Results 1  10
of
201
Partially observable markov decision processes with continuous observations for dialogue management
 Computer Speech and Language
, 2005
"... This work shows how a dialogue model can be represented as a Partially Observable Markov Decision Process (POMDP) with observations composed of a discrete and continuous component. The continuous component enables the model to directly incorporate a confidence score for automated planning. Using a t ..."
Abstract

Cited by 210 (50 self)
 Add to MetaCart
(Show Context)
This work shows how a dialogue model can be represented as a Partially Observable Markov Decision Process (POMDP) with observations composed of a discrete and continuous component. The continuous component enables the model to directly incorporate a confidence score for automated planning. Using a testbed simulated dialogue management problem, we show how recent optimization techniques are able to find a policy for this continuous POMDP which outperforms a traditional MDP approach. Further, we present a method for automatically improving handcrafted dialogue managers by incorporating POMDP belief state monitoring, including confidence score information. Experiments on the testbed system show significant improvements for several example handcrafted dialogue managers across a range of operating conditions. 1
Pointbased POMDP algorithms: Improved analysis and implementation
 in Proceedings of Uncertainty in Artificial Intelligence
"... Existing complexity bounds for pointbased POMDP value iteration algorithms focus either on the curse of dimensionality or the curse of history. We derive a new bound that relies on both and uses the concept of discounted reachability; our conclusions may help guide future algorithm design. We also ..."
Abstract

Cited by 154 (3 self)
 Add to MetaCart
Existing complexity bounds for pointbased POMDP value iteration algorithms focus either on the curse of dimensionality or the curse of history. We derive a new bound that relies on both and uses the concept of discounted reachability; our conclusions may help guide future algorithm design. We also discuss recent improvements to our (pointbased) heuristic search value iteration algorithm. Our new implementation calculates tighter initial bounds, avoids solving linear programs, and makes more effective use of sparsity. Empirical results show speedups of more than two orders of magnitude. 1
An analytic solution to discrete bayesian reinforcement learning
 In Proc. ICML
, 2006
"... Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms come short of achieving this goal because the amount of exploration required is often too costly and/or too time consuming ..."
Abstract

Cited by 136 (8 self)
 Add to MetaCart
(Show Context)
Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms come short of achieving this goal because the amount of exploration required is often too costly and/or too time consuming for online learning. As a result, RL is mostly used for offline learning in simulated environments. We propose a new algorithm, called BEETLE, for effective online learning that is computationally efficient while minimizing the amount of exploration. We take a Bayesian modelbased approach, framing RL as a partially observable Markov decision process. Our two main contributions are the analytical derivation that the optimal value function is the upper envelope of a set of multivariate polynomials, and an efficient pointbased value iteration algorithm that exploits this simple parameterization. 1.
Online planning algorithms for POMDPs
 Journal of Artificial Intelligence Research
, 2008
"... Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decisionmaking under uncertainty in stochastic domains. However, solving a POMDP is often intractable except for small problems due to their complexity. Here, we focus on online approaches that alleviate ..."
Abstract

Cited by 109 (3 self)
 Add to MetaCart
(Show Context)
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decisionmaking under uncertainty in stochastic domains. However, solving a POMDP is often intractable except for small problems due to their complexity. Here, we focus on online approaches that alleviate the computational complexity by computing good local policies at each decision step during the execution. Online algorithms generally consist of a lookahead search to find the best action to execute at each time step in an environment. Our objectives here are to survey the various existing online POMDP methods, analyze their properties and discuss their advantages and disadvantages; and to thoroughly evaluate these online approaches in different environments under various metrics (return, error bound reduction, lower bound improvement). Our experimental results indicate that stateoftheart online heuristic search methods can handle large POMDP domains efficiently. 1.
Anytime pointbased approximations for large pomdps
 Journal of Artificial Intelligence Research
, 2006
"... The Partially Observable Markov Decision Process has long been recognized as a rich framework for realworld planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. A wellknown tech ..."
Abstract

Cited by 102 (7 self)
 Add to MetaCart
(Show Context)
The Partially Observable Markov Decision Process has long been recognized as a rich framework for realworld planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. A wellknown technique for speeding up POMDP solving involves performing value backups at specific belief points, rather than over the entire belief simplex. The efficiency of this approach, however, depends greatly on the selection of points. This paper presents a set of novel techniques for selecting informative belief points which work well in practice. The point selection procedure is combined with pointbased value backups to form an effective anytime POMDP algorithm called PointBased Value Iteration (PBVI). The first aim of this paper is to introduce this algorithm and present a theoretical analysis justifying the choice of belief selection technique. The second aim of this paper is to provide a thorough empirical comparison between PBVI and other stateoftheart POMDP methods, in particular the Perseus algorithm, in an effort to highlight their similarities and differences. Evaluation is performed using both standard POMDP domains and realistic robotic tasks.
PointBased Value Iteration for Continuous POMDPs
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for modelbased POMDPs are restricted to discrete states, actions, and observations, but many realworld problems such as, for instance, robot na ..."
Abstract

Cited by 65 (4 self)
 Add to MetaCart
We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for modelbased POMDPs are restricted to discrete states, actions, and observations, but many realworld problems such as, for instance, robot navigation, are naturally defined on continuous spaces. In this work, we demonstrate that the value function for continuous POMDPs is convex in the beliefs over continuous state spaces, and piecewiselinear convex for the particular case of discrete observations and actions but still continuous states. We also demonstrate that continuous Bellman backups are contracting and isotonic ensuring the monotonic convergence of valueiteration algorithms. Relying on those properties, we extend the PERSEUS algorithm, originally developed for discrete POMDPs, to work in continuous state spaces by representing the observation, transition, and reward models using Gaussian mixtures, and the beliefs using Gaussian mixtures or particle sets. With these representations, the integrals that appear in the Bellman backup can be computed in closed form and, therefore, the algorithm is computationally feasible. Finally, we further extend PERSEUS to deal with continuous action and observation sets by designing effective sampling approaches.
Grasping POMDPs
 in Proc. IEEE Int. Conf. on Robotics and Automation (ICRA
, 2007
"... Abstract — We provide a method for planning under uncertainty for robotic manipulation by partitioning the configuration space into a set of regions that are closed under compliant motions. These regions can be treated as states in a partially observable Markov decision process (POMDP), which can be ..."
Abstract

Cited by 60 (3 self)
 Add to MetaCart
(Show Context)
Abstract — We provide a method for planning under uncertainty for robotic manipulation by partitioning the configuration space into a set of regions that are closed under compliant motions. These regions can be treated as states in a partially observable Markov decision process (POMDP), which can be solved to yield optimal control policies under uncertainty. We demonstrate the approach on simple grasping problems, showing that it can construct highly robust, efficiently executable solutions. I.
Closing the learningplanning loop with predictive state representations (Extended Abstract)
, 2010
"... A central problem in artificial intelligence is to plan to maximize future reward under uncertainty in a partially observable environment. Models of such environments include Partially Observable Markov Decision Processes (POMDPs) [4] as well as their generalizations, Predictive State Representation ..."
Abstract

Cited by 49 (12 self)
 Add to MetaCart
(Show Context)
A central problem in artificial intelligence is to plan to maximize future reward under uncertainty in a partially observable environment. Models of such environments include Partially Observable Markov Decision Processes (POMDPs) [4] as well as their generalizations, Predictive State Representations (PSRs) [9] and Observable Operator Models (OOMs) [7]. POMDPs model the state of the world as a latent variable; in contrast, PSRs and OOMs represent state by tracking occurrence probabilities of a set of future events (called tests or characteristic events) conditioned on past events (called histories or indicative events). Unfortunately, exact planning algorithms such as value iteration [14] are intractable for most realistic POMDPs due to the curse of history and the curse of dimensionality [11]. However, PSRs and OOMs hold the promise of mitigating both of these curses: first, many successful approximate planning techniques designed to address
Motion Planning under Uncertainty for Robotic Tasks with Long Time Horizons
"... Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation of autonomous robots. By using probabilistic sampling, pointbased POMDP solvers have drastically improved the speed of ..."
Abstract

Cited by 39 (2 self)
 Add to MetaCart
(Show Context)
Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation of autonomous robots. By using probabilistic sampling, pointbased POMDP solvers have drastically improved the speed of POMDP planning, enabling POMDPs to handle moderately complex robotic tasks. However, robot motion planning tasks with long time horizons remain a severe obstacle for even the fastest pointbased POMDP solvers today. This paper proposes Milestone Guided Sampling (MiGS), a new pointbased POMDP solver, which exploits state space information to reduce the effective planning horizon. MiGS samples a set of points, called milestones, from a robot’s state space, uses them to construct a compact, sampled representation of the state space, and then uses this representation of the state space to guide sampling in the belief space. This strategy reduces the effective planning horizon, while still capturing the essential features of the belief space with a small number of sampled points. Preliminary results are very promising. We tested MiGS in simulation on several difficult POMDPs modeling distinct robotic tasks with long time horizons; they are impossible with the fastest pointbased POMDP solvers today. MiGS solved them in a few minutes. 1