Open AccessProceedings Article
Symbolic dynamic programming for first-order MDPs
Craig Boutilier,Raymond Reiter,Bob Price +2 more
- pp 690-697
TLDR
This technique uses an MDP whose dynamics is represented in a variant of the situation calculus allowing for stochastic actions and 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 valuefunction and policy.Abstract:
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.read more
Citations
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Dynamic bayesian networks: representation, inference and learning
Kevin Murphy,Stuart Russell +1 more
TL;DR: This thesis will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in Dbns, and how to learn DBN models from sequential data.
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Markov Decision Processes
Nicole Bäuerle,Ulrich Rieder +1 more
TL;DR: The theory of Markov Decision Processes is the theory of controlled Markov chains as mentioned in this paper, which has found applications in various areas like e.g. computer science, engineering, operations research, biology and economics.
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Probabilistic reasoning with answer sets
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A Concise Introduction to Models and Methods for Automated Planning
Hector Geffner,Blai Bonet +1 more
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References
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Book
Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
Neuro-Dynamic Programming.
TL;DR: In this article, the authors present the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
Book
Neuro-dynamic programming
TL;DR: This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
Journal ArticleDOI
Decision-theoretic planning: structural assumptions and computational leverage
TL;DR: In this article, the authors present an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI.