本文選題：多agent系統(tǒng) + 聯(lián)盟形成　； 參考：《控制與決策》2017年04期

【摘要】：僅采用任務(wù)性?xún)r(jià)比作為多智能體任務(wù)分配過(guò)程中的任務(wù)選擇標(biāo)準(zhǔn),會(huì)產(chǎn)生時(shí)間消耗大、資源利用低等問(wèn)題.為此,綜合任務(wù)性?xún)r(jià)比和智能體資源的特點(diǎn),提出了多任務(wù)準(zhǔn)備度的概念.根據(jù)多智能體任務(wù)分配過(guò)程的收斂性和時(shí)效性,采用Learning Automata算法動(dòng)態(tài)調(diào)整任務(wù)準(zhǔn)備度各項(xiàng)的權(quán)重;進(jìn)而利用該方法模擬解決了低、中、高3種任務(wù)需求下多智能體任務(wù)分配問(wèn)題.仿真實(shí)驗(yàn)結(jié)果驗(yàn)證了所提出方法的有效性,資源冗余可至少減少20%.
[Abstract]:Only using the cost / performance ratio of the task as the task selection criterion in the task assignment process of multi-agent will lead to the problems of large time consumption and low utilization of resources. In this paper, the concept of multi-task readiness is put forward by synthesizing the characteristics of cost performance and agent resources. According to the convergence and timeliness of multi-agent task assignment process, Learning Automata algorithm is used to dynamically adjust the weight of task readiness, and then the multi-agent task assignment problem with low, medium and high task requirements is solved by using this method. The simulation results show that the proposed method is effective, and the resource redundancy can be reduced by at least 20%.
【作者單位】： 北京理工大學(xué)自動(dòng)化學(xué)院;
【基金】：國(guó)家基金委創(chuàng)新研究群體項(xiàng)目(61321002);國(guó)家基金委重大國(guó)際合作項(xiàng)目(61120106010) 國(guó)家自然科學(xué)基金項(xiàng)目(61573062)
【分類(lèi)號(hào)】：TP18

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本文編號(hào)：1901316