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”Representing Temporal Knowledge for Case-Based Prediction” Martha Dørum Jære, Agnar Aamodt, Pål Skalle.

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1 ”Representing Temporal Knowledge for Case-Based Prediction” Martha Dørum Jære, Agnar Aamodt, Pål Skalle

2 Introduction Current CBR: snap-shots in time, temporal relations ignored or handeled explisit within reasoning algorithms Real world context (more interactive and user-transparent)

3 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

4 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

5 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

6 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

7 Related research (2) Hansen: weather prediction Branting and Hastings: pest management, ”temporal projection” McLaren & Ashley: temporal intervals, engineering ethics system

8 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 ?

9 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

10 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.)

11 Allen’s 13 ways

12 Allen’s temporal intervals(2) Temporal network, transitivity rule Generalization method using reference intervals

13 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

14 Prediction of unwanted events Oil drilling domain Stuck pipe situation Alert state Alarm state

15 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

16 Temporal representation in Creek Allen’s approach Intervals stored as temporal relationships inside cases Cases restrict computational complexity Transitivity Case + explanations

17 Temporal representation in Creek(2) Two intervals added: For every new interval that is added to the network: 1.Create a relationship 2.Create relationships 3.Create relationships 4.Infer new relationships

18 Temporal representation in Creek(3)

19 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

20 Temporal Paths & Dynamic Ordering Original:  Activation strength  Explanation strength  Matching strength Temporal similarity matching:  Temporal path strength

21 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 findings 3.If match - Update temporal path strength 4.Check {getSameTimeIntervals} for new information and special situations If special situations - Perform action 5. {getNextInterval} from CC and IC 6.Unless {getNextInterval} is empty - Go to (2) 7.Return temporal path strength

22 Example Prediction Oil-well drilling Highlights:  Retrieving similar cases (matching strength above treshold)  Compare -> temporal path stregth  i.e. alerts

23 Conclusion Support prediction of events for ind. processes Allen’s temporal intervals incorporated into Creek I

24 Conclusion (2) +:  Intervals->closer to human expert think  Integration into model based reasoning system component

25 Conclusion (3) - :  One fixed layer of intervals  System: Raw data -> qualitative changes  Many processes too complex

26 Discussion Hypotheses = ? How represent time intervalls in cases? (When having to monitore over time?) Continous matching? Or treshold/event driven?

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