This paper focuses on the rational distribution of task utilities in coalition skill games, which is a restricted form of coalition game, where each service agent has a set of skills and each task agent needs a set of skills in order to be completed. These two types of agents are assumed to be self-interested. Given the task selection strategy of service agents, the utility distribution strategies of task agents play an important role in improving their individual revenues and system total revenue. The problem that needs to be resolved is how to design the task selection strategies of the service agents and the utility distribution strategies of the task agents to make the self-interested decisions improve the system whole performance. However, to the best of our knowledge, this problem has been the topic of very few studies and has not been properly addressed. To address this problem, a task allocation algorithm for self-interested agents in a coalition skill game is proposed, it distributes the utilities of tasks to the needed skills according to the powers of the service agents that possess the corresponding skills. The final simulation results verify the effectiveness of the algorithm.
This paper presents an optimization method about multiple evaluation function with hybrid particle swarm with constraints on the base of an optimization algorithm of hybrid particle swarm, which is used to solve the problem of multi-agent collaboration in the rescue simulation system. The optimization process uses a variety of evaluation function and also calculates the constraint relationship among the evaluation functions on the particle iterative process in order to obtain multi-objective optimization results that meet multiple conditions. The method is suitable for the collaborative problem among a variety of heterogeneous agents, which presents the collaboration among heterogeneous agents through constraints. The method proves to be effective in the practical application of the rescue simulation system.
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