Artificial Intelligence
Coalition formation among rational information agents
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
Behavior of Agents Based on Mental States
ICOIN '98 Proceedings of the 13th International Conference on Information Networking
Task Allocation: A Group Self-Design Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Task allocation via coalition formation among autonomous agents
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Coalition formation among bounded rational agents
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Methods for task allocation via agent coalition formation
Artificial Intelligence
A kernel-oriented model for coalition-formation in general environments: implementation and results
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Dynamic Behavior of Multiagents with Subjective Cooperative Relations
ICOIN '02 Revised Papers from the International Conference on Information Networking, Wireless Communications Technologies and Network Applications-Part II
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In this paper, we propose a method of coalition formation for assigning tasks to appropriate agents to improve the efficiency of multiagent systems. To form a coalition, we introduce subjective information to agents, which are the internal information of the agents. The subjective information reflect the agents' cooperative behavior of the past. Next, we introduce loose coalition, a concept of a coalition of agents based on the subjective information. Using the agents' sense of values defined by their subjective information, each agent can give priority to the loose coalitions to ask for the working status or to assign tasks. Thus loose coalitions with higher priority will be better cooperating candidates. Furthermore, loose coalitions enable agents to collect information (e.g. busyness of loose coalitions) for task assignment efficiently. Therefore, the agents on the system can decide its behavior properly, depending on the current status of the system, and thus the efficiency of the system can be improved. Then, we observe dynamic properties of system under several settings of agents to derive a guideline for designing effective multiagent systems based on loose coalitions.