Mechanizing temporal knowledge

Kahn, Ken; Gorry, G. Anthony

doi:10.1016/0004-3702(77)90015-7

Cited by 125 publications

(44 citation statements)

References 2 publications

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“…For example, "Mechanization of Temporal Knowledge" by Kahn [15] dates back to 1975. There exist many different temporal formalisms.…”

Section: Temporal Formalismsmentioning

confidence: 99%

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Efficient Temporal Reasoning with Uncertainty

Nilsson¹

2015

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Automated Planning is an active area within Artificial Intelligence. With the help of computers we can quickly find good plans in complicated problem domains, such as planning for search and rescue after a natural disaster. When planning in realistic domains the exact duration of an action generally cannot be predicted in advance. Temporal planning therefore tends to use upper bounds on durations, with the explicit or implicit assumption that if an action happens to be executed more quickly, the plan will still succeed. However, this assumption is often false. If we finish cooking too early, the dinner will be cold before everyone is at home and can eat. Simple Temporal Networks with Uncertainty (STNUs) allow us to model such situations. An STNU-based planner must verify that the temporal problems it generates are executable, which is captured by the property of dynamic controllability (DC). If a plan is not dynamically controllable, adding actions cannot restore controllability. Therefore a planner should verify after each action addition whether the plan remains DC, and if not, backtrack. Verifying dynamic controllability of a full STNU is computationally intensive. Therefore, incremental DC verification algorithms are needed.We start by discussing two existing algorithms relevant to the thesis. These are the very first DC verification algorithm called MMV (by Morris, Muscettola and Vidal) and the incremental DC verification algorithm called FastIDC, which is based on MMV.We then show that FastIDC is not sound, sometimes labeling networks as dynamically controllable when they are not. We analyze the algorithm to pinpoint the cause and show how the algorithm can be modified to correctly and efficiently detect uncontrollable networks.In the next part we use insights from this work to re-analyze the MMV algorithm. This algorithm is pseudo-polynomial and was later subsumed by first an O(n 5 ) algorithm and then an O(n 4 ) algorithm. We show that the basic techniques used by MMV can in fact be used to create an O(n 4 ) algorithm for verifying dynamic controllability, with a new termination criterion based on a deeper analysis of MMV. This means that there is now a comparatively easy way of implementing a highly efficient dynamic controllability verification algorithm. From a theoretical viewpoint, understanding MMV is important since it acts as a building block for all subsequent algorithms that verify dynamic controllability. In our analysis we also discuss a change in MMV which reduces the amount of regression needed in the network substantially.In the final part of the thesis we show that the FastIDC method can result in traversing part of a temporal network multiple times, with constraints slowly tightening towards their final values. As a result of our analysis we then present a new algorithm with an improved traversal strategy that avoids this behavior. The new algorithm, EfficientIDC, has a time complexity which is lower than that of FastIDC. We prove that it is sound and complete.This research work ...

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“…For example, "Mechanization of Temporal Knowledge" by Kahn [15] dates back to 1975. There exist many different temporal formalisms.…”

Section: Temporal Formalismsmentioning

confidence: 99%

“…It could have made other choices. [10,15]. When the dispatcher follows this decision by choosing a low T 1 value of 11 in the last step (Figure 2.5d), the bounds propagated to node 3 will be too high: [16,21].…”

Section: Notationmentioning

confidence: 99%

Efficient Temporal Reasoning with Uncertainty

Nilsson¹

2015

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“…They together divide the time line of a domain under consideration. The reference events are to be related using before or after relations [11]. The key wars to which other wars can be associated are chosen as reference events.…”

Section: Reference Events Identificationmentioning

confidence: 99%

“…OWL, a Description Logic based formalism allows reasoning over Allen's temporal relations. Allen's interval relations before and after are contextual and ambiguous [10][11][12] as the temporal distance and event order cannot be determined. Event ordering is defined as the linear sequencing (ordering) of events with respect to the start time of the events in this work.…”

Section: Introductionmentioning

confidence: 99%

Event order generation using Reference Event based qualitative Temporal (REseT) relations in Time Event Ontology

Uma

Aghila²

2014

Open Computer Science

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OWL (Web Ontology Language) is the standard language for Semantic Web and is used in defining ontologies for Web. Temporal event data are ubiquitous in nature. Temporal data can be represented qualitatively using temporal relations in OWL, enabling temporal ordering of events which plays a vital role in task planners. The basic Allen's temporal interval relations can be used to describe relations in OWL. Allen's interval algebra is a well known formalism used to represent and reason the temporal knowledge. In this work, Allen's interval algebra is extended by Reference Event based Temporal (REseT) relations to reduce the ambiguity in the before relation. The extended formalism is used in the representation of relations between time intervals and the viability of ordering of events in ontology is elucidated. This paper proposes a temporal knowledge representation and reasoning based event ordering system which helps in the temporal ordering of events. The advantage of this method is that it does not introduce any additional constructs in OWL and hence the existing reasoning tools and DL based query languages are capable of generating the linear order of events. The system is investigated experimentally using the COW (Correlates of War) dataset and has been evaluated using the Percent_ Similarity measure.

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“…El razonador temporal trata sólo con tareas de gestión e inferencia de tiempos y relaciones temporales, y no precisa ningún conocimiento sobre el dominio de la aplicación; este otro conocimiento será manejado exclusivamente por el resolutor de problemas. Esta idea, basada en una estricta separación entre conocimiento sobre el tiempo y sobre el dominio, se remite a Kahn y Gorry [18] y Allen [3].…”

Section: Introductionunclassified

Temporal Constraint Satisfaction Problems

Marín¹,

Navarrete²

2003

Int. Artif.

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In this paper we deal with the problem of temporal reasoning as a special case of a constraint satisfaction problem. We review several models, either qualitative or metric, for constraint based temporal reasoning and we show the main features of each one, such as expressivity and computational complexity. We also show some recent advances in the field of temporal reasoning with points and durations as temporal objects and finally we suggest some future works.

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Mechanizing temporal knowledge

Cited by 125 publications

References 2 publications

Efficient Temporal Reasoning with Uncertainty

Efficient Temporal Reasoning with Uncertainty

Event order generation using Reference Event based qualitative Temporal (REseT) relations in Time Event Ontology

Temporal Constraint Satisfaction Problems

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