Abstract
In this article, we study a problem of dynamic task allocation with multiple agent responsibilities in distributed multi-agent systems. Agents in the research have two responsibilities, communication and task execution. Movements in agent task execution bring changes to the system network structure, which will affect the communication. Thus, agents need to be autonomous on communication network reconstruction for good performance on task execution. First, we analyze the relationships between the two responsibilities of agents. Then, we design a multi-responsibility–oriented coalition formation framework for dynamic task allocation with two parts, namely, task execution and self-adaptation communication. For the former part, we integrate our formerly proposed algorithm in the framework for task execution coalition formation. For the latter part, we develop a constrained Bayesian overlapping coalition game model to formulate the communication network. A task-allocation efficiency–oriented communication coalition utility function is defined to optimize a coalition structure for the constrained Bayesian overlapping coalition game model. Considering the geographical location dependence between the two responsibilities, we define constrained agent strategies to map agent strategies to potential location choices. Based on the abovementioned design, we propose a distributed location pruning self-adaptive algorithm for the constrained Bayesian overlapping coalition formation. Finally, we test the performance of our framework, multi-responsibility–oriented coalition formation framework, with simulation experiments. Experimental results demonstrate that the multi-responsibility oriented coalition formation framework performs better than the other two distributed algorithms on task completion rate (by over 9.4% and over 65% on average, respectively).
Highlights
Nowadays, distributed multi-agent systems (DMASs) are employed in practice growingly for dealing with sundry tasks, for example, disaster rescue,[1] military operations,[2,3,4,5] and distributed sensor networks.[6]
Since the problem is formulated based on the coalition game, to ensure that the calculation converges to a Bayesian equilibrium, we make some rules on agent location self-adaptation
Rule 1 ensures that the algorithm we proposed for selfadaptation communication formation in “Algorithms in multi-responsibility–oriented coalition formation framework (MOCFF)” section will converge to a Bayesian equilibrium
Summary
Nowadays, distributed multi-agent systems (DMASs) are employed in practice growingly for dealing with sundry tasks, for example, disaster rescue,[1] military operations,[2,3,4,5] and distributed sensor networks.[6]. Our main contribution in this article is a multi-responsibility–oriented coalition formation framework (MOCFF) for dynamic task allocation with multiple agent responsibilities. We firstly introduce some related system assumptions and preliminaries of this study Based on those assumptions and definitions, we analyze the relationship between the two agent responsibilities and the relationship between the two kinds of coalitions. Overlapping is allowed in CCs. Agents obtain information to form task execution coalition via their social network, task execution coalition relies on CC. Considering the issues discussed above, an ideal local social network should be capable of transmitting task announcements in a range as wide as possible to form task-executing coalitions in time. The performance of the social network could be improved based on the following considerations
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