A multi-agent simulation for intelligence economy

This paper introduces an intelligence economy and information network approach for intelligence economic using a Multi-agent system. While regular intelligent economy structure try to induce a general decision function for a learning task, multi-agent take into account a particular test set and try to simulate those particular example. The paper presents an analysis of why multi-agent is well suited for intelligence economy information management. These theoretical findings are supported by experiments on real-world data collections. The case studies show substantial improvements over inductive methods, especially for small Multi-agent-based computer information system training sets, improving the research activities as well as the economic value associated with such intelligence economy assets. This work also proposes a model for evaluate y efficiently, handling 1,909 examples and more.