Principles of artificial intelligence
Principles of artificial intelligence
Search in Artificial Intelligence
Artificial Intelligence
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Heuristic sampling: a method for predicting the performance of tree searching programs
SIAM Journal on Computing
Optimal schedules for monitoring anytime algorithms
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Monitoring and control of anytime algorithms: a dynamic programming approach
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Time complexity of iterative-deepening-A
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Anytime Heuristic Searc: First Results TITLE2:
Anytime Heuristic Searc: First Results TITLE2:
Comparing real-time and incremental heuristic search for real-time situated agents
Autonomous Agents and Multi-Agent Systems
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Iterative-deepening-A: an optimal admissible tree search
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
Minimizing response times in real time planning and search
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Solving time-dependent planning problems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
A mobius automation: an application of artificial intelligence techniques
IJCAI'69 Proceedings of the 1st international joint conference on Artificial intelligence
Best-first utility-guided search
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Predicting the performance of IDA* using conditional distributions
Journal of Artificial Intelligence Research
Planning with deadlines in stochastic domains
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Metareasoning: Thinking about Thinking
Metareasoning: Thinking about Thinking
On-line planning and scheduling: an application to controlling modular printers
Journal of Artificial Intelligence Research
A bayesian approach to tackling hard computational problems
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Rational deployment of CSP heuristics
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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In many applications of shortest-path algorithms, it is impractical to find a provably optimal solution; one can only hope to achieve an appropriate balance between search time and solution cost that respects the user's preferences. Preferences come in many forms; we consider utility functions that linearly trade-off search time and solution cost. Many natural utility functions can be expressed in this form. For example, when solution cost represents the makespan of a plan, equally weighting search time and plan makespan minimizes the time from the arrival of a goal until it is achieved. Current state-of-theart approaches to optimizing utility functions rely on anytime algorithms, and the use of extensive training data to compute a termination policy. We propose a more direct approach, called Bugsy, that incorporates the utility function directly into the search, obviating the need for a separate termination policy. We describe a new method based on off-line parameter tuning and a novel benchmark domain for planning under time pressure based on platform-style video games. We then present what we believe to be the first empirical study of applying anytime monitoring to heuristic search, and we compare it with our proposals. Our results suggest that the parameter tuning technique can give the best performance if a representative set of training instances is available. If not, then Bugsy is the algorithm of choice, as it performs well and does not require any off-line training. This work extends the tradition of research on metareasoning for search by illustrating the benefits of embedding lightweight reasoning about time into the search algorithm itself.