Distributed Problem Solving (DPS) decomposes problems into subproblems to be solved by interacting, cooperative software agents. Thus, DPS is suitable for modeling, in the context of parallel and distributed architectures, the solving of problems characterized by many inter-dependencies among subproblems. Concurrent Constraint Programming (CCP) provides a powerful execution framework for DPS, where constraints can declaratively implement both local problem solving as well as exchange of information, and hence DPS, among agents. To optimize DPS, the protocol for constraint communication must be tuned to the specific kind of DPS problem and the characteristics of the underlying system architecture. In this paper, we provide a formal framework for modeling different options and we show how it applies to concrete, generalizable examples. Key words: constraint propagation, distributed artificial intelligence, distributed problemsolving, constraint-based knowledge brokers, cooperative agents, protocols.

ARCELLI FONTANA, F., Borghoff, U., Formato, F., Pareschi, R. (1995). Tuning Costraint-Based Communication in Distributed Problem Solving. In Proc. of the 1st Intl. Workshop on Concurrent Constraint Programming (CCP'95).

Tuning Costraint-Based Communication in Distributed Problem Solving

ARCELLI FONTANA, FRANCESCA;
1995

Abstract

Distributed Problem Solving (DPS) decomposes problems into subproblems to be solved by interacting, cooperative software agents. Thus, DPS is suitable for modeling, in the context of parallel and distributed architectures, the solving of problems characterized by many inter-dependencies among subproblems. Concurrent Constraint Programming (CCP) provides a powerful execution framework for DPS, where constraints can declaratively implement both local problem solving as well as exchange of information, and hence DPS, among agents. To optimize DPS, the protocol for constraint communication must be tuned to the specific kind of DPS problem and the characteristics of the underlying system architecture. In this paper, we provide a formal framework for modeling different options and we show how it applies to concrete, generalizable examples. Key words: constraint propagation, distributed artificial intelligence, distributed problemsolving, constraint-based knowledge brokers, cooperative agents, protocols.
paper
distributed problem solving; constraint-based computation; CCP programming;
English
Workshop on Concurrent Constraint Programming (CCP'95)
1995/200
ARCELLI FONTANA, F., Borghoff, U., Formato, F., Pareschi, R. (1995). Tuning Costraint-Based Communication in Distributed Problem Solving. In Proc. of the 1st Intl. Workshop on Concurrent Constraint Programming (CCP'95).
ARCELLI FONTANA, F; Borghoff, U; Formato, F; Pareschi, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/38410
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