Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Presence and absence of pathology on game trees
Advances in computer chess
Artificial Intelligence
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Machine Discovery of Effective Admissible Heuristics
Machine Learning
Performance of Lookahead Control Policies in the Face of Abstractions and Approximations
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Recent progress in understanding minimax search
ACM '83 Proceedings of the 1983 annual conference on Computers : Extending the human resource
Recursive random games: a probabilistic model for perfect information games
Recursive random games: a probabilistic model for perfect information games
Finding optimal solutions to the twenty-four puzzle
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Pessimistic Heuristics Beat Optimistic Ones in Real-Time Search
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Thinking Too Much: Pathology in Pathfinding
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Learning in real-time search: a unifying framework
Journal of Artificial Intelligence Research
Dynamic control in real-time heuristic search
Journal of Artificial Intelligence Research
When is it better not to look ahead?
Artificial Intelligence
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Admissible and consistent heuristic functions are usually preferred in single-agent heuristic search as they guarantee optimal solutions with complete search methods such as A* and IDA*. Larger problems, however, frequently make a complete search intractable due to space and/or time limitations. In particular, a path-planning agent in a real-time strategy game may need to take an action before its complete search has the time to finish. In such cases, incomplete search techniques (such as RTA*, SRTA*, RTDP, DTA*) can be used. Such algorithms conduct a limited ply lookahead and then evaluate the states envisioned using a heuristic function. The action selected on the basis of such evaluations can be suboptimal due to the incompleteness of search and inaccuracies in the heuristic. It is usually believed that deeper lookahead increases the chances of taking the optimal action. In this paper, we demonstrate that this is not necessarily the case, even when admissible and consistent heuristic functions are used.