Enhancing Case-Based Reasoning with Temporal Knowledge for Event Prediction
This paper explores the integration of temporal knowledge into case-based reasoning (CBR) to improve predictions of unwanted events in industrial processes. It highlights the limitations of traditional CBR approaches, which often ignore temporal relations, and proposes a model that combines case data with general domain knowledge within a semantic network. Key concepts include the use of interval-based temporal logic to predict events, supporting frameworks based on James Allen’s temporal intervals, and success stories in applications like oil drilling. The findings emphasize the importance of representational structures for effective matching and reasoning.
Enhancing Case-Based Reasoning with Temporal Knowledge for Event Prediction
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Presentation Transcript
”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 research • Summary of James Allen’s temporal intervals • Introduces problem of predicting unwanted events in an industiral process • Temporal representation in system • How representation is utilized for matching of temporal intervals
Overview • Related research • Summary of James Allen’s temporal intervals • Introduces problem of predicting unwanted events in an industiral process • Temporal representation in system • How 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 prediction • Branting and Hastings: pest management, ”temporal projection” • McLaren & Ashley: temporal intervals, engineering ethics system
Hypothesis • Large and complex data • Explanatory reasoning methodes underlying the CBR apporach • Strongly indicate that a qualitative, interval-based framework for temporal reasoning is preferrable ?
Overview • Related research • Summary of James Allen’s temporal intervals • Introduces problem of predicting unwanted events in an industiral process • Temporal representation in system • How representation is utilized for matching of temporal intervals
Allen’s temporal intervals • Interval-based temporal logic • Intervals decomposable • Intervals may be open or closed • Intervals: hierarchy connected by temporal relations • ”During” hierachy propostions inhereted • 13 ways ordered pair of intervals can be related (mutually exclusive temporal rel.)
Allen’s temporal intervals(2) • Temporal network, transitivity rule • Generalization method using reference intervals
Overview • Related research • Summary of James Allen’s temporal intervals • Introduces problem of predicting unwanted events in an industiral process • Temporal representation in system • How representation is utilized for matching of temporal intervals
Prediction of unwanted events • Oil drilling domain • Stuck pipe situation • Alert state • Alarm state
Overview • Related research • Summary of James Allen’s temporal intervals • Introduces problem of predicting unwanted events in an industiral process • Temporal representation in system • How representation is utilized for matching of temporal intervals
Temporal representation in Creek • Allen’s approach • Intervals stored as temporal relationships inside cases • Cases restrict computational complexity • Transitivity • Case + explanations
Temporal representation in Creek(2) • Two intervals added: • For every new interval that is added to the network: • Create a <has interval> relationship • Create <has finding> relationships • Create <Temporal Relation> relationships • Infer new <Temporal Relation> relationships
Overview • Related research • Summary of James Allen’s temporal intervals • Introduces problem of predicting unwanted events in an industiral process • Temporal representation in system • How representation is utilized for matching of temporal intervals
Original: Activation strength Explanation strength Matching strength Temporal similarity matching: Temporal path strength Temporal Paths & Dynamic Ordering
Temporal Paths & Dynamic Ordering (2) • Dynamic ordering algorithm: • Find first interval in IC and CC • Check intervalIC and intervalCC for matching or explainable findings • If match - Update temporal path strength • Check {getSameTimeIntervals} for new information and special situations If special situations - Perform action • {getNextInterval} from CC and IC • Unless {getNextInterval} is empty - Go to (2) • Return temporal path strength
Example Prediction • Oil-well drilling • Highlights: • 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?