Query-answering in fuzzy temporal constraint networks

Vila, Lluís; Godo, Lluı́s

doi:10.1109/fuzzy.1995.409658

Cited by 17 publications

(15 citation statements)

References 11 publications

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“…The Simple Temporal Problem with Uncertainty, which the STPPU builds on, introduces uncertainty due to contingent events. Besides STPUs, uncertainty has been introduced into the TCP with possibilistic reasoning; [16] is one framework for Fuzzy Temporal Constraint Problems. In principle, vagueness due to soft preferences can also be accommodated with fuzzy constraints.…”

Section: Related Workmentioning

confidence: 99%

Controllability of Soft Temporal Constraint Problems

Rossi

Venable

Yorke-Smith

2004

Principles and Practice of Constraint Programming – CP 2004

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Abstract. In real-life temporal scenarios, uncertainty and preferences are often essential, coexisting aspects. We present a formalism where temporal constraints with both preferences and uncertainty can be defined. We show how three classical notions of controllability (strong, weak and dynamic), which have been developed for uncertain temporal problems, can be generalised to handle also preferences. We then propose algorithms that check the presence of these properties and we prove that, in general, dealing simultaneously with preferences and uncertainty does not increase the complexity beyond that of the separate cases. In particular, we develop a dynamic execution algorithm, of polynomial complexity, that produces plans under uncertainty that are optimal w.r.t. preference. MotivationResearch on temporal reasoning, once exposed to the difficulties of real-life problems, can be found lacking both expressiveness and flexibility. To address the lack of expressiveness, preferences can be added to the temporal framework; to address the lack of flexibility to contingency, reasoning about uncertainty can be added. In this paper we introduce a framework to handle both preferences and uncertainty in temporal problems. This is done by merging the two pre-existing models of Simple Temporal Problems with Preferences (STPPs) [7] and Simple Temporal Problems with Uncertainty (STPUs). [14]. We adopt the notion of controllability of STPUs, to be used instead of consistency because of the presence of uncertainty, and we adapt it to handle preferences.The proposed framework, Simple Temporal Problems with Preferences and Uncertainty (STPPUs), represents temporal problems with preferences and uncertainty via a set of variables, which represent the starting or ending times of events (which may be controllable or not), and a set of soft temporal constraints over the variables. Each constraint includes an interval containing the allowed durations of the event or the allowed interleaving times between events, and a preference function associating each element of the interval with a value corresponding to how much its preferred. Such soft constraints can be defined on both controllable and uncontrollable events.Examples of real-life problems with temporal constraints, preferences, and uncertainty can easily be found in several application domains [2,9]. Here we describe in detail one such problem, arising in an aerospace application domain. The problem refers to planning for fleets of Earth Observing Satellites (EOS) [5]. This planning problem involves multiple satellites, hundreds of requests, constraints on when and how to service each request, and multiple resources. Scientists place requests to receive earth images from space. After the image data is acquired by an EOS, it can either be downlinked in real time or recorded on board for playback at a later time. Ground stations or other

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Section: Related Workmentioning

confidence: 99%

Controllability of Soft Temporal Constraint Problems

Rossi

Venable

Yorke-Smith

2004

Principles and Practice of Constraint Programming – CP 2004

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“…TCNs dealing with fuzzy temporal constraints are known as Fuzzy Temporal Constraint Networks (FTCN) [36,37,38]. Several implementations of such models exist, but for the sake of conciseness, we will focus on the one implemented in the FuzzyTIME engine developed by the AIKE group of the University of Murcia [36,38].…”

Section: Temporal Representation and Reasoningmentioning

confidence: 99%

Multilingual generation of uncertain temporal expressions from data: A study of a possibilistic formalism and its consistency with human subjective evaluations

Gatt

Portet

2016

Fuzzy Sets and Systems

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In NLG systems, temporal uncertainty in raw data can hamper the inference of temporal and causal relationships between events and thus impact the quality of the generated texts. In this paper, we introduce a framework to represent and reason with temporal uncertainty based on possibility theory and propose a model that uses the outcomes of such temporal reasoning to select linguistic expressions to convey uncertainty to the reader. Our model is based on Fuzzy Temporal Constraint Networks (FTCN) and our work is based on the assumption that uncertainty should be communicated to an end user. The model we propose is grounded in experimental data from three languages. We present a large-scale empirical study that investigates the conditions that influence human subjective uncertainty in reasoning about temporal relations. Based on this, we also construct a classifier to select expressions to convey uncertainty, based on possibility and necessity values. We then present an evaluation which shows that the predictions of the FTCN model correlate well with human subjective uncertainty in different scenarios. An evaluation of our temporal expressions classifier also suggests good results, compared to human selection of linguistic expressions, as compared to baseline models.

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“…Alternatively, Dubois, HadjAli, and Prade (Dubois et al, 2003b) propose fuzziness in temporal reasoning to deal with uncertainty. They bring in Fuzzy Allen Relations, and as in Vila and Godo (Vila & Godo, 1994), consider on available information characterized by imprecision, and vagueness. Focusing on the notions of consistency and entailment, the authors ignore preferences and controllability.…”

Section: Related Workmentioning

confidence: 99%

“…Simple Temporal Problems (STPs) (Dechter et al, 1991) have traditionally been limited to hard crisp constraint network lacking expressivity and flexibility. Fuzzy temporal constraint networks were then introduced (Vila & Godo, 1994) proposing a propositional temporal language based on fuzzy temporal constraints to express knowledge and, imprecision as a single type of uncertainty. It provides an inference mechanism involving rules to reason on fuzzy temporal constraints, and ultimately specifying the tightest constraints possible on event duration.…”

Section: Related Workmentioning

confidence: 99%