This tutorial will be presented by three researchers in the heuristic search community:
||Ariel Felner is a Professor of Computer Science at Ben-Gurion University of the
Negev, Israel. He is now the vice chair of the department of Software and
Information Systems Engineering. His research area includes all aspects of
heuristic search such as theoretical foundations, new search algorithms, the
study and development of heuristics and applying all these to different domains
and settings. He also has special interest in historical and pedagogical
aspects of search algorithms. In addition, he has been working on the problem
of multi-agent pathfinding for a number of years.
Sven Koenig is a professor in computer science at the University of Southern California. Most of his research centers around techniques for decision making (planning and learning) that enable single situated agents (such as robots or decision-support systems) and teams of agents to act intelligently in their environments and exhibit goal-directed behavior in real-time, even if they have only incomplete knowledge of their environment, imperfect abilities to manipulate it, limited or noisy perception or insufficient reasoning speed. Additional information about Sven can be found on his webpages: idm-lab.org|
|Nathan Sturtevant is an Amii fellow and full professor at the University of Alberta. His research looks at heuristic and combinatorial search for single and multiple agents including bidirectional search, cooperative search, large-scale and parallel search, search for game design, heuristic learning, automated abstraction for building heuristics, refinement search, and inconsistent heuristics. Particular applications of his work include pathfinding and planning in memory-constrained real-time environments (e.g. commercial video games) as well as algorithms for building and using memory-based heuristics via large-scale search. He is also interested in theoretical and practical issues in games with more than two players, including opponent modeling, learning, and imperfect information.