Multi-agent systems for data-rich, information-poor environments

The recent development of sensors integrated with memory, power supply and wireless networking capabilities marks a new era in sensor technology, with wide ranging implications for both military and civilian domains. The capability for ubiquitous and distributed sensing has lead to the possibility of data-rich and information-poor environments, where the ability to collect data has overtaken the ability to understand its relevance and importance to the overall system goals. If the benefits of the sensor technology developments are to reach end users, we need to address two key questions. First, what data should be gathered given resource constraints like limited sensor battery power? Second, what information should be shared with humans, and between humans, given their cognitive constraints? This thesis focuses on development of agent-based information management algorithms and architectures that can deal with the massive amounts of data generated, without overloading the human operators. Intelligent agent technology with its emphasis on autonomy provides a valuable paradigm for this problem. This thesis mainly focuses on designing and building a market-based resource allocation architecture for sensor management in distributed sensor networks. A second domain, supply chain management, examines the question of what information should be shared, and involved development of a collaborative sense-making application. A market-based agent design is proposed for the distributed sensor management problem, where the different system units are regarded as various market entities. This approach has the ability to create a comprehensive sensor management paradigm that can optimally distribute non-commensurate sensor network resources (e.g., sensor attention, battery power, and transmission capacity) among the distributed consumers, operating in a co-operative or semi-cooperative environment. A team-based agent design is proposed for collaborative sense-making in a multi-echelon supply chain. The various supply-chain entities, including the data generating entities (like RF sensors) are treated as team members with specific roles in a multi-agent team, based on the multi-agent team framework, Collaborative Agents for Team work (CAST). This approach holds the promise of addressing the information needs of the individual agents without the causing the problems of information overload, using the CAST based pro-active information and knowledge delivery policies.