Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Computationally feasible bounds for partially observed Markov decision processes
Operations Research
Planning and control
Technical Note: \cal Q-Learning
Machine Learning
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Dynamic Programming
Optimal Policies for Partially Observable Markov Decision Processes
Optimal Policies for Partially Observable Markov Decision Processes
Approximating optimal policies for partially observable stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Exploiting the rule structure for decision making within the independent choice logic
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Contingent planning under uncertainty via stochastic satisfiability
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Value Iteration over Belief Subspace
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Progressive Planning for Mobile Robots (A Progress Report)
Revised Papers from the International Seminar on Advances in Plan-Based Control of Robotic Agents,
Contingent planning under uncertainty via stochastic satisfiability
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Reinforcement Learning with Factored States and Actions
The Journal of Machine Learning Research
Exploiting belief bounds: practical POMDPs for personal assistant agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Heuristic anytime approaches to stochastic decision processes
Journal of Heuristics
Continual planning and acting in dynamic multiagent environments
PCAR '06 Proceedings of the 2006 international symposium on Practical cognitive agents and robots
Point-Based Value Iteration for Continuous POMDPs
The Journal of Machine Learning Research
Partially observable Markov decision processes with imprecise parameters
Artificial Intelligence
Selecting treatment strategies with dynamic limited-memory influence diagrams
Artificial Intelligence in Medicine
An online multi-agent co-operative learning algorithm in POMDPs
Journal of Experimental & Theoretical Artificial Intelligence
Graphical models for interactive POMDPs: representations and solutions
Autonomous Agents and Multi-Agent Systems
Probabilistic planning with clear preferences on missing information
Artificial Intelligence
Natural Language Engineering
Compact, convex upper bound iteration for approximate POMDP planning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Planning in models that combine memory with predictive representations of state
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Exploiting symmetries in POMDPs for point-based algorithms
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Symbolic heuristic search value iteration for factored POMDPs
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Finding approximate POMDP solutions through belief compression
Journal of Artificial Intelligence Research
Restricted value iteration: theory and algorithms
Journal of Artificial Intelligence Research
A model approximation scheme for planning in partially observable stochastic domains
Journal of Artificial Intelligence Research
Dynamic non-Bayesian decision making
Journal of Artificial Intelligence Research
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
Conditional progressive planning under uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Intensional dynamic programming. A Rosetta stone for structured dynamic programming
Journal of Algorithms
Knowledge representation for stochastic decision processes
Artificial intelligence today
Model minimization in Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Efficient planning in large POMDPs through policy graph based factorized approximations
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Adaptive decision support for structured organizations: a case for OrgPOMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Identifying and exploiting weak-information inducing actions in solving POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A partition-based first-order probabilistic logic to represent interactive beliefs
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Decision Support in Organizations: A Case for OrgPOMDPs
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Value-directed belief state approximation for POMDPs
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Vector-space analysis of belief-state approximation for POMDPs
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Value-directed sampling methods for monitoring POMDPs
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Planning and acting under uncertainty: a new model for spoken dialogue systems
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Algorithms and limits for compact plan representations
Journal of Artificial Intelligence Research
Recognizing internal states of other agents to anticipate and coordinate interactions
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
Tractable POMDP representations for intelligent tutoring systems
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Hi-index | 0.00 |
Partially-observable Markov decision processes provide a general model for decision theoretic planning problems, allowing trade-offs between various courses of actions to be determined under conditions of uncertainty, and incorporating partial observations made by an agent. Dynamic programming algorithms based on the belief state of an agent can be used to construct optimal policies without explicit consideration of past history, but at high computational cost. In this paper, we discuss how structured representations of system dynamics can be incorporated in classic POMDP solution algorithms. We use Bayesian networks with structured conditional probability matrices to represent POMDPs, and use this model to structure the belief space for POMDP algorithms, allowing irrelevant distinctions to be ignored. Apart from speeding up optimal policy construction, we suggest that such representations can be exploited in the development of useful approximation methods.