Learning for Search
Papers from the AAAI Workshop
Wheeler Ruml and Frank Hutter, Cochairs
Heuristic search is among the most widely used techniques in AI. In its different varieties, such as tree-based search and local search, it provides the core engine for applications as diverse as planning, parsing, and protein folding. One of the most promising avenues for developing improved search techniques is to use some kind of algorithmic component that learns from experience. Many disparate techniques have arisen in recent years that exploit learning to improve search and problem-solving. These techniques can be off-line or on-line, based on hard constraints or probabilistic biases, and applied to tree-structured or local search.
This workshop aims to bring together researchers and practitioners from the various subcommunities where such methods have arisen in order to learn from each other, develop common understandings, and inspire new algorithms and approaches.