Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Introduction to algorithms
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Algebraic decision diagrams and their applications
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Top-down induction of first-order logical decision trees
Artificial Intelligence
Stochastic dynamic programming with factored representations
Artificial Intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Symbolic Model Checking
Machine Learning
Symbolic heuristic search for factored Markov decision processes
Eighteenth national conference on Artificial intelligence
Dynamic Programming
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exploiting first-order regression in inductive policy selection
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
FLUCAP: a heuristic search planner for first-order MDPs
Journal of Artificial Intelligence Research
On mining closed sets in multi-relational data
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
First order decision diagrams for relational MDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Practical solution techniques for first-order MDPs
Artificial Intelligence
Intensional dynamic programming. A Rosetta stone for structured dynamic programming
Journal of Algorithms
Generalized first order decision diagrams for first order Markov decision processes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Planning with noisy probabilistic relational rules
Journal of Artificial Intelligence Research
Relational preference rules for control
Artificial Intelligence
Decision-theoretic planning with generalized first-order decision diagrams
Artificial Intelligence
A partition-based first-order probabilistic logic to represent interactive beliefs
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Probabilistic relational planning with first order decision diagrams
Journal of Artificial Intelligence Research
Exploration in relational domains for model-based reinforcement learning
The Journal of Machine Learning Research
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Markov decision processes capture sequential decision making under uncertainty, where an agent must choose actions so as to optimize long term reward. The paper studies efficient reasoning mechanisms for Relational Markov Decision Processes (RMDP) where world states have an internal relational structure that can be naturally described in terms of objects and relations among them. Two contributions are presented. First, the paper develops First Order Decision Diagrams (FODD), a new compact representation for functions over relational structures, together with a set of operators to combine FODDs, and novel reduction techniques to keep the representation small. Second, the paper shows how FODDs can be used to develop solutions for RMDPs, where reasoning is performed at the abstract level and the resulting optimal policy is independent of domain size (number of objects) or instantiation. In particular, a variant of the value iteration algorithm is developed by using special operations over FODDs, and the algorithm is shown to converge to the optimal policy.