Search Techniques for Problem Solving Under Uncertainty and Incomplete Information
Papers from the AAAI Spring Symposium
Weixiong Zhang and Sven Koenig,Cochairs
To build practical AI systems, one has to address issues related to uncertainty and incomplete information, which can result from various sources, including actuator and sensor noise, reasoning with approximate models, limited communication bandwidth, and insufficient domain understanding. This symposium focused on the selection of search strategies for problem solving under uncertainty and incomplete information, where the large number of contingencies can create large search spaces. Using appropriate search strategies can significantly increase system performance by exploiting problem-specific knowledge and restricting the search to the right regions of the search spaces to find satisfactory solutions quickly.