”Representing Temporal Knowledge for Case-Based Prediction”

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: • Create a <has interval> relationship • Create <has finding> relationships • Create <Temporal Relation> relationships • Infer new <Temporal Relation> 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. Original: Activation strength Explanation strength Matching strength Temporal similarity matching: Temporal path strength Temporal Paths & Dynamic Ordering

21. 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

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?