Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Planning in polynomial time: the SAS-PUBS class
Computational Intelligence
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Machine Learning Methods for Planning
Machine Learning Methods for Planning
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
Domain-independent construction of pattern database heuristics for cost-optimal planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Macro-FF: improving AI planning with automatically learned macro-operators
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
A selective macro-learning algorithm and its application to the N × N sliding-tile puzzle
Journal of Artificial Intelligence Research
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Compiling uncertainty away in conformant planning problems with bounded width
Journal of Artificial Intelligence Research
Planning as satisfiability: parallel plans and algorithms for plan search
Artificial Intelligence
Cost-optimal planning with landmarks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Soft goals can be compiled away
Journal of Artificial Intelligence Research
Strengthening Landmark Heuristics via Hitting Sets
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Implicit abstraction heuristics
Journal of Artificial Intelligence Research
The first learning track of the international planning competition
Machine Learning
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Towards rational deployment of multiple heuristics in A*
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
An admissible heuristic for SAS+ planning obtained from the state equation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Domain-independent planning is one of the foundational areas in the field of Artificial Intelligence. A description of a planning task consists of an initial world state, a goal, and a set of actions for modifying the world state. The objective is to find a sequence of actions, that is, a plan, that transforms the initial world state into a goal state. In optimal planning, we are interested in finding not just a plan, but one of the cheapest plans. A prominent approach to optimal planning these days is heuristic state-space search, guided by admissible heuristic functions. Numerous admissible heuristics have been developed, each with its own strengths and weaknesses, and it is well known that there is no single "best" heuristic for optimal planning in general. Thus, which heuristic to choose for a given planning task is a difficult question. This difficulty can be avoided by combining several heuristics, but that requires computing numerous heuristic estimates at each state, and the tradeoff between the time spent doing so and the time saved by the combined advantages of the different heuristics might be high. We present a novel method that reduces the cost of combining admissible heuristics for optimal planning, while maintaining its benefits. Using an idealized search space model, we formulate a decision rule for choosing the best heuristic to compute at each state. We then present an active online learning approach for learning a classifier with that decision rule as the target concept, and employ the learned classifier to decide which heuristic to compute at each state. We evaluate this technique empirically, and show that it substantially outperforms the standard method for combining several heuristics via their pointwise maximum.