”Representing Temporal Knowledge for Case-Based Prediction”

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Representing Temporal Knowledge for Case-Based Prediction. Martha Drum Jre, Agnar Aamodt, Pl 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). - PowerPoint PPT Presentation

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  • Representing Temporal Knowledge for Case-Based PredictionMartha Drum Jre, Agnar Aamodt, Pl 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)

  • Creekintegrates cases with general domain konwledge within a single semantic network feature and feature value -> concept in semantic networkInterliked with other consept, semantic relations specified in general domain modelGeneral domain knowledge : model based reasoning support to the CBR processes Retrieve, Reuse and Retain

  • OverviewRelated researchSummary of James Allens temporal intervalsIntroduces problem of predicting unwanted events in an industiral processTemporal representation in systemHow representation is utilized for matching of temporal intervals

  • OverviewRelated researchSummary of James Allens temporal intervalsIntroduces problem of predicting unwanted events in an industiral processTemporal representation in systemHow representation is utilized for matching of temporal intervals

  • Related researchEarly AI research on temporal reasoning make distinction between point-based (instans-based) and interval-based (periode-based)(Allen)Jaczynski and Trousse: Time-extended situationsMendelez: 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

  • HypothesisLarge and complex dataExplanatory reasoning methodes underlying the CBR apporachStrongly indicate that a qualitative, interval-based framework for temporal reasoning is preferrable?

  • OverviewRelated researchSummary of James Allens temporal intervalsIntroduces problem of predicting unwanted events in an industiral processTemporal representation in systemHow representation is utilized for matching of temporal intervals

  • Allens 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.)

  • Allens 13 ways

  • Allens temporal intervals(2)Temporal network, transitivity ruleGeneralization method using reference intervals

  • OverviewRelated researchSummary of James Allens 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 eventsOil drilling domainStuck pipe situation

    Alert stateAlarm state

  • OverviewRelated researchSummary of James Allens 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 CreekAllens 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:

    Create a relationshipCreate relationshipsCreate relationshipsInfer new relationships

  • Temporal representation in Creek(3)

  • OverviewRelated researchSummary of James Allens 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 OrderingOriginal:Activation strengthExplanation strengthMatching strengthTemporal similarity matching:Temporal path strength

  • Temporal Paths & Dynamic Ordering (2)Dynamic ordering algorithm:

    Find first interval in IC and CC Check intervalIC and intervalCC for matching or explainable findingsIf match - Update temporal path strengthCheck {getSameTimeIntervals} for new information and special situationsIf special situations - Perform action {getNextInterval} from CC and ICUnless {getNextInterval} is empty - Go to (2)Return temporal path strength

  • Example PredictionOil-well drillingHighlights:Retrieving similar cases (matching strength above treshold)Compare -> temporal path stregth i.e. alerts

  • ConclusionSupport prediction of events for ind. processesAllens temporal intervals incorporated into CreekI

  • Conclusion (2)+:Intervals->closer to human expert thinkIntegration into model based reasoning system component

  • Conclusion (3) - :One fixed layer of intervals System: Raw data -> qualitative changesMany processes too complex

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

    Moving away from self contained problem solvers to user interactive assistantse.g. SEMANTIC RELATIONS: (subclass and instance relations, part-subpart, process-subprocess, causal and functional realtions, as well as particular domain realtions (e.g. has color )

    Case retrival: partly an explanatory process, in which intitial matching based on index links are justified or citicizedReuse: becomes process of explaining the adaption of past case within the context of the current problemRetain: becomes the process of explaining/justifying what to retain from a problem just solved

    Extend to include temporal realtionships (utilized by the explanatory mechanism that underly the systems reasoning model)

    Instants: Situation Calculus and Time specialist) based on instans as the temporal primitiveAllen: theory of temporal intervals, advocates that the interval is the appropriate temporal primitve for reasoning about timeTime-extended situations: temporal knowledge is represented as temporal patterns, i.e. Multiple streams of data related to time pointsMendelez: i.e. Recipies for making products. Case repr recipe, temporal problem is the controll a set of recipies (a batch) in order to fullfill process conditions and achieve product goal. Deviation = event = actions and reactions.

    Hansen:point-based representation of whether observations are utilized in a combined CB and fuzzy set system. Time included in similarity metric (togehter with other weather parameters).

    Branting & Hastings: ki-CBR system, temporal projection : aligns two cases in time, projecting a retireval case forward or backwards in order to match on other parameters

    HOWEVER! All above essentialy point-based! Scarse research on temporal reasoning interval based apporach. McLaren&Ashley:temporal relations used for checking time consistency among facts that match other criteria, temporality amongst several matching criterias (this paper: more central role!)Decomposable:Can always be decomposed into sub-parts, including time pointsClosed: meet each others exactlyOpen: wil be point between them that has an empty state when neither of them trueIf interval X is during an interval Y, and P holds during X, then P holds during Y.Dring hierarchy allows reasoning processes to be constrained so that irrelevant facts are not considered. 13: express any relationship that can exist between two intervals. Maintained in network where nodes represents individual intervals. Alg. Who creates missing temporal realtions (temporal constraints) from existing knowledge.

    When new temporal realtion entered in a temporal network all possible temporal realtionships can be derived by use og the transitivity rule.

    Reference interval:cluster of intervals where the temporal constraints have been computed.

    Costly, trends prametersQualitive changes of particular parameters, strong indicators

    Alert:matching past case/expereience indicates an upcoming unwanted event, past case actual stuck pipe or a situation where stuck pipe was indicated but avoided Alarm:seemingly unavoidable unwanted event is about to happen(alert: warning about alarm, ideally alert state discovered in time to avoif the alarm state)(SP 3 parameters, continously access, identfy early indicators)2 sources knowledge:general doimain model and case base (similarity above similarity treshold) Open domains, imprecise and uncertain knowledgeReference interval:cluster of intervals where the temporal constraints have been computed. (just like Allens ref. Int.)

    RESTRICT COMPL: limiting amount og temporal relations resulting from transitivity rulesTrans. Rules: higher degree of regularity, created relations (cognitive persp.) NOT as human way of thinking about timeMORE explanatory-based way of reasoning about temporal relationshipsCombine temporal knowledge inside a case with explanations in the general domain knowledge

    Each relation has a inverse relation (in Creek)Intervals connected to each other with temporal realtions (meets)

    #4: transivity rulesActivation strength: direct index matchingEXPLANATION: similarity explained in the general domain modelMATCHING: combines the two former into resulting similarity degreeTEMPORAL: each such path the matching degree og corresponding findings is calculated(comparing temporal paths: NOT trivial because often many possible telmporal paths for a combination of two primitie realtions)Search space redused by goal directed search(guided by what to predict)ALG: enable intervals to be related to each othersimilarity assesment of parameters can be made to predict particulare statesIC: input case, CC: current1.) Find start point 2.) first intervals: activation, explanation and matching strength computed (no match, compare 2. int in CC comp 1. IC)3.) matching strength (path strength this temporal path), added to the temporal path strength4.) find all intervals in CC that has the same time as intervalIC (all findings share some time-points with intervalIC) (keeps current time perspective of the input case, while matching corresponding findings) (i.e. Contains, during, equals, starts, started-by, finishes, finished-by)

    7.)accumulated temporal path strength is normalized

    ALG: Trace temporal findings, compare intervals, update temporal path stregth, {getsametimeintervals},find next interval {getnextinterval}, update temporal path strength

    i.e. Ix5 and Iy5 checked for goal-related situations -> alert stateExplained finding, matched finding

    CLOSER to the way human experts think/reason

    HOWEVER two-step retrieval methode of Creek, interval-based apporach work with pure syntax-based retreieval methods WEAKNESS: only allow one fixed layer of intervals(fix: more flexible: subintervals)

    TOO COMPLEX: not able to predicted by standard trend analysis

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