Using backprojections for fine motion planning with uncertainty
International Journal of Robotics Research
Principles of artificial intelligence
Principles of artificial intelligence
Generalized subsumption and its applications to induction and redundancy
Artificial Intelligence
Explanation-based generalisation = partial evaluation
Artificial Intelligence
Explanation-based learning: a survey of programs and perspectives
ACM Computing Surveys (CSUR)
Explanation-based learning: a problem solving perspective
Artificial Intelligence
ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Removing redundancy from a clause
Artificial Intelligence
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Stochastic dynamic programming with factored representations
Artificial Intelligence
Guarded commands, nondeterminacy and formal derivation of programs
Communications of the ACM
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Machine Learning
Structured Prioritised Sweeping
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
The synthesis of structure-changing programs
ICSE '78 Proceedings of the 3rd international conference on Software engineering
Dynamic Programming
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Dynamic programming for structured continuous Markov decision problems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Planning with POMDPs Using a Compact, Logic-Based Representation
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
First order markov decision processes
First order markov decision processes
Lazy approximation for solving continuous finite-horizon MDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Probabilistic partial evaluation: exploiting rule structure in probabilistic inference
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
First order decision diagrams for relational MDPs
Journal of Artificial Intelligence Research
First order decision diagrams for relational MDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
We present intensional dynamic programming (IDP), a generic framework for structured dynamic programming over atomic, propositional and relational representations of states and actions. We first develop set-based dynamic programming and show its equivalence with classical dynamic programming. We then show how to describe state sets intensionally using any form of structured knowledge representation and obtain a generic algorithm that can optimally solve large, even infinite, MDPs without explicit state space enumeration. We derive two new Bellman backup operators and algorithms. In order to support the view of IDP as a Rosetta stone for structured dynamic programming, we review many existing techniques that employ either propositional or relational knowledge representation frameworks.