Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Taxonomic syntax for first order inference
Journal of the ACM (JACM)
Learning action strategies for planning domains
Artificial Intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Machine Learning
Relational Reinforcement Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Learning Generalized Policies from Planning Examples Using Concept Languages
Applied Intelligence
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Macro-FF: improving AI planning with automatically learned macro-operators
Journal of Artificial Intelligence Research
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
Discriminative learning of beam-search heuristics for planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using learned policies in heuristic-search planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Inductive learning of search control rules for planning
Artificial Intelligence
Top-down induction of first-order logical decision trees
Artificial Intelligence
Learning Linear Ranking Functions for Beam Search with Application to Planning
The Journal of Machine Learning Research
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
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
Improving control-knowledge acquisition for planning by active learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
A case-based approach to heuristic planning
Applied Intelligence
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Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are worth the expense. However, when evaluation functions are misguiding or when planning problems are large enough, lots of node evaluations must be computed, which severely limits the scalability of heuristic planners. In this paper, we present a novel solution for reducing node evaluations in heuristic planning based on machine learning. Particularly, we define the task of learning search control for heuristic planning as a relational classification task, and we use an off-the-shelf relational classification tool to address this learning task. Our relational classification task captures the preferred action to select in the different planning contexts of a specific planning domain. These planning contexts are defined by the set of helpful actions of the current state, the goals remaining to be achieved, and the static predicates of the planning task. This paper shows two methods for guiding the search of a heuristic planner with the learned classifiers. The first one consists of using the resulting classifier as an action policy. The second one consists of applying the classifier to generate lookahead states within a Best First Search algorithm. Experiments over a variety of domains reveal that our heuristic planner using the learned classifiers solves larger problems than state-of-the-art planners.