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POMDPs under Probabilistic Semantics
Artificial Intelligence, 2014 We consider partially observable Markov decision processes (POMDPs) with limit-average payoff, where a reward value in the interval [0,1] is associated to every transition, and the payoff of an infinite path is the long-run average of the rewards.
Chatterjee, Krishnendu, Chmelik, Martin
core +4 more sources
, 2014 We present quantum observable Markov decision processes (QOMDPs), the quantum analogues of partially observable Markov decision processes (POMDPs). In a QOMDP, an agent's state is represented as a quantum state and the agent can choose a superoperator to apply.
Jennifer Barry +2 more
openalex +3 more sources
Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models [PDF]
EntropyWe introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research ...
Samuel William Nehrer +5 more
doaj +2 more sources
An algorithm to create model file for Partially Observable Markov Decision Process for mobile robot path planning [PDF]
MethodsXThe Partially Observable Markov Decision Process (POMDP), a mathematical framework for decision-making in uncertain environments suffers from the curse of dimensionality.
Shripad V. Deshpande +3 more
doaj +2 more sources
Enhancing Geomagnetic Navigation with PPO-LSTM: Robust Navigation Utilizing Observed Geomagnetic Field Data [PDF]
SensorsGeospatial navigation in GPS-denied environments presents significant challenges, particularly for autonomous vehicles operating in complex, unmapped regions.
Xiaohui Zhang +5 more
doaj +2 more sources
Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Selecting appropriate tutoring help actions that account for both a student’s content mastery and engagement level is essential for effective human tutors, indicating the critical need for these skills in autonomous tutors. In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable ...
Aditi Ramachandran +2 more
openalex +3 more sources
OGAI-Journal, 2008 In recent work, Bayesian methods for exploration in Markov decision processes (MDPs) and for solving known partially-observable Markov decision processes (POMDPs) have been proposed. In this paper we review the similarities and differences between those two domains and propose methods to deal with them simultaneously.
Christos Dimitrakakis
+5 more sources
Electrophysiological Markers of Aberrant Cue-Specific Exploration in Hazardous Drinkers
Computational Psychiatry, 2023 Background: Hazardous drinking is associated with maladaptive alcohol-related decision-making. Existing studies have often focused on how participants learn to exploit familiar cues based on prior reinforcement, but little is known about the mechanisms ...
Ethan M. Campbell +6 more
doaj +1 more source
Decision Modeling of UAV On-Line Path Planning Based on IMM [PDF]
Xibei Gongye Daxue Xuebao, 2018 In order to enhance the capability of tracking targets autonomously of UAV, a model for UAV on-line path planning is established based on the theoretical framework of partially observable markov decision process(POMDP).
doaj +1 more source
Forward and Backward Bellman Equations Improve the Efficiency of the EM Algorithm for DEC-POMDP
Entropy, 2021 Decentralized partially observable Markov decision process (DEC-POMDP) models sequential decision making problems by a team of agents. Since the planning of DEC-POMDP can be interpreted as the maximum likelihood estimation for the latent variable model ...
Takehiro Tottori, Tetsuya J. Kobayashi
doaj +1 more source