Authors:
Daniel Fišer
and
Antonín Komenda
Affiliation:
Czech Technical University, Czech Republic
Keyword(s):
Multi-Agent Planning, Finite-Domain Representation, State Invariants, Mutex Groups.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Planning and Scheduling
;
Simulation and Modeling
;
Software Engineering
;
Symbolic Systems
Abstract:
Planning tasks for the distributed multi-agent planning in deterministic environments are described in highly
expressive, but lifted, languages, similar to classical planning. On the one hand, these languages allow for the
compact representation of exponentially large planning problems. On the other hand, the solvers using such
languages need efficient grounding methods to translate the high-level description to a low-level representation
using facts or atomic values. Although there exist ad-hoc implementations of the grounding for the multi-agent
planning, there is no general scheme usable by all multi-agent planners. In this work, we propose such a
scheme combining centralized processes of the grounding and the inference of mutex groups. Both processes
are needed for the translation of planning tasks from the Multi-agent Planning Description Language (MA-PDDL)
to the finite domain representation. We experimentally show a space reduction of the multi-agent finite
domain rep
resentation in contrast to the binary representation on the common benchmark set.
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