As our computational environments become more complex and life-like, adaptive techniques will mature and become integrated into the core of computer science. In the other direction of the bridge, I advocate the use of efficient and rigorous computational tools to test new models and theories about natural systems. I am beginning to build the pillars of my bridge around some of the questions that are already making the two sides visible from each other. The ideal agents would have to capture regularities at multiple spatial and temporal scales, identify those environmental features that best correlate with their performance, and internalize such features into their behaviors based only on local context. Individual-based learning algorithms are limited in their capability to capture global features and cover heterogeneous domains.
Genetic algorithms are prone to coarseness of scale and premature convergence.
I am extending and combining both approaches into a common algorithmic framework that overcomes these limitations by allowing single agents and populations to integrate environmental signal over different time scales. From Nature to Technology One of the exciting challenges brought about by the explosive development of the Internet is the problem of helping users cope with the vast amounts of unorganized, distributed information on the Web. There is a real need for tools that allow ,users to quickly locate the documents they need. Further, users need personalized tools that can refine their requests based on context and adapt to their changing, long- standing queries and profiles. Part of my research focuses on the application of ecologically-inspired adaptive agents to information discovery, retrieval, and filtering in distributed environments such as the Web.