Results 201 to 210 of about 10,494 (264)

A stochastic evolutionary game of boosting urban low-carbon development in China. [PDF]

Sci Rep
Cai R, Loi EHN, Wang X, Zhao S, Zhang T.
europepmc   +1 more source

Reinforcement Learning With Timed Constraints for Robotics Motion Planning

Artificial Intelligence for Engineering, EarlyView.
This work presents a unified automata‐based reinforcement learning framework that enforces MITL time‐bounded task specifications in both MDPs and POMDPs. Results from grid‐world and office scenarios show robust policy learning under stochastic dynamics and partial observability.
Zhaoan Wang, Junchao Li, Mohammad Mahdi Dehghan Pir, Shaoping Xiao   +3 more
wiley   +1 more source

Bayesian Networks and Causal Discovery. [PDF]

Entropy (Basel)
Gao X, Wang Z.
europepmc   +1 more source

Decentralized resource allocation in UAV communication networks through reward based multi agent learning. [PDF]

Sci Rep
Shoaib M, Husnain G, Khan M, Ghadi YY, Lim S.   +4 more
europepmc   +1 more source

A secured cloud‐medical data sharing with A‐BRSA and Salp ‐Ant Lion Optimisation Algorithm

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Sharing medical data among healthcare providers, researchers, and patients is crucial for efficient healthcare services. Cloud‐assisted smart healthcare (s‐healthcare) systems have made it easier to store EHRs effectively. However, the traditional encryption algorithms used to secure this data can be vulnerable to attacks if the encryption key
Adel Binbusayyis, Abed Alanazi, Shtwai Alsubai, Areej Alasiry, Mehrez Marzougui, Abdullah Alqahtani, Mohemmed Sha, Muhammad Aslam   +7 more
wiley   +1 more source

Unilateral incentive alignment in two-agent stochastic games. [PDF]

Proc Natl Acad Sci U S A
McAvoy A, Madhushani Sehwag U, Hilbe C, Chatterjee K, Barfuss W, Su Q, Leonard NE, Plotkin JB.   +7 more
europepmc   +1 more source

AT‐AER: Adversarial Training With Adaptive Example Reuse

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Adversarial training (AT) is widely regarded as a crucial defense method for deep neural networks against adversarial attacks. Most of the existing AT methods suffer from the problems of insufficient coverage of perturbation space and robust overfitting.
Meng Hu, Yanting Guo, Ran Wang, Xizhao Wang, Rihao Li, Qin Wang   +5 more
wiley   +1 more source

To Signal or Not to Signal? A Non-cooperative Game-Theoretic Approach to Discretionary Communication Between Road Users. [PDF]

SN Comput Sci
Bitar I, Solernou Crusat A, Romano R, Watling D.   +3 more
europepmc   +1 more source

Credit‐Driven Adaptive Grouping for Refined Cooperative Multi‐Agent Reinforcement Learning

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Policy heterogeneity is crucial for achieving sophisticated coordination in complex collaborative tasks, which has emerged as one of the key challenges in multi‐agent reinforcement learning (MARL) in recent years. Notably, the grouping paradigm has made remarkable progress in addressing policy heterogeneity.
Yirui Liu, Xueqiang Gu, Caiming Zheng, Xin Xu, Zhangling Wang, Wanpeng Zhang, Yixing Lan   +6 more
wiley   +1 more source

Multiagent game-theoretic robust optimization for power system planning under source-load uncertainty. [PDF]

Sci Rep
Mi J, Xu M, An J, Wang Y, Liu F, Yang H, Tan Z.   +6 more
europepmc   +1 more source