Handling contingency in temporal constraint networks: from consistency to controllabilities

Vidal, Thierry

doi:10.1080/095281399146607

Cited by 177 publications

(226 citation statements)

References 17 publications

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“…Firstly, an STPU is Strongly Controllable (SC) if there is a fixed execution strategy that works in all situations (an observation of all contingent timepoints). Checking strong controllability is in P [14]. Secondly, an STPU is Dynamically Controllable (DC) if there is an online execution strategy that depends only on observed timepoints in the past and that can always be extended to a complete schedule whatever may happen in the future.…”

Section: Given An Stp and A Constraintmentioning

confidence: 99%

“…Controllable implies the agent has a means to execute the timepoints under its control, subject to all constraints. Three notions have been proposed in [14]. Firstly, an STPU is Strongly Controllable (SC) if there is a fixed execution strategy that works in all situations (an observation of all contingent timepoints).…”

Section: Given An Stp and A Constraintmentioning

confidence: 99%

“…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.…”

Section: Motivationmentioning

confidence: 99%

“…To address the lack of flexibility in execution of standard STPs, the Simple Temporal Problem under Uncertainty (STPU) framework [14] divides the timepoints into two classes: executable (or requirement) and contingent (or uncontrollable). The former, as in an STP, are decided by the agent, but the latter are decided by 'Nature': the agent has no control over when the activity will end; it observes rather than executes.…”

Section: Given An Stp and A Constraintmentioning

confidence: 99%

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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: Given An Stp and A Constraintmentioning

confidence: 99%

Section: Given An Stp and A Constraintmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Given An Stp and A Constraintmentioning

confidence: 99%

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Controllability of Soft Temporal Constraint Problems

Rossi

Venable

Yorke-Smith

2004

Principles and Practice of Constraint Programming – CP 2004

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“…In temporal constraint problems with preferences, much work has been done to understand how to best deal with uncontrollable events (Vidal & Fargier, 1999). In fact, three notions of controllability, that is, strong, weak, and dynamic, are usually considered in this setting.…”

Section: Representing Preferences and Coping With Uncertaintymentioning

confidence: 99%

New trends in constraint satisfaction, planning, and scheduling: a survey

Barták

Salido

Rossi

2010

The Knowledge Engineering Review

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During recent years the development of new techniques for constraint satisfaction, planning, and scheduling has received increased attention, and substantial effort has been invested in trying to exploit such techniques to find solutions to real life problems. In this paper, we present a survey on constraint satisfaction, planning and scheduling from the Artificial Intelligence point of view. In particular, we present the main definitions and techniques, and we discuss possible ways of integrating such techniques. We also analyze the role of constraint satisfaction in planning and scheduling, and we hint at some open research issues related to planning, scheduling and constraint satisfaction.

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Reasoning and querying bounds on differences with layered preferences

et al. 2021

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Bounds on differences are widely used in AI to model binary constraints regarding different dimensions, such as time, space, costs, calories, etc. Representing and reasoning with them is an important task in several areas such as knowledge representation, scheduling and planning. Researchers are increasingly focusing on the treatment of fuzzy or probabilistic constraints, to deal with preferences and/or uncertainty. Current approaches to constraints with preferences focus on the evaluation of the optimal (i.e., with highest preference) solutions for the set of constraints and propose a wide range of alternative operators to combine preferences within constraint propagation. However, in decision support tasks, finding a specific (though optimal) solution is not the main goal, but rather it is more important to identify the "space of solutions" (i.e., the minimal network) with their preferences, and to provide users with query answering mechanisms to explore it. We propose the first approach that addresses such a need by (i) supporting user-defined layered scales of preferences (e.g., Low, Medium, High, Very High), (ii) proposing a family of extensions of bounds on differences constraints to deal with such layered preferences, (iii) defining a family of reasoning algorithms to evaluate the minimal network, which is parametric with respect to the basic operations to combine preferences (and the scale of preferences), and (iv) providing suitable query-answering facilities. The properties of the family of approaches are also analyzed.

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Handling contingency in temporal constraint networks: from consistency to controllabilities

Cited by 177 publications

References 17 publications

Controllability of Soft Temporal Constraint Problems

Controllability of Soft Temporal Constraint Problems

New trends in constraint satisfaction, planning, and scheduling: a survey

Reasoning and querying bounds on differences with layered preferences

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