Probabilistic backward and forward reasoning in stochastic relational worlds
Tobias Lang and Marc Toussaint
In: ICML 2010(2010).

Abstract

Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic plan- ning in relational domains uses forward infer- ence in dynamic Bayesian networks compiled from learned probabilistic relational rules. Inspired by work in non-relational domains with small state spaces, we derive a back- propagation method for such nets in rela- tional domains starting from a goal state mix- ture distribution. We combine this with for- ward reasoning in a bidirectional two-lter approach. We perform experiments in a complex 3D simulated desktop environment with an articulated manipulator and realis- tic physics. Empirical results show that bidi- rectional probabilistic reasoning can lead to more ecient and accurate planning in com- parison to pure forward reasoning.

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