Controlling cooperative problem solving in industrial multi-agent systems using joint intentions
Artificial Intelligence
Collaborative plans for complex group action
Artificial Intelligence
Balancing between Reactivity and Deliberation in the ICAGENT Framework
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
Situation Based Strategic Positioning for Coordinating a Team of Homogeneous Agents
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
MAAMAW '92 Selected papers from the 4th European Workshop on on Modelling Autonomous Agents in a Multi-Agent World, Artificial Social Systems
Journal of Artificial Intelligence Research
Deriving multi-agent coordination through filtering strategies
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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Cooperative activity involves collaboration and communication. Through the stages of collaboration, agents may play different roles either for performing domain tasks, or for forming decisions concerning the collaborative activity itself. Collaboration and communication can be enhanced if dependencies between agents' intentions are captured. Role-specification is expected to be a vital factor towards this goal. This is evidenced by roles' importance in many implemented systems. Agents' coordination, plan monitoring and re-planning in these systems rely on contextual information and agents' roles. However, there is not an implemented generic agent architecture that realizes the importance of roles for flexible cooperative activity. This paper shows how the ICagent development framework has been evolved to support cooperative activity through representing and reasoning about multi-role recipes.