Insurance services in multi-agent systems

In a multi-agent environment, there is often the need for an agent to cooperate with others so as to ensure that a given task is achieved timely and cost-effectively. Present agent systems currently maximizes this through mechanisms such as trust and risk assessments. In this paper, we extend this mechanism by introducing the concept of insurance, in which the insurance agents act as a bridge between agents who require resources from others. Unlike traditional systems, agents purchase insurance so as to guarantee to have the requested resources during the task execution time and thus minimize the risk in task failure. The novelty of this proposal is that it ensures agents continuously to exchange resources and to seek maximum expected utility in a dynamic environment at the same time. Our experimental results confirm the feasibility of our approach.

Integrating insurance services, trust and risk mechanisms into multi-agent systems

In multi-agent systems, there is often the need for an agent to cooperate with others so as to ensure that a given task is achieved timely and cost effectively. Currently multi-agent systems maximize this through mechanisms such as coalition formation, trust and risk assessments, etc. In this paper, we incorporate the concept of insurance with trust and risk mechanisms in multi-agent systems. The novelty of this proposal is that it ensures continuous sharing of resources while encouraging expected utility to be maximized in a dynamic environment. Our experimental results confirm the feasibility of our approach.

Buying and selling with insurance in open multi-agent marketplace

In this paper, we incorporate the insurance concept in buying and selling model for agents to trade in the open multi-agent marketplace. During buying, agents purchase insurance as a method to search for potential sellers and select their partners based on the information provided by insurance agents. During selling, agents purchase insurance as a method to protect themselves against potential risk. The insurance concept greatly simplifies the trading procedure in the open marketplace. The novelty of this proposal is that it ensures a dynamic trading environment while agents continue to seek maximum utility and being fully protected by insurance. Our experimental results confirm the feasibility of our approach.

Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data

The approaches proposed in the past for discovering sequential patterns mainly focused on single sequential data. In the real world, however, some sequential patterns hide their essences among multi-sequential event data. It has been noted that knowledge discovery with either user-specified constraints, or templates, or skeletons is receiving wide attention because it is more efficient and avoids the tedious selection of useful patterns from the mass-produced results. In this paper, a novel pattern in multi-sequential event data that are correlated and its mining approach are presented. We call this pattern sequential causal pattern. A group of skeletons of sequential causal patterns, which may be specified by the user or generated by the program, are verified or mined by embedding them into the mining engine. Experiments show that this method, when applied to discovering the occurring regularities of a crop pest in a region, is successful in mining sequential causal patterns with user-specified skeletons in multi-sequential event data.