Y. Shoham, N. Goyal "Temporal reasoning in artificial intelligence" in Exploring artificial intelligence, H. Shrobe, Ed., Morgan-Kaufmann, pp. 419-438, 1988.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....temporal logic [WORB90] for queries as What was the first deed that John registered . Dynamic logic [WORB90] if the application contemplates actions that modify the contents of the database, such as, if I modify A : will 13 be affected by the integrity constraints . Non monotonic logic [SHOH88], if reasoning is to be done in data poor environments and the system is forced to jump into conclusions by providing an extra body of hypotheses based upon common sense or past experiences. 4 Representations of time The discussions on this topic are divided into three categories: the ....

.... facilities provided in 00 DBMS for the creation, modification, and version maintenance of complex objects [KIM88] iii) Existing categorizations of temporal propositions, depending on how an assertion s validity propagates from a given time interval to others containing it or contained within it [SHOH88]. For example, a proposition is downward hereditary if its validity over an interval implies its validity on each of the subintervals; a proposition is clay like if whenever it holds on two consecutive intervals, it holds in their union. iv) Existing mechanisms for propagating changes across ....

Y. Shoham, N. Goyal "Temporal reasoning in artificial intelligence" in Exploring artificial intelligence, H. Shrobe, Ed., Morgan-Kaufmann, pp. 419-438, 1988.

....may be planned to be carried out at the same time in order to save time, or decrease production cost, or for many other context dependent purposes. Admitting concurrent actions in formalisms for reasoning about change is so important that Shoham took it as a requirement [20] In the survey talk [21] temporal reasoning methods in AI were classified into two approaches: the change based approach (such as the situation calculus) and the time based approach, and seven problems (limitations) were identified with temporal reasoning systems: instantaneous actions, instantaneous and immediate ....

Shoham, Y. and N. Goyal, Temporal reasoning in Artificial Intelligence, in: Exploring Artificial Intelligence, H. E. Shrobe (ed. ), Morgan Kaufmann Publishers, Inc. ,

....we added space) it would appear that we then provide the required functionality. However, time is merely a framework in which change is possible. As expressed by Accepted for publication in book of ESF GISDATA Conference on Modelling Change in Socio Economic Units, Napthlion, Greece, 1998 2 Shoham and Goyal (1988), The passage of time is important only because changes are possible with time. The concept of time would become meaningless in a world where no changes were possible. The principal thesis that is explored in this chapter is that it is not time that is the key to conceptual modelling of ....

Shoham, Y. and Goyal, N. 1988. Temporal reasoning in artificial intelligence. In Shrobe H. (ed.), Exploring Artificial Intelligence, pp. 419-438, Morgan Kaufmann.

.... also interesting to note, in the AI literature, that Rosenschein s formulation of planning problems in dynamic logic [ Rosenschein, 1981 ] omitted the ff construct ) Despite its expressiveness, dynamic logic has been criticized within the AI community for its rigid change of state semantics [ Shoham and Goyal, 1988 ] More closely related to our approach is the family of process logics proposed by Pratt [ Pratt, 1979; Harel et al. 1982 ] in which modal operators can refer to the total trajectory in the execution of an action. We have also argued in this paper that our formalism for reasoning about ....

Y. Shoham and N. Goyal. Temporal reasoning in artificial intelligence. In H. Shrobe, editor, Exploring Artificial Intelligence. Morgan Kaufmann, 1988.

....any major difficulties. However, quite different intuitions surface as soon as people undertake to construct a formal temporal representation. 2.1.1 Change Based vs. Time Based Representations There have been two approaches for representing temporal phenomena: change based and timebased [SG88] The change based approach is based on the intuition that time is important only because the world around us changes constantly. If the world was static, there would probably be no reason for having a notion of time. In this approach, the entities which bring about change, called ....

....change based approach is based on the intuition that time is important only because the world around us changes constantly. If the world was static, there would probably be no reason for having a notion of time. In this approach, the entities which bring about change, called change indicators in [SG88] are given primary consideration. For example, in situation calculus [MH69] the primitives are actions; states come into being only as a result of executing actions. In a similar way, events are the primitives in event calculus [KS86] they start or end time periods during which relationships ....

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Yoav Shoham and Nita Goyal. Temporal Reasoning in Artificial Intelligence. In Strobe, Howard and AAAI, editor, Exploring Artificial Intelligence, pages 419--439. Morgan Kaufmann, 1988.

....In the context of temporal databases, Snodgrass and Ahn have used the term valid time for history time and transaction time for belief time [SA85] 2.1 Change Based vs. Time Based Representations There have been two approaches for representing temporal phenomena: change based and timebased [SG88] The change based approach is based on the intuition that time is important only because the world around us changes constantly. If the world was static, there would probably be no reason for having a notion of time. In this approach, the entities which bring about change, called ....

....change based approach is based on the intuition that time is important only because the world around us changes constantly. If the world was static, there would probably be no reason for having a notion of time. In this approach, the entities which bring about change, called change indicators in [SG88] are given primary consideration. For example, in situation calculus [MH69] the primitives are actions; states come into being only as a result of executing actions. In a similar way, events are the primitives in event calculus [KS86] they start or end time periods during which relationships ....

[Article contains additional citation context not shown here]

Yoav Shoham and Nita Goyal. Temporal Reasoning in Artificial Intelligence. In Strobe, Howard and AAAI, editor, Exploring Artificial Intelligence, pages 419--439. Morgan Kaufmann, 1988.

....that time is dense. We present results on consistency checking, canonical forms and variable elimination for this new class of temporal constraints. 1 INTRODUCTION In recent years temporal reasoning has received much attention from the artificial intelligence and database community (see [SG88, Sno90] for surveys) This is a rather natural trend since reasoning about time is essential in many applications (e.g. natural language understanding, planning, scheduling etc. In the artificial intelligence community in particular, some researchers have introduced binary constraint networks as ....

Yoav Shoham and Nita Goyal. Temporal Reasoning in Artificial Intelligence. In Strobe, Howard and AAAI, editor, Exploring Artificial Intelligence, pages 419--439. Morgan Kaufmann, 1988.

....we were to assume a discrete temporal model we would set t = t 1. 3.3. 1 The structure of causal rules Many proposals have appeared for reasoning about the dynamics of the world, variously called theories of time [McDermott 1982] theories of action [Lifschitz 1987] or theories of change [Shoham 1988]. The idea in all cases is the same: we identify events as the propositions that initiate change in the world; events change the state of facts. The agent s world model consists of a set of causal rules that dictate what event types cause what changes to what fact types under what circumstances. ....

....an exogenous event triggering a rule, which then triggers other rules subsequently. This assumption allowed us to compute s probability at t taking into account the world s state prior to t only. All of these assumptions are common in the work on causal reasoning within a logical framework (e.g. [Shoham 1988]) Finally we made the following assumptions about the structure of the probability space: ffl Sensor reliability not systematically predictable. We assumed that an observation s reliability parameter is not correlated with other states of the world, in particular that it is independent of the ....

[Article contains additional citation context not shown here]

Yoav Shoham and Nita Goyal. Temporal Reasoning in Artificial Intelligence. In Howard Shrobe, editor, Exploring Artificial Intelligence. Morgan Kaufmann, 1988.

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