Probabilistic Approaches in Search
Papers from the AAAI Workshop
Carla Gomes and Toby Walsh, Cochairs
Recently there has been considerable interest in approaches based on randomization, probability, and uncertainty to speed up computation and to model resources more realistically. For example, the performance and robustness of search procedures can often be improved by adding an element of randomness to the search procedures, as in randomized backtrack search and local search. Furthermore, probabilistic methods like Markov decision processes, Monte Carlo sampling, and Bayesian learning are now being used to study and improve the behavior of search procedures. The aim of this workshop is to explore the use of probabilistic methods in the modeling and understanding of search procedures, as well as their role in improving search procedures. This is the third workshop in the series. Previous workshops have been held alongside the AAAI-2000 conference and within the AAAI-01 Fall Symposium.