Toward a mathematical theory of plan synthesis
Toward a mathematical theory of plan synthesis
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Learning by analogical reasoning in general problem-solving
Learning by analogical reasoning in general problem-solving
Genetic programming and AI planning systems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Learning action strategies for planning domains
Artificial Intelligence
Artificial Intelligence
Neuro-Dynamic Programming
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Learning Generalized Policies from Planning Examples Using Concept Languages
Applied Intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Learning action strategies for planning domains using genetic programming
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
GA-FreeCell: evolving solvers for the game of FreeCell
Proceedings of the 13th annual conference on Genetic and evolutionary computation
HH-evolver: a system for domain-specific, hyper-heuristic evolution
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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This work investigates the application of Evolutionary Computation (EC) to the induction of generalised policies used to solve AI planning problems. A policy is defined as an ordered list of rules that specifies which action to perform under which conditions; a solution (plan) to a planning problem is a sequence of actions suggested by the policy. We compare an evolved policy with one produced by a state-of-the art approximate policy iteration approach. We discuss the relative merits of the two approaches with a focus on the impact of the knowledge representation and the learning strategy. In particular we note that a strategy commonly and successfully used for the induction of classification rules, that of Iterative Rule Learning, is not necessarily an optimal strategy for the induction of generalised policies aimed at minimising the number of actions in a plan.