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Autonomous agents coordination: Action languages meet CLP($\mathcal {FD}$) and Linda*

Published online by Cambridge University Press:  24 September 2012

AGOSTINO DOVIER ,
ANDREA FORMISANO  and
ENRICO PONTELLI
AGOSTINO DOVIER
Affiliation:
Dipartimento di Matematica e Informatica, Università di Udine, Udine, Italy (e-mail: dovier@dimi.uniud.it
ANDREA FORMISANO
Affiliation:
Dipartimento di Matematica e Informatica, Università di Perugia, Perugia, Italy (e-mail: formis@dmi.unipg.it
ENRICO PONTELLI
Affiliation:
Department of Computer Science, New Mexico State University, University Park, NM, USA (e-mail: epontell@cs.nmsu.edu
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Abstract

The paper presents a knowledge representation formalism, in the form of a high-level Action Description Language (ADL) for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans. A distributed planning problem is formalized by providing the declarative specifications of the portion of the problem pertaining to a single agent. Each of these specifications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog.

Keywords

Action languagesMulti-agent systemsConstraint logic programming
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 13 , Issue 2: 25th Annual GULP Conference , March 2013 , pp. 149 - 173
Copyright
Copyright © Cambridge University Press 2012

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Footnotes

*

Research partially funded by GNCS-INdAM projects, MUR-PRIN: Innovative and multidisciplinary approaches for constraint and preference reasoning project; NSF grants IIS-0812267 and HRD-0420407; and grants 2009.010.0336 and 2010.011.0403.

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