”representing temporal knowledge for case-based prediction” martha dørum jære, agnar aamodt,...
Post on 22-Dec-2015
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”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 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
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
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