s temporal

intervalsIntroduces problem of predicting unwanted

events in an industiral processTemporal representation in systemHow representation is utilized for matching

of temporal intervals

Temporal representation in Creek

Allen’s approachIntervals stored as temporal relationships

inside casesCases restrict computational complexityTransitivityCase + explanations

Temporal representation in Creek(2)

Two intervals added:

For every new interval that is added to the network:

1. Create a <has interval> relationship2. Create <has finding> relationships3. Create <Temporal Relation> relationships4. Infer new <Temporal Relation> relationships

Temporal representation in Creek(3)

Overview

Related researchSummary of James Allen’s temporal

intervalsIntroduces problem of predicting unwanted

events in an industiral processTemporal representation in systemHow representation is utilized for matching

of temporal intervals

Temporal Paths & Dynamic Ordering

Original: Activation strength Explanation strength Matching strength

Temporal similarity matching: Temporal path strength

Temporal Paths & Dynamic Ordering (2)

Dynamic ordering algorithm:

1. Find first interval in IC and CC 2. Check intervalIC and intervalCC for matching or

explainable findings3. If match - Update temporal path strength4. Check {getSameTimeIntervals} for new information and

special situationsIf special situations - Perform action

5. {getNextInterval} from CC and IC6. Unless {getNextInterval} is empty - Go to (2)7. Return temporal path strength

Example Prediction

Oil-well drillingHighlights:

Retrieving similar cases (matching strength above treshold)

Compare -> temporal path stregth i.e. alerts

Conclusion

Support prediction of events for ind. processes

Allen’s temporal intervals incorporated into Creek

I

Conclusion (2)

+: Intervals->closer to human expert think Integration into model based reasoning system

component

Conclusion (3)

- : One fixed layer of intervals System: Raw data -> qualitative changes Many processes too complex

Discussion

Hypotheses = ? How represent time intervalls in cases?

(When having to monitore over time?)Continous matching? Or treshold/event

driven?