T. L. Dean and D. V. McDermott. Temporal data base management. Artificial Intelligence, 32:1--55, 1987.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....1970s onwards, computer scientists working in planning [3,4] and temporal reasoning [5 11] rediscovered relation algebra. Later, scholars working in the field of Knowledge Representation, and specifically Spatial and Temporal Knowledge Representation, also used the formalism of relation algebra [10,12 18]. For them, the principal method of reasoning using a relation algebra was by checking the consistency of a set of constraints over that algebra. So this work became integrated with a wider study of constraint handling in computer science [19 24] This problem of determining the satisfiability of ....

T. Dean, D. McDermott, Temporal database management, Artificial Intelligence 32 (1987) 1--55.

.... arm TASK: NO OP [ MOVE ( b) TOP: CLEAR ON (a, table) MOVE (a, b) TOP: CLEAR (b) NO OP CLEAR ( a) ON (a, ON (a, b) BOTFOM: ON (b ,table) Figure 6: A simple plan in HSTS TBDB. The HSTS TBDB is a constraint based temporal database that extends the time maps approach [6]. At any point in time, HSTS TBDB can only represent a set of behaviors of a dynamical system specified in HSTS DDL. The constraints allow one to leave partially unspecified both the values and the time of occurrence of their start and end events on different segments of the time llne. A planner ....

T.L. Dean and D.V. McDermott. Temporal data base management. Artificial Intelli- gence, 32:1-55, 1987.

....To guarantee consistency either the data must be converted into a uniform representation that is independent of time granularity or temporal operations must be generalized to cope with data associated with di#erent temporal domains. In both cases, a precise semantics for time granularity is needed [4, 15, 21, 27, 30, 70, 71, 72, 93, 102, 109, 121, 122, 123]. With regard to data mining, a huge amount of data is collected every day in the form of event time sequences. These sequences represent valuable sources of information, not only for what is explicitly registered, but also for deriving implicit information and predicting the future behaviour of ....

T. Dean and D. V. McDermott. Temporal data base management. Artificial Intelligence, 32:1--55, 1987.

....manager consists of a set of task descriptions of nonzero length, the temporal constraints among them, and possibly nonzero delays between tasks, to cover communication and transportation delays. The agent incorporates a temporal reasoning system similar to Dean and McDermott s Time Map Manager [5] to build and maintain this representation. Our decision to use a traditional non linear planner, as opposed to a formulation that integrates planning and scheduling [11] is based on the fact that the integrated approach operates from a resource based time line, and in our system the set of ....

Thomas L. Dean and Drew V. McDermott. Temporal data base management. Artificial Intelligence, 32:1--55, 1987.

....temporal information is an essential part of many AI systems for individual agents. Several formalisms for expressing and reasoning about temporal knowledge have been proposed, most notably, Allen s interval algebra [2] Vilain and Kautz s point algebra [61] and Dean and McDermott s time map [13]. Each of these representation schemes is supported by a specialized constraint directed reasoning algorithm. At the same time, extensive research has been carried out on problems involving general constraints as in [37] Some of these have been extended to problems involving temporal constraints ....

T. L. Dean and D. V. McDermott. Temporal data base management. Artificial Intelli- gence, 32:1-55, 1987.

....of the sources of this ambiguity, which we treat as explicit disjunction, in the sense that ambiguous information can be interpreted as defining a set of possible conclusions. We describe how these sources of disjunction are dealt with in our current implementation of Dean s Time Map Manager [ Dean and McDermott, 1987, Dean, 1986 ] Briefly, we take one of three approaches. ffl We represent the disjunction explicitly. ffl We remove the disjunction by limiting the expressive power of the system. ffl We approximate the disjunction by a weaker form of representation that subsumes the disjunction. The first of ....

....describe these extensions in Section 5. In the rest of this paper, we briefly discuss the ontology and semantics of tmm, provide some specific examples of the kinds of disjunction that arise, and demonstrate the application and limitations of this approach. 2 TMM Dean s Time Map Manager (tmm) Dean and McDermott, 1987, Dean, 1986 ] is an implemented temporal reasoning system, intended as a foundation for building planning and scheduling systems. tmm includes capabilities for reasoning about partially ordered events, persistence and clipping, and simple causal reasoning, all in the presence of incremental ....

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Dean, Thomas and McDermott, Drew 1987. Temporal data base management. Artificial Intelligence 32(1):1--55.

....to be considered in all those reasoning tasks which take account of a dynamic domain. Most of AI systems incorporating an explicit temporal representation are based in some manner on constraint reasoning techniques [Allen, 1983; Malik and Binford, 1983; Valdes Perez, 1986; Vilain and Kautz, 1986; Dean and McDermott, 1987; Knight and JLxin, 1992; Porto and Ribeiro, 1992; Vila, 1994a] 1. Temporal constraints account for uncertainty in temporal knowledge up to a certain extent. Both qual itative [Allen, 1983; Vilain and Kautz, 1986; van Beek, 1989] and metric temporal constraints [Dechter et al. 1991] represent ....

Thomas L. Dean and Drew V. McDermott. Temporal data base management. Artificial Intelligence, 32:1987, 1987.

....systems that use data structures that reduce assertion time but increase query time appear to be effective. The six systems studied in this report are TimeLogic[Koomen, 1989] TimeGraph [Miller and Schubert, 1990] MATS[Kautz and Ladkin, 1991; Kautz, 1991] Tachyon[Arthur and Stillman, 1992] TMM[Dean and McDermott, 1987; Schrag et al. 1992] and TimeGraph II[Gerevini and Schubert, 1993] These systems differ greatly in their underlying mechanisms and expressive power. They can be roughly classified into systems that use constraint satisfaction techniques at assertion time and have a fixed query time (TimeLogic, ....

....( 2) Another improvement of TimeGraph II over TimeGraph is that it automatically structures the timegraph for efficiency. Since it uses the timegraph data structure, it also cannot handle disjoint relationship. The current version of TimeGraph II cannot handle metric information. TMM[Dean and McDermott, 1987; Schrag et al. 1992] is a temporal database management system. It uses temporal constraint graph (TCG) data structure to 2 construct a time map with simple point relations ( It can handle both metric and qualitative information but cannot handle disjoint relationship. 3 Dataset ....

T. Dean and D. McDermott, "Temporal data base management," Artificial Intelligence, 32:1-55, 1987.

....order to maintain the integrity of the knowledge base. Maintenance of these contexts requires extensive use of qualitative reasoning and a truth maintenance mechanism based on Doyle s system [5] These have been modi ed to handle qualitative processes and views. The tnms is similar to Dean s tmm [3], but has been modi ed to handle both processes and execution occurring over time. The functions of the tnms are as follows: The tnms must maintain the integrity of the temporal network contained in the world model in that no two tokens asserting contradictory information are allowed to ....

T.L. Dean and D.V. McDermott. Temporal database management. Articial Intelligence, 33:1-58, 1987.

.... By expressing knowledge of causal rules in terms of conditional probabilities, probabilistic temporal reasoning can make appropriate judgments concerning the persistence of propositions and the probabilistic projection problem [DK87] which is a probabilistic version of the projection problem [DM87], which involves computing the consequences of a set of conditions (observations) given a set of cause and effect relations (causal rules) Further work can be found in Kanazawa Dean [KD89] and Dean et. al [DFM89] 9 2.2.2 BURIDAN: Probabilistic Planning BURIDAN [KHW93, KHW94] is an ....

Thomas L. Dean and Drew V. McDermott. Temporal Data Base Management. Artificial Intelligence, 32:1--55, 1987.

....Temporal Reasoner Problem Solver Problem Data User Queries Solution T. Assertion T. Query T. Solution 316 L. Vila and L.Godo of temporal representation has been an active area within the AI community. Several general formalisms have been proposed for representing temporal knowledge [BRU72, KG77, DER82, ALL84, KS86, DD87, SHO87, GAL90, PR92, BAR93, VEI94, SKD94, VIL94a, VR95]. They di#er in the temporal ontological primitive they are based on and or in the sort of well formed forms they take for the representation language. These factors have a direct influence on the expressive power of the proposed formalism. Moreover, all those works that also take the issue of ....

....have a direct influence on the expressive power of the proposed formalism. Moreover, all those works that also take the issue of automated reasoning into consideration incorporate a specialized constraint directed representation with a reasoning algorithm to e#ciently manage temporal expressions [ALL84, DD87, BAR93, VEI94, SKD94]. Figure 1 shows a diagram from a system architecture point of view of the resulting scheme. Figure 1: Temporal Reasoning System architecture: the problem solver has a temporal extended language, and purely temporal expressions are managed by a specialized temporal reasoner. Significant examples ....

Thomas L. DEAN and Drew V. Mc DERMOTT. Temporal data base management. Artificial Intelligence, 32:1987, 1987.

....has recently been introduced in the literature [Jonsson et al. 1996] Quantitative Algebras A number of quantitative temporal algebras have been proposed in the literature, most or all of them based on time points. The ancestor of most of these algebras is the time map manager (TMM) [Dean and McDermott, 1987], which maintains a network of time points and metric relations between these. A relation consists of upper and lower bounds on the temporal distance between two time points. This is what Dechter et al. 1991] refers to as a binary constraint, which can be written as c t 1 Gamma t 2 d, where t ....

....also hold over all subintervals and does a proposition that holds at all time points in an interval also hold over the whole interval 6.4 Temporal reasoning Systems Various systems for temporal reasoning have been implemented or suggested. One of the first systems was TMM (Time Map Manager) [Dean and McDermott, 1987]. The TMM maintains a network of time points and information about upper and lower bounds for the metric duration between these, thus implementing a metric time point algebra. It is also possible to state that propositions are true or false at certain time points, and TMM implements a clipping ....

Thomas L Dean and Drew V McDermott. Temporal data base management. Artificial Intelligence, 32:1--55, 1987.

....took 15 20 minutes to get to work) is considered as quantitative information. Interval algebra (Allen 1983) and point algebra (Vilain Kautz 1986) are two traditional models to represent and reason with qualitative information when events are considered as intervals and points, respectively. In (Dean McDermott 1987) and (Dechter, Meiri, Pearl 1991) two systems for handling metric information between point events were proposed. The integration of qualitative and quantitative information between point and interval events was attempted in (Meiri 1996) and (Kautz Ladkin 1991) In (Barber 1993) an ....

Dean, T., and McDermott, D. 1987. Temporal data base management. Artificial Intelligence 32:1--55.

....In the rest of this paper, we provide a brief introduction to the TMM, describe the application of the TMM to scheduling, and describe some related work. 2 TMM Overview As part of the DARPA RL Planning Initiative, Honeywell has developed a new implementation of Dean s Time Map Manager (TMM) [5], involving improvements in robustness, user interface, and documentation, in addition to a number of extensions in functionality. The TMM provides users and application programs (e.g. planners and schedulers) with the following functionality: ffl Metric and ordering constraints between any two ....

....evaluation, and algorithms that are designed to search only those parts of the database that may result in useful answers. 3 Scheduling Using the TMM The assumptions underlying our scheduling work are as follows: 2 This capability (temporal reason maintenance) is described in detail elsewhere [5]. 4 SV1 SV2 Figure 2: Time line scheduling SV1 SV2 Figure 3: Constraint posting scheduling and the resulting partial order 1. Explicitly modelling the constraints resulting from specific scheduling decisions makes the schedule easier to construct and modify. 2. Representing only those ....

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Dean, Thomas and McDermott, Drew V., Temporal Data Base Management, Artificial Intelligence, 32 (1987) 1--55.

....more thanthe JTMS. Intime varying situations whichthe beFF change the truth maintefi syste cannotcorrefiF maintain a knowleCF base be 9CF it do e notconside renside among teg oral data. To solve thefi probleob ne approache we suggeBfiE such asthe TRMS (Tefi oralrelfi maintenfi B systeB [3] and the ExteBF Cfi TMS [4] The TRMS using a JTMS meMS d cannot maintain multiple contefiF andthe ExteF 9F TMS cannot maintain ane contefi that is ge9 CfiE throughinfehfi about tefi oralrelfiFH Manuscript received April 19, 1999. Manuscript revised September 9, 1999. The author is with ....

L. Dean and D. McDermott, "Temporal database management, " Artificial Intelligence, vol.32, no.1, pp.1--55, 1987.

....and or intervals) 2) explicit constraints on their temporal separation, and (3) implicit constraints like the interval transitivity relations of [ Allen, 1983 ] or the transitivity properties of Euclidean metric distance, compute the tightest bounds on the separation of any two individuals. Dean and McDermott, 1987 ] Koomen, 1988 ] and [ Kautz and Ladkin, 1991 ] report on implementations of these domain independent algorithms. There have been two predominant approaches to temporal reasoning. Interval based systems, Allen, 1983 ] take intervals to be the fundamental temporal entities, and allow the ....

....algorithms. There have been two predominant approaches to temporal reasoning. Interval based systems, Allen, 1983 ] take intervals to be the fundamental temporal entities, and allow the specification of qualitative constraints that hold between intervals. Systems based on time points [ Dean and McDermott, 1987; Dechter et al. 1991 ] on the other hand, take instants in time (points) to be the fundamental temporal entities, and allow both qualitative and metric constraints among those individuals. Constraints specify that the temporal distance between two time points falls within some particular ....

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T. Dean and D. McDermott. Temporal data base management. Artificial Intelligence, 32(1), April 1987.

....databases have no temporal support whatsoever nor any deontic support. Actions and time: have been extensively studied by many researchers in several areas of computing (e.g. Manna and Pnueli 1992; Haddawy 1991; Morgenstern 1988; Allen 1984; Lamport 1994; Nirkhe, Kraus, Perlis, and Miller 1997; Dean and McDermott 1987; McDermott 1982; Rabinovich 1998; Singh 1998) We present here the main di erences between others work and ours, and discuss work that combines time with deontic operators. Surveys of research on temporal reasoning include (Benthem 1991; Benthem 1995; Baker and Shoham 1995; Lee, Tannock, and ....

Dean, T. and D. McDermott (1987). Temporal data base management. Arti cial Intelligence 32 (1), 1-55.

....lines on a log log scale. Finally, we should mention some experiments conducted by Yampratoom and Allen [36] comparing the performance of Timegraph I and II with several temporal reasoning systems based on constraint propagation algorithms TimeLogic [17] MATS [16] Tachyon [4] and TMM [6] in which the timegraph approach proved by far the most efficient for large data sets generated for the TRAINS world [1] 12 In order to simplify the figure these numbers are not shown. 13 These experiments were conducted on a sun sparcstation 2 and hence a comparison with the results in ....

T. Dean and D. V. McDermott. Temporal data base management. Artificial Intelligence, 32:1--55, 1987.

....Davis [8] provides a complete study on constraint propagation with interval or sign values. In his paper, Davis includes examples of applications on several domains, characteristics of different constraint languages, their strengths and weaknesses, and a complexity analysis for various cases. TMM [13] keeps a set of events, denoting particular instants of time. The time at which a given event happens, or the difference between events are bounden by intervals. Constraint propagation is used to infer as much as possible about time relationships between events. CLP(intervals) 3] implements a ....

T. L. Dean and D. V. McDermott. Temporal data base management. Artificial Intelligence, 32:1--55, 1987.

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T. L. Dean and D. V. McDermott. Temporal data base management. Artificial Intelligence, 32:1--55, 1987.

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Thomas L. Dean and Drew V. McDermott. Temporal data base management. Artificial Intelligence, 32:1--55, 1987.

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T. L. Dean and D. V. McDermott. Temporal data base management. Artificial Intelligence, 32:1--55, 1987.

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Dean T., and D. V. Mc Dermott, Temporal Data Base Management, Arti cial Intelligence, 32:1-55, 1987.

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T. L Dean and D. V. McDermott. Temporal database management. Artificial Intelligence, 32:1--55, 1987.

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#26# T.L. Dean and D.V. McDermott. Temporal data base management. Arti#cial Intelligence,

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