In open normative multi-agent communities,an agent is not usually and explicitly given the norms of the host agents.Thus,when it is not able to adapt the communities's norms,it is totally deprived of accessing res...In open normative multi-agent communities,an agent is not usually and explicitly given the norms of the host agents.Thus,when it is not able to adapt the communities's norms,it is totally deprived of accessing resources and services from the host.Such circumstance severely affects its performance resulting in failure to achieve its goal.Consequently,this study attempts to overcome this deficiency by proposing a technique that enables an agent to detect the host's potential norms via self-enforcement and update its norms even in the absence of sanctions from a third-party.The authors called this technique as the potential norms detection technique(PNDT).The PNDT consists of five components: Agent's belief base; observation process; potential norms mining algorithm(PNMA);verification process; and updating process.The authors demonstrate the operation of the PNMA algorithm by testing it on a typical scenario and analyzing the results on several perspectives.The tests' results show that the PNDT performs satisfactorily albeit the success rate depends on the environment variables settings.展开更多
Assessing machine's performance through comparing the same or similar machines is important to implement intelligent maintenance for swarm machine.In this paper,an outlier mining based abnormal machine detection a...Assessing machine's performance through comparing the same or similar machines is important to implement intelligent maintenance for swarm machine.In this paper,an outlier mining based abnormal machine detection algorithm is proposed for this purpose.Firstly,the outlier mining based on clustering is introduced and the definition of cluster-based global outlier factor(CBGOF) is presented.Then the modified swarm intelligence clustering(MSIC) algorithm is suggested and the outlier mining algorithm based on MSIC is proposed.The algorithm can not only cluster machines according to their performance but also detect possible abnormal machines.Finally,a comparison of mobile soccer robots' performance proves the algorithm is feasible and effective.展开更多
文摘In open normative multi-agent communities,an agent is not usually and explicitly given the norms of the host agents.Thus,when it is not able to adapt the communities's norms,it is totally deprived of accessing resources and services from the host.Such circumstance severely affects its performance resulting in failure to achieve its goal.Consequently,this study attempts to overcome this deficiency by proposing a technique that enables an agent to detect the host's potential norms via self-enforcement and update its norms even in the absence of sanctions from a third-party.The authors called this technique as the potential norms detection technique(PNDT).The PNDT consists of five components: Agent's belief base; observation process; potential norms mining algorithm(PNMA);verification process; and updating process.The authors demonstrate the operation of the PNMA algorithm by testing it on a typical scenario and analyzing the results on several perspectives.The tests' results show that the PNDT performs satisfactorily albeit the success rate depends on the environment variables settings.
基金the National Natural Science Foundation of China (No. 50705054)
文摘Assessing machine's performance through comparing the same or similar machines is important to implement intelligent maintenance for swarm machine.In this paper,an outlier mining based abnormal machine detection algorithm is proposed for this purpose.Firstly,the outlier mining based on clustering is introduced and the definition of cluster-based global outlier factor(CBGOF) is presented.Then the modified swarm intelligence clustering(MSIC) algorithm is suggested and the outlier mining algorithm based on MSIC is proposed.The algorithm can not only cluster machines according to their performance but also detect possible abnormal machines.Finally,a comparison of mobile soccer robots' performance proves the algorithm is feasible and effective.
