基于可能回答集程序的多Agent信念协调

多Agent系统(Multi-Agent System,MAS)是人工智能领域的一个非常活跃的研究方向。在多Agent系统中,由于Agent之间信念的差异,会不可避免地造成行动冲突。Sakama等提出的严格协调方法只适用于各Agent之间有共同信念的情境,当不存在共同信念时,此协调方法无解。针对该问题,文中提出了一种基于可能回答集程序(Possibilistic Answer Set Programming,PASP)的信念协调方法。首先,针对各Agent的不同信念集,基于加权定量的方法计算PASP的回答集相对Agent信念的满足度,以此来弱化某些信念,并且引入缺省决策理论推理得到Agent信念协调的一致解。然后,根据一致解建立一致的协调程序,将其作为Agent共同认同的背景知识库。最后,以dlv求解器为基础实现了多Agent信念协调算法,使Agent之间可以自主完成信念协调。文中以旅游推荐系统为例,说明该算法能够打破严格协调方法的局限,有效解决各Agent之间无共同信念时的协调问题。

Belief Coordination for Multi-agent System Based on Possibilistic Answer Set Programming

Multi-agent system MAS is a very active research direction in the field of artificial intelligence.In multi-agent systems,action conflicts will inevitably occur due to the difference in beliefs between agents.The rigorous coordination method proposed by Sakama et al.is only applicable to situations where there is a common belief among agents.When there is no common belief,this coordination method has no solution.In order to solve this problem,this paper proposed a belief coordination method based on possiblistic answer set programming(PASP).Firstly,according to different belief sets of agents,the weighted quantitative method is used to calculate the satisfaction degree of PASP’s answer set relative to Agent’s belief,so as to weaken some beliefs,and the default decision theory is introduced to deduce the consis-tent solution of Agent’s belief coordination.Then,a consistent coordination program is constructed according to the consistent solution,which serves as the background knowledge base commonly recognized by agents.Finally,the multi-agent belief coordination algorithm is implemented to enable the belief coordination among agents to be completed auto-nomously based on the DLV solver.The example of tourism recommendation system shows that this algorithm can break the limitations of rigorous coordination method and effectively solve the coordination problem when there is no common belief among all agents.