Constraint-based protocols for distributed problem solving

AbstractDistributed Problem Solving (DPS) approaches decompose problems into subproblems to be solved by interacting, cooperative software agents. Thus, DPS is suitable for solving problems characterized by many interdependencies among subproblems in the context of parallel and distributed architectures. Concurrent Constraint Programming (CCP) provides a powerful execution framework for DPS where constraints define local problem solving and the exchange of information among agents declaratively. 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 problems and we show how the framework applies to simple yet generalizable examples

Computational Models for Information Reuse

... In this paper, we define a formal framework suitable for the study of information reuse from the point of view of concurrent systems. The main result of our work is in the identification of two distinct morphological features of computational domains, namely recursively replicated structures and structure copying. These features induce two different forms of information reuse that can be optimized, respectively, by solipsistic agents with large local memory and by large bandwidth networks of collaborative agent

Tuning Constraint-Based Communication in Distributed Problem Solving

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 problem solving, constraint-based knowledge brokers, cooperative agen..