Relevance grounding for planning in relational domains
Tobias Lang and Marc Toussaint
In: European Conference on Machine Learning (ECML), 7-11 Sept 2009, Bled, Slovenia.
Probabilistic relational models are an efficient way to learn
and represent the dynamics in realistic environments consisting of many
objects. Autonomous intelligent agents that ground this representation
for all objects need to plan in exponentially large state spaces and large
sets of stochastic actions. A key insight for computational efficiency is
that successful planning typically involves only a small subset of relevant
objects. In this paper, we introduce a probabilistic model to represent
planning with subsets of objects and provide a definition of object relevance. Our definition is sufficient to prove consistency between repeated planning in partially grounded models restricted to relevant objects and
planning in the fully grounded model. We propose an algorithm that exploits
object relevance to plan efficiently in complex domains. Empirical
results in a simulated 3D blocksworld with an articulated manipulator
and realistic physics prove the effectiveness of our approach.