Incremental Methods for Computing Bounds in Partially Observable Markov Decision Processes

No abstract is available for this paper.

• Publication year

  1997

• Venue

  AAAI/IAAI

• Publication date

  1997-07-27

• Fields of study

  Mathematics, Computer Science

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Dynamic Decision Making in Stochastic Partially Observable Domains: Ischemic Heart Disease Example

  1997cited by this paper
• Planning and control in stochastic domains with imperfect information

  1997cited by this paper
• Learning Policies for Partially Observable Environments: Scaling Up

  1997cited by this paper
• Approximating Optimal Policies for Partially Observable Stochastic Domains

  1995cited by this paper
• Efficient dynamic-programming updates in partially observable Markov decision processes

  1995cited by this paper
• Optimal Policies for Partially Observable Markov Decision Processes

  1994cited by this paper
• Suboptimal Policies, with Bounds, for Parameter Adaptive Decision Processes

  1993cited by this paper
• Computationally Feasible Bounds for Partially Observed Markov Decision Processes

  1991cited by this paper
• The Optimal Control of Partially Observable Markov Processes over a Finite Horizon

  1973cited by this paper
• Optimal control of Markov processes with incomplete state information

  1965cited by this paper

• An Oracle-Guided Approach to Constrained Policy Synthesis Under Uncertainty

  2025cites this paper
• Decentralized Planning Using Probabilistic Hyperproperties

  2025cites this paper
• Joint Optimization of Condition-Based Maintenance and Spare Parts Ordering for a Hidden Multi-State Deteriorating System

  2025cites this paper
• Multi-Environment POMDPs: Discrete Model Uncertainty Under Partial Observability

  2025cites this paper
• Small Decision Trees for MDPs with Deductive Synthesis

  2025cites this paper
• Learning Explainable and Better Performing Representations of POMDP Strategies

  2024cites this paper
• Online Planning in POMDPs with State-Requests

  2024cites this paper
• Pragmatic Communication for Remote Control of Finite-State Markov Processes

  2024cites this paper
• Search and explore: symbiotic policy synthesis in POMDPs

  2023cites this paper
• Inductive Synthesis of Finite-State Controllers for POMDPs

  2022cites this paper
• Age-Optimal Network Coding HARQ Scheme for Satellite-Based Internet of Things

  2022cites this paper
• Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation

  2021cites this paper
• AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings)

  2020cites this paper
• A Knowledge-Fusion Ranking System with an Attention Network for Making Assignment Recommendations

  2020cites this paper
• Generative Inverse Deep Reinforcement Learning for Online Recommendation

  2020cites this paper
• Towards Real Time Team Optimization

  2019cites this paper
• Understanding user goals by analyzing logged interactions and asking the right questions

  2019cites this paper
• Efficient Exploration using Value Bounds in Deep Reinforcement Learning Effiziente Exploration in Deep Reinforcement Learning mittels beschraenkter

  2019cites this paper
• Deep reinforcement learning for page-wise recommendations

  2018cites this paper
• Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

  2018cites this paper
• Efficient Decision-Theoretic Target Localization

  2017cites this paper
• Cooperative Multi-agent Control Using Deep Reinforcement Learning

  2017cites this paper
• Deep Reinforcement Learning for List-wise Recommendations

  2017cites this paper
• Resource Allocation Models in Healthcare Decision Making

  2017cites this paper
• Event-triggered adaptive dynamic programming for continuous-time nonlinear system using measured input-output data

  2015cites this paper
• Approximation Methods for Determining Optimal Allocations in Response Adaptive Clinical Trials

  2014cites this paper
• Formal Methods for Control Synthesis in Partially Observed Environments: Application to Autonomous Robotic Manipulation

  2014cites this paper
• Automating Software Customization via Crowdsourcing using Association Rule Mining and Markov Decision Processes

  2014cites this paper
• Data-driven partially observable dynamic processes using adaptive dynamic programming

  2014cites this paper
• A point-reduced POMDP value iteration algorithm with application to robot navigation

  2014cites this paper
• Accounting for Parametric Model Uncertainty in Collision Avoidance for Unmanned Vehicles Using Sparse Grid Interpolation

  2013cites this paper
• The value of sleeping: A rollout algorithm for sensor scheduling in HMMs

  2013cites this paper
• Alleviating the Patient's Price of Privacy Through a Partially Observable Waiting List

  2013influential citation
• Active Sensing for Partially Observable Markov Decision Processes

  2013cites this paper
• Reliability Prediction of Consumer Computing Applications

  2013cites this paper
• Alleviating the Patient’S Price of Privacy Through a Partially Observable Waiting List

  2013cites this paper
• Closing the Gap: Improved Bounds on Optimal POMDP Solutions

  2011cites this paper
• Restricted Value Iteration: Theory and Algorithms

  2011cites this paper
• Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes

  2011cites this paper
• Closing the Gap: Improved Bounds on Optimal POMDP Solutions

  2011influential citation
• Optimization for stochastic, partially observed systems using a sampling-based approach to learn switched policies

  2011cites this paper
• Predictive representations for sequential decision making under uncertainty

  2010cites this paper
• A Survey of Collaborative Filtering Techniques

  2009cites this paper
• LUDOVIC TOBIN A Stochastic Point-Based Algorithm for Partially Observable Markov Decision Processes

  2009cites this paper
• Approximate Dynamic Programming Using Random Sampling : Summary

  2008cites this paper
• A stochastic point-based algorithm for Partially Observable Markov Decision Processes

  2008cites this paper
• Estimating the price of privacy in liver transplantation

  2008cites this paper
• Random Sampling of States in Dynamic Programming

  2007cites this paper
• ON KNOWLEDGE REPRESENTATION AND DECISION MAKING UNDER UNCERTAINTY

  2007cites this paper
• Stochastic -Model -Driven Adaptation and Recovery in Distributed Systems

  2007cites this paper
• Probabilistic planning for robotic exploration

  2007influential citation
• Region-Based Dynamic Programming for POMDPs.

  2006cites this paper
• Compact, Convex Upper Bound Iteration for Approximate POMDP Planning

  2006influential citation
• Algorithms for Planning under Uncertainty in Prediction and Sensing

  2006cites this paper
• Automatic Recovery Using Bounded Partially Observable Markov Decision Processes

  2006cites this paper
• Hybrid POMDP Algorithms

  2006cites this paper
• Region-Based Dynamic Programming for POMDPs

  2006cites this paper
• Hybrid POMDP Algorithms Paper # 89

  2006cites this paper
• Transition Entropy in Partially Observable Markov Decision Processes

  2006cites this paper
• Anytime Point-Based Approximations for Large POMDPs

  2006cites this paper
• The role of representation and abstraction in stochastic planning

  2005cites this paper
• Point-Based POMDP Algorithms: Improved Analysis and Implementation

  2005cites this paper
• Distributed Decision-Making and TaskCoordination in Dynamic, Uncertain andReal-Time Multiagent Environments

  2005cites this paper
• Rover Science Autonomy: Probabilistic Planning for Science-Aware Exploration

  2005cites this paper
• Exploiting structure to efficiently solve large scale partially observable Markov decision processes

  2005cites this paper
• Tractable planning under uncertainty: exploiting structure

  2004influential citation
• Heuristic Search Value Iteration for POMDPs

  2004cites this paper
• A ( Revised ) Survey of Approximate Methods for Solving Partially Observable Markov Decision Processes

  2003cites this paper
• Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions

  2003cites this paper
• Policy-Gradient Algorithms for Partially Observable Markov Decision Processes

  2003influential citation
• Internal-State Policy-Gradient Algorithms for Partially Observable Markov Decision Processes

  2002cites this paper
• Possibilistic Markov decision processes

  2001cites this paper
• An Improved Grid-Based Approximation Algorithm for POMDPs

  2001cites this paper
• Algorithms for partially observable markov decision processes

  2001cites this paper
• Planning treatment of ischemic heart disease with partially observable Markov decision processes

  2000cites this paper
• Working Notes of the AIMDM ' 99 Workshop on Prognostic Models in Medicine : Arti cial Intelligence and Decision Analytic Approaches held during The Joint European Conference on Arti cial Intelligence in Medicine and Medical Decision Making

  1999cites this paper
• A specialized POMDP form and algorithm for clinical patient management

  1999cites this paper
• Approximation Algorithms for Solving Cost Observable Markov Decision Processes

  1999cites this paper
• Integration of Partially Observable Markov Decision Processes and Reinforcement Learning for Simulat

  1999cites this paper
• A Possibilistic Model for Qualitative Sequential Decision Problems under Uncertainty in Partially Observable Environments

  1999cites this paper
• The cog project: building a humanoid robot

  1999cites this paper
• Solving POMDPs by Searching in Policy Space

  1998influential citation
• Alternative Essences of Intelligence

  1998cites this paper
• A Model Approximation Scheme for Planning in Partially Observable Stochastic Domains

  1997cites this paper