Computer Science – Artificial Intelligence
Scientific paper
2011-10-10
Journal Of Artificial Intelligence Research, Volume 27, pages 617-674, 2006
Computer Science
Artificial Intelligence
Scientific paper
10.1613/jair.2135
In real-life temporal scenarios, uncertainty and preferences are often essential and coexisting aspects. We present a formalism where quantitative temporal constraints with both preferences and uncertainty can be defined. We show how three classical notions of controllability (that is, strong, weak, and dynamic), which have been developed for uncertain temporal problems, can be generalized to handle preferences as well. After defining this general framework, we focus on problems where preferences follow the fuzzy approach, and with properties that assure tractability. For such problems, we propose algorithms to check the presence of the controllability properties. In particular, we show that in such a setting dealing simultaneously with preferences and uncertainty does not increase the complexity of controllability testing. We also develop a dynamic execution algorithm, of polynomial complexity, that produces temporal plans under uncertainty that are optimal with respect to fuzzy preferences.
Rossi Fausto
Venable Kristen Brent
Yorke-Smith Neil
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