Intelligent Information Processing II: IFIP TC12/WG12.3 by Xinjun Mao, Jiajia Chen (auth.), Zhongzhi Shi, Qing He

By Xinjun Mao, Jiajia Chen (auth.), Zhongzhi Shi, Qing He (eds.)

This ebook is predicated on IIP2004, which supplies a discussion board for engineers and scientists in academia, college and to offer their most up-to-date examine findings in any element of clever details processing. Papers on intelligence technological know-how, clever brokers, computing device studying, and autonomic computing, in addition to papers that spotlight bioinformatics, e-commerce, and company intelligence have been offered. IIP2004 meets the desires of a big and numerous community.

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Sample text

In classical environments, an intelligent agent is an active object with the ability to perceive, reason and act. We assume that an agent has explicitly represented knowledge and a mechanism for operating on or drawing inferences from its knowledge. We also assume that an agent has the ability to communicate. In a distributed computing system, intelligent agent has been introduced to communicate with each other in order to achieve their goals. Here we propose a new environment model to study knowledge base in mobile environments.

E. it is high profit, low risk and low cost to invest sharel on MH3. The MH4 is slept at the moment the information is retrieved from it. rrri ci MiiJ, ,a ) ^ ^ p) ) ^ Mijol For agent a^ssi ? risk{share\) is a belief in output of a^^^^ , while ~:risk{share\) is a belief in output of a^^2 » they are inconsistent. Here we assume selection Sanction Sp takes positive atom as higher preference for investment risk, therefore mA:(5/zörrel) will become the input of aj^ssx. We have knowledge input as below: KnowledgeInput{a Mssxy)"" {p^ofit(sharel),risk(sharel\^cost{sharel)] We can see that different knowledge input is derived with considering selection function in specific problem domain, therefore different answer set is derived for decision making due to selection function.

Message output is information output for communication purpose, this information will not cause any influence to the other agent's local knowledge base, while knowledge output is the information that produced by the agent's local knowledge base and will have impact for the other agent's knowledge base. Definition 3: We define knowledge input and output in MLPMAS systems on MH level as follows. e. e. knowledge output is an answer set of the program formed by the local logic program of agent aj^^ with extending of knowledge input from Y for agent aj^i^.

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