Incidence calculus: A mechanism for probabilistic reasoning
Journal of Automated Reasoning
The contract net protocol: high-level communication and control in a distributed problem solver
Distributed Artificial Intelligence
Brokering and matchmaking for coordination of agent societies: a survey
Coordination of Internet agents
Communication and Concurrency
An improvement to matchmaking algorithms for middle agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Semantic Matching of Web Services Capabilities
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Quality driven web services composition
WWW '03 Proceedings of the 12th international conference on World Wide Web
Languages, Methodologies and Development Tools for Multi-Agent Systems
Discovery and Uncertainty in Semantic Web Services
Uncertainty Reasoning for the Semantic Web I
Models of Interaction as a Grounding for Peer to Peer Knowledge Sharing
Advances in Web Semantics I
Protocol synthesis with dialogue structure theory
ArgMAS'05 Proceedings of the Second international conference on Argumentation in Multi-Agent Systems
Selecting web services statistically
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
Lightweight coordination calculus for agent systems: retrospective and prospective
DALT'11 Proceedings of the 9th international conference on Declarative Agent Languages and Technologies
Hi-index | 0.00 |
Matchmaking will be an important component of future agent and agent-like systems, such as the semantic web. Most research on matchmaking has been directed toward sophisticated matching of client requirements with provider capabilities based on capability descriptions. This is a vital mechanism for conducting matchmaking, but ignores the likelihood that in practice, and for various reasons, capability descriptions will not fully characterise the interaction behaviour of agents. This problem is further compounded in systems with many interacting agents, all of which have idiosyncrasies. As in everyday life, some groupings of agents will be more effective than others, regardless of their individual competencies or suitability to the task. The quality of the interaction between agents is a crucial factor. Using the incidence calculus and the lightweight coördination calculus, we show that we can easily implement matchmaking agents that will learn from experience how to select those groups known to inter-operate well for particular tasks.