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Socially-Centred Design for Socially Embedded Multi-Agent Systems
Toru Ishida (Japan)
Given the need to realize ubiquitous/pervasive computing, we must be able to control information agents so that they can coexist with humans in the real world. To realize large-scale socially embedded multiagent systems, we propose a new system design methodology towards society-centered design. We have already developed the scenario description language Q, which describes interaction protocols that link agents to society. Using the virtual space called FreeWalk, wherein agents behave under given Q scenarios, we explain each step of society-centered design. The process consists of participatory simulation, where agents and human-controlled avatars coexist in virtual space to jointly perform simulations, and augmented experiment, where an experiment is performed in real space by human subjects, scenario controlled agents, and human extras. As an application of society-centered design, we are working on mega navigation, where millions of humans and socially embedded agents collaborate for developing services for traffic control, crisis management and large-scale event navigation.
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Agent-Based Distributed Data Mining: Current Pleasures, and Future Directions
Hillol Kargupta (USA)
Advances in computing and communication over wired and wireless networks have resulted in many pervasive distributed computing environments. The Internet, intranets, local area networks, ad hoc wireless networks, and sensor networks are some examples. These environments often come with different distributed sources of data and computation. Mining in such environments naturally calls for proper utilization of these distributed resources. However, most off-the-shelf data mining systems are designed to work as a monolithic centralized application which usually do not scale up in large distributed applications. The field of Distributed Data Mining (DDM) offers an alternate choice. It pays careful attention to the distributed resources of data, computing, communication, and human factors in order to use them in an optimal fashion. This presentation will first offer a brief overview of the current state-of-the-art DDM Technology. It will then point toward a synergy between multi-agent systems and DDM which may lead to very large scale data intensive problem solving capabilities. It will particularly discuss a few applications in the sensor network and security domains where such a merger particularly appears to be promising.
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