Actors: a model of concurrent computation in distributed systems
Actors: a model of concurrent computation in distributed systems
Distributed Artificial Intelligence
Distributed Artificial Intelligence
Coordination languages and their significance
Communications of the ACM
DAI approaches to coordination
Distributed artificial intelligence
Efficient support of location transparency in concurrent object-oriented programming languages
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Concurrent programming for DAI
Multiagent systems
A foundation for actor computation
Journal of Functional Programming
Cyberorgs: a model for resource bounded complex agents
Cyberorgs: a model for resource bounded complex agents
Design and semantics of quantum: a language to control resource consumption in distributed computing
DSL'97 Proceedings of the Conference on Domain-Specific Languages on Conference on Domain-Specific Languages (DSL), 1997
A scalable approach to multi-agent resource acquisition and control
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Distributed Coordination of Massively Multi-Agent Systems
Massively Multi-Agent Technology
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Scalable coordination is a key challenge in deploying massively multi-agent systems. Resource usage is one part of agent behavior which naturally lends itself to abstraction. CyberOrgs is a model for hierarchical coordination of resource usage by multi-agent applications in a network of peer-owned resources. Programming constructs based on CyberOrgs allow resource trade and control reification while maintaining a separation between functional and resource concerns. An operational semantics of CyberOrgs is presented. Expressive power of programming constructs based on CyberOrgs is illustrated with examples. Hierarchical control presents challenges in scalability. However, some types of resource coordination are amenable to efficient implementation using CyberOrgs. Hierarchical control of processor time, for instance, can be implemented scalably by efficiently flattening the hierarchical schedule on the fly. Experimental results demonstrate scalability of the technique. Generalizations of this solution for hierarchical control of processor, network and other computational resources in a distributed system are discussed.