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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 30 / No. 1: Thirtieth AAAI Conference On Artificial Intelligence

Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies

March 8, 2023

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Authors

Alessandro Cimatti

Fondazione Bruno Kessler


Andrea Micheli

Fondazione Bruno Kessler


Marco Roveri

Fondazione Bruno Kessler


DOI:

10.1609/aaai.v30i1.10401


Abstract:

The Temporal Network with Uncertainty (TNU) modeling framework is used to represent temporal knowledge in presence of qualitative temporal uncertainty. Dynamic Controllability (DC) is the problem of deciding the existence of a strategy for scheduling the controllable time points of the network observing past happenings only. In this paper, we address the DC problem for a very general class of TNU, namely Disjunctive Temporal Network with Uncertainty. We make the following contributions. First, we define strategies in the form of an executable language; second, we propose the first decision procedure to check whether a given strategy is a solution for the DC problem; third we present an efficient algorithm for strategy synthesis based on techniques derived from Timed Games and Satisfiability Modulo Theory. The experimental evaluation shows that the approach is superior to the state-of-the-art.

Topics: AAAI

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Alessandro Cimatti|| Andrea Micheli|| Marco Roveri Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies Proceedings of the AAAI Conference on Artificial Intelligence, 30 (2016) .

Alessandro Cimatti|| Andrea Micheli|| Marco Roveri Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies AAAI 2016, .

Alessandro Cimatti|| Andrea Micheli|| Marco Roveri (2016). Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies. Proceedings of the AAAI Conference on Artificial Intelligence, 30, .

Alessandro Cimatti|| Andrea Micheli|| Marco Roveri. Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies. Proceedings of the AAAI Conference on Artificial Intelligence, 30 2016 p..

Alessandro Cimatti|| Andrea Micheli|| Marco Roveri. 2016. Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies. "Proceedings of the AAAI Conference on Artificial Intelligence, 30". .

Alessandro Cimatti|| Andrea Micheli|| Marco Roveri. (2016) "Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies", Proceedings of the AAAI Conference on Artificial Intelligence, 30, p.

Alessandro Cimatti|| Andrea Micheli|| Marco Roveri, "Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies", AAAI, p., 2016.

Alessandro Cimatti|| Andrea Micheli|| Marco Roveri. "Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies". Proceedings of the AAAI Conference on Artificial Intelligence, 30, 2016, p..

Alessandro Cimatti|| Andrea Micheli|| Marco Roveri. "Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies". Proceedings of the AAAI Conference on Artificial Intelligence, 30, (2016): .

Alessandro Cimatti|| Andrea Micheli|| Marco Roveri. Dynamic Controllability of Disjunctive Temporal Networks: Validation and Synthesis of Executable Strategies. AAAI[Internet]. 2016[cited 2023]; .


ISSN: 2374-3468


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