Results 41 to 50 of about 30,788 (321)

Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

International Journal of Applied Mathematics and Computer Science, 2015
The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the ...
Itoh Hideaki, Fukumoto Hisao, Wakuya Hiroshi, Furukawa Tatsuya   +3 more
doaj   +1 more source

Adapting Content Delivery to Limited Resources and Inferred User Interest

International Journal of Digital Multimedia Broadcasting, 2008
This paper discusses adaptation policies for information systems that are subject to dynamic and stochastic contexts such as mobile access to multimedia web sites. In our approach, adaptation agents apply sequential decisional policies under uncertainty.
Cezar Plesca, Vincent Charvillat, Romulus Grigoras   +2 more
doaj   +1 more source

Partially Observable Markov Decision Process-Based Transmission Policy over Ka-Band Channels for Space Information Networks

Entropy, 2017
The Ka-band and higher Q/V band channels can provide an appealing capacity for the future deep-space communications and Space Information Networks (SIN), which are viewed as a primary solution to satisfy the increasing demands for high data rate services.
Jian Jiao, Xindong Sui, Shushi Gu, Shaohua Wu, Qinyu Zhang   +4 more
doaj   +1 more source

Learning Dynamics and Control of a Stochastic System under Limited Sensing Capabilities

Sensors, 2022
The operation of a variety of natural or man-made systems subject to uncertainty is maintained within a range of safe behavior through run-time sensing of the system state and control actions selected according to some strategy.
Mohammad Amin Zadenoori, Enrico Vicario
doaj   +1 more source

Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation [PDF]

, 2015
Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable,
Caballero, Fernando, Capitán, Jesús, Merino, Luis, Pérez Hurtado de Mendoza, Ignacio   +3 more
core   +1 more source

Robust Partially Observable Markov Decision Processes

SSRN Electronic Journal, 2018
In a variety of applications, decisions needs to be made dynamically after receiving imperfect observations about the state of an underlying system. Partially Observable Markov Decision Processes (POMDPs) are widely used in such applications. To use a POMDP, however, a decision-maker must have access to reliable estimations of core state and ...
Mohammad Rasouli, Soroush Saghafian
openaire   +2 more sources

Partially Observable Markov Decision Processes in Robotics: A Survey

IEEE Transactions on Robotics, 2023
Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision and control tasks under uncertainty.
Lauri, Mikko, Hsu, David, Pajarinen, Joni   +3 more
openaire   +4 more sources

Partially Observable Markov Decision Processes in Shared Autonomy Applications: A Survey

IEEE Access
Shared autonomy consists of a collaborative effort between a human user and a robotic system having a shared goal, in which the human-controlled robot adapts its behaviour to provide assistive actions.
Shyrailym Shaldambayeva, Kamila Zhumakhanova, Anara Sandygulova, Matteo Rubagotti, Almas Shintemirov   +4 more
doaj   +1 more source

UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration

Drones, 2022
Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan ...
Julian Galvez-Serna, Fernando Vanegas, Shahzad Brar, Juan Sandino, David Flannery, Felipe Gonzalez   +5 more
doaj   +1 more source

Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions [PDF]

, 2017
This paper presents a data-driven approach for multi-robot coordination in partially-observable domains based on Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a general framework for
Amato, Christopher, How, Jonathan P., Liu, Miao, Omidshafiei, Shayegan, Sivakumar, Kavinayan   +4 more
core   +2 more sources