Results 11 to 20 of about 937,585 (275)
Machine Learning and Knowledge Extraction, 2021 The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems.
Xuanchen Xiang, Simon Foo
doaj +2 more sources
Cost-Bounded Active Classification Using Partially Observable Markov Decision Processes [PDF]
arXiv, 2018 Active classification, i.e., the sequential decision-making process aimed at data acquisition for classification purposes, arises naturally in many applications, including medical diagnosis, intrusion detection, and object tracking. In this work, we study the problem of actively classifying dynamical systems with a finite set of Markov decision process
Bo Wu +3 more
arxiv +3 more sources
Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes [PDF]
arXiv, 2013 Most exact algorithms for general partially observable Markov decision processes (POMDPs) use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" method for solving this problem and compare them to earlier algorithms ...
Anthony R. Cassandra +2 more
arxiv +3 more sources
Partially observable Markov decision processes with imprecise parameters
Artificial Intelligence, 2007 AbstractThis study extends the framework of partially observable Markov decision processes (POMDPs) to allow their parameters, i.e., the probability values in the state transition functions and the observation functions, to be imprecisely specified. It is shown that this extension can reduce the computational costs associated with the solution of these
H. Itoh, Kiyohiko Nakamura
openalex +3 more sources
Qualitative Analysis of Partially-Observable Markov Decision Processes [PDF]
, 2010 We study observation-based strategies for partially-observable Markov decision processes (POMDPs) with omega-regular objectives. An observation-based strategy relies on partial information about the history of a play, namely, on the past sequence of observa- tions.
Krishnendu Chatterjee +2 more
openalex +8 more sources
Entropy Maximization for Partially Observable Markov Decision Processes [PDF]
IEEE Transactions on Automatic Control, 2022 14 pages, 10 figures.
Yagiz Savas +4 more
openaire +3 more sources
Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method [PDF]
arXiv, 2013 There is much interest in using partially observable Markov decision processes (POMDPs) as a formal model for planning in stochastic domains. This paper is concerned with finding optimal policies for POMDPs. We propose several improvements to incremental pruning, presently the most efficient exact algorithm for solving POMDPs.
Nevin L. Zhang, Stephen S. Lee
arxiv +3 more sources
Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes [PDF]
AAAI 2020, 2020 Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the state of the environment. We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP)
Maxime Bouton +2 more
arxiv +3 more sources
Task-Aware Verifiable RNN-Based Policies for Partially Observable Markov Decision Processes
Journal of Artificial Intelligence Research, 2021 Partially observable Markov decision processes (POMDPs) are models for sequential decision-making under uncertainty and incomplete information. Machine learning methods typically train recurrent neural networks (RNN) as effective representations of POMDP
Steven Carr, Nils Jansen, Ufuk Topcu
openalex +3 more sources
Autonomous Thermalling as a Partially Observable Markov Decision Process [PDF]
Robotics: Science and Systems XIV, 2018 Small uninhabited aerial vehicles (sUAVs) commonly rely on active propulsion to stay airborne, which limits flight time and range. To address this, autonomous soaring seeks to utilize free atmospheric energy in the form of updrafts (thermals). However, their irregular nature at low altitudes makes them hard to exploit for existing methods.
Jim Piavi +3 more
openaire +3 more sources