Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Distributed problem solving and planning
Multiagent systems
Multiagent systems
Coalition structure generation with worst case guarantees
Artificial Intelligence
Fundamenta Informaticae
Organization Self-Design of Distributed Production Systems
IEEE Transactions on Knowledge and Data Engineering
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Cloning for Intelligent Adaptive Information Agents
Revised Papers from the Second Australian Workshop on Distributed Artificial Intelligence: Multi-Agent Systems: Methodologies and Applications
Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents
ECAI '96 Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages
Increasing Resource Utilization and Task Performance by Agent Cloning
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Distributed Constraint Satisfaction Algorithm for Complex Local Problems
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Problem Structure and Subproblem Sharing in Multi-Agent Systems
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Eigenvalues and graph bisection: An average-case analysis
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
A one-shot dynamic coordination algorithm for distributed sensor networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Several interesting practical problems in process control, planning and scheduling can be expressed and solved using the model of constraint satisfaction problems. At least four drawbacks of this classical model directly relate to areas of distribution: complexity, scalability, privacy and robustness. Hence, research on distributed constraint satisfaction problems is a new direction in the area of multi-agent systems. A typical engineering task in distributed constraint satisfaction is the design of the distribution itself. A careful look at this task reveals that the design of distribution is critical to the quality and efficiency of the problem solving process and is itself an optimization problem. In this article we formalize different variants of this configuration problem and prove them to be all at least NP-complete. For solving these problems, we present two local operators, agent melting and agent splitting, that can be combined to allow for an autonomous and dynamic reconfiguration of the organizational structure of the problem-solving agents. We prove sequences of these operators to be sufficient for solving any given configuration problem. We also briefly describe what practical steps are necessary to exploit the rather theoretical result of the proof in realistic applications.