Publication

Learning relational dynamics of stochastic domains for planning

Conference Article

Conference

International Conference on Automated Planning and Scheduling (ICAPS)

Edition

26th

Pages

235-243

Doc link

http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13079

File

Download the digital copy of the doc pdf document

Abstract

Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.

Categories

learning (artificial intelligence), planning (artificial intelligence), uncertainty handling.

Scientific reference

D. Martínez, G. Alenyà, C. Torras, T. Ribeiro and K. Inoue. Learning relational dynamics of stochastic domains for planning, 26th International Conference on Automated Planning and Scheduling, 2016, London, pp. 235-243.