Results 81 to 90 of about 13,931 (229)
Strong Simple Policies for POMDPs
International Journal on Software Tools for Technology TransferAbstractThe synthesis problem for partially observable Markov decision processes (POMDPs) is to compute a policy that provably adheres to one or more specifications. Yet, the general problem is undecidable, and policies require full (and thus potentially unbounded) traces of execution history.
Winterer, Leonore +3 more
openaire +3 more sources
The Inner Loop of Collective Human–Machine Intelligence
Topics in Cognitive Science, Volume 17, Issue 2, Page 248-267, April 2025.Abstract With the rise of artificial intelligence (AI) and the desire to ensure that such machines work well with humans, it is essential for AI systems to actively model their human teammates, a capability referred to as Machine Theory of Mind (MToM).
Scott Cheng‐Hsin Yang +2 more
wiley +1 more source
IEEE AccessThis paper introduces the POMA-C (Partial Observable Model for Anesthesia Control) framework, developed to address the challenge of anesthesia management in environments with incomplete physiological monitoring, such as low-resource settings where ...
Yide Yu +6 more
doaj +1 more source
Prediction of stock prices with automated reinforced learning algorithms
Expert Systems, Volume 42, Issue 2, February 2025.Abstract Predicting stock price movements remains a major challenge in time series analysis. Despite extensive research on various machine learning techniques, few models have consistently achieved success in automated stock trading. One of the main challenges in stock price forecasting is that the optimal model changes over time due to market dynamics.
Said Yasin +2 more
wiley +1 more source
Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration With Application to Autonomous Sequential Repair Problems [PDF]
, 2020 Sushmita Bhattacharya +4 more
openalex +1 more source
, 2008 Markov decision processes (MDPs) and partially observable Markov decision processes (DEC-POMDPs) are both mathematical models that have been successfully used to formalize sequential decision-theoretic problems under uncertainty. These models rely on different types of hypotheses that can be classified within: i) each agent has a complete knowledge of ...
Beynier, Aurélie +3 more
openaire +2 more sources
Engineering Reports, Volume 7, Issue 1, January 2025.The article introduces EPPTA, a reinforcement learning framework designed for penetration testing, and assesses its performance across diverse network configurations. EPPTA incorporates a belief module, augmenting its capacity to handle partially observable security challenges.
Zegang Li, Qian Zhang, Guangwen Yang
wiley +1 more source
Penetration Testing == POMDP Solving?
, 2011 Penetration Testing is a methodology for assessing network security, by generating and executing possible attacks. Doing so automatically allows for regular and systematic testing without a prohibitive amount of human labor. A key question then is how to generate the attacks. This is naturally formulated as a planning problem.
Sarraute, Carlos +2 more
openaire +3 more sources
Optimal Joint Defense and Monitoring for Networks Security under Uncertainty: A POMDP-Based Approach
IET Information SecurityThe increasing interconnectivity in our infrastructure poses a significant security challenge, with external threats having the potential to penetrate and propagate throughout the network.
Armita Kazeminajafabadi, Mahdi Imani
doaj +1 more source
Dianxin kexue, 2013 In order to solve the dilemma of the tradeoff between spectrum sensing performance and spectrum sensing efficiency in cognitive radio network，a nove1 ED/FD cooperation spectrum sensing algorithm based on featured belief points was proposed.
Hongyan Zheng +3 more
doaj +2 more sources