Sciweavers

Share
IJCAI
2001

Symbolic Dynamic Programming for First-Order MDPs

8 years 8 months ago
Symbolic Dynamic Programming for First-Order MDPs
We present a dynamic programming approach for the solution of first-order Markov decisions processes. This technique uses an MDP whose dynamics is represented in a variant of the situation calculus allowing for stochastic actions. It produces a logical description of the optimal value function and policy by constructing a set of first-order formulae that minimally partition state space according to distinctions made by the value function and policy. This is achieved through the use of an operation known as decision-theoretic regression. In effect, our algorithm performs value iteration without explicit enumeration of either the state or action spaces of the MDP. This allows problems involving relational fluents and quantification to be solved without requiring explicit state space enumeration or conversion to propositional form.
Craig Boutilier, Raymond Reiter, Bob Price
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where IJCAI
Authors Craig Boutilier, Raymond Reiter, Bob Price
Comments (0)
books