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Extended Automata for Temporal Planning of Interacting Agents

Extended Automata for Temporal Planning of Interacting Agents

Christine Largouët, Omar Krichen, Yulong Zhao
Copyright: © 2017 |Volume: 5 |Issue: 1 |Pages: 19
ISSN: 2166-7241|EISSN: 2166-725X|EISBN13: 9781522515494|DOI: 10.4018/IJMSTR.2017010102
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MLA

Largouët, Christine, et al. "Extended Automata for Temporal Planning of Interacting Agents." IJMSTR vol.5, no.1 2017: pp.30-48. http://doi.org/10.4018/IJMSTR.2017010102

APA

Largouët, C., Krichen, O., & Zhao, Y. (2017). Extended Automata for Temporal Planning of Interacting Agents. International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 5(1), 30-48. http://doi.org/10.4018/IJMSTR.2017010102

Chicago

Largouët, Christine, Omar Krichen, and Yulong Zhao. "Extended Automata for Temporal Planning of Interacting Agents," International Journal of Monitoring and Surveillance Technologies Research (IJMSTR) 5, no.1: 30-48. http://doi.org/10.4018/IJMSTR.2017010102

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Abstract

In this paper, the authors consider the planning problem for a system represented as a set of interacting agents evolving along time according to explicit timing constraints. Given a goal, the planning problem is to find the sequence of actions such that the system reaches the goal state in a limited time and in an optimal manner, assuming actions have a cost. In their approach, the planning problem is based on model-checking and controller synthesis techniques while the goal is defined using temporal logic. Each agent of the system is represented using the formalism of Priced Timed Game Automata (PTGA). PTGA is an extension of Timed Automata that allows the representation of cost on actions and the definition of uncontrollable actions. The authors define a planning algorithm that computes the best strategy to achieve a goal. To experiment their approach, they extend the classical Transport Domain with timing constraints, cost on actions and uncontrollable actions. The planning algorithm is finally presented on a marine ecosystem management problem.

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