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On-Line Update of Situation Assessment Based on Asynchronous Data Streams [chapter]

Vladimir Gorodetsky, Oleg Karsaev, Vladimir Samoilov
2004 Lecture Notes in Computer Science  
The subject of the paper is multi-agent architecture of and algorithmic basis for on-line situation assessment update based on asynchronous streams of input data received from multiple sources and having  ...  A case study from computer network security area that is anomaly detection is used for demonstration.  ...  for support.  ... 
doi:10.1007/978-3-540-30132-5_154 fatcat:ho6nlrvz6fgabgsqzypx5vzxbe

Data Mining for Decision Making in Multi-Agent Systems [chapter]

Hani K., Hoda K., Sally S.
2011 Multi-Agent Systems - Modeling, Interactions, Simulations and Case Studies  
This chapter considers applying different data mining techniques for the decision making process in a Multi-Agent System for Collaborative E-learning (MASCE).  ...  A previous research outlined the development and the implementation processes of a Multi-Agent System for Collaborative E-learning (MASCE) which is designed to be used to assist Multi-Agent  ...  in Multi-Agent System for Collaborative E-learning (MASCE).  ... 
doi:10.5772/15584 fatcat:u4jicl2oazekno5o6m455naenu

Learning-Based Run-Time Power and Energy Management of Multi/Many-Core Systems: Current and Future Trends

Amit Kumar Singh, Charles Leech, Basireddy Karunakar Reddy, Bashir M. Al-Hashimi, Geoff V. Merrett
2017 Journal of Low Power Electronics  
This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches.  ...  Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing.  ...  Experimental data used in this paper can be found at DOI:  ... 
doi:10.1166/jolpe.2017.1492 fatcat:wrqqr345ujhglkpqt5hllonnxa

A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment

Yeou-Ren Shiue, Ken-Chuan Lee, Chao-Ton Su
2020 IEEE Access  
INDEX TERMS Manufacturing execution system, dynamic scheduling, machine learning, reinforcement learning, Q-learning.  ...  Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for monitoring crucial changes that occur in the production system.  ...  Therefore, for Q-based learning agents, it is unreasonable to have a large number of system states.  ... 
doi:10.1109/access.2020.3000781 fatcat:nl2ivlyppreqtp7igju5vamg6q

An Improved Bayesian Learning Method for Multi-agent System

Shenghui Dai, Xueqin Zhu, Ying Gui, Hongzhen Xu
2015 International Journal of Online Engineering (iJOE)  
Bayesian learning is used in conjunction with RMM for belief update. Based on this method, a multi-agent traffic control system is established and the results rated its effective.  ...  modeling their decision making in a distributed multi-agent environment.  ...  A series of web forms have been designed for modification of the rule bases and general system maintenance. III. BAYESIAN LEARNING IN MULTI-AGENT SYSTEMS A.  ... 
doi:10.3991/ijoe.v11i9.5071 fatcat:nucrdfuflbbmflh3tud3tt3uai

A Q-Learning-Based Approach for Simple and Multi-Agent Systems [chapter]

Ümit Ulusoy, Mehmet Serdar Güzel, Erkan Bostanci
2020 Multi Agent Systems - Strategies and Applications  
This study proposes different machine learning-based solutions to both single and multi-agent systems, took place on a 2-D simulation platform, namely, Robocode.  ...  The performance of the Q-Learning-based system and the supervised learning techniques are compared by employing different scenarios for this problem.  ...  Table 2 . 2 Configuration of ANN-based system. 9 A Q-Learning-Based Approach for Simple and Multi-Agent Systems DOI: Multi Agent Systems  ... 
doi:10.5772/intechopen.88484 fatcat:6htvjvsdqfexjly7c7s7qhkcnq

Leveraging cognitive context knowledge for argumentation based object classification in multi-sensor networks

Zhiyong Hao, Junfeng Wu, Tingting Liu, Xiaohong Chen
2019 IEEE Access  
To address this category of granularity inconsistent problem in multi-sensor collaborative object classification tasks, we propose a cognitive context knowledge-enriched method for classification conflict  ...  Therefore, it is suggested that people who can benefit from the proposed method in this paper are the human user of multi-sensor object classification systems, in which explaining decision support is one  ...  Considering multi-agent learning, an argumentation based framework for multi-agent inductive learning [17] is proposed by introducing dialogue game, to improve the performance of classification systems  ... 
doi:10.1109/access.2019.2919073 fatcat:azmkmxylcneppjga45mct7nmcq

A Systematic State-of-The-Art Analysis of Multi-Agent Intrusion Detection

I. A. Saeed, A. Selamat, M. Rohani, O. Krejcar, J. Chaudhry
2020 IEEE Access  
We are also grateful for the support of Ph.D. student Sebastien Mambou in for consultations regarding application aspects.  ...  The multi-agent system for attack classification based on a reputation algorithm was evaluated by using classification accuracy using and without using reputation [60] , [76] .  ...  data and learning effectively in a multi-agent environment [96] .  ... 
doi:10.1109/access.2020.3027463 fatcat:vaudgewisnhihghc4f3xa3otby

A Novel Deep Web Data Mining Algorithm based on Multi-Agent Information System and Collaborative Correlation Rule

Hongpu Sun, Qianru Hu
2016 International Journal of Future Generation Communication and Networking  
To enhance the traditional mining algorithms theoretically and numerically, we propose the novel deep web data mining algorithm based on multi-agent information system and collaborative correlation rule  ...  Then, we introduce the revised agent based algorithm.  ...  In this research, we propose the novel deep web data mining algorithm based on the multi-agent information system and the collaborative correlation rule.  ... 
doi:10.14257/ijfgcn.2016.9.11.08 fatcat:f6wojyxevfbnxgy4qlens4lp24

Reinforcement Learning over Knowledge Graphs for Explainable Dialogue Intent Mining

Kai Yang, Xinyu Kong, Yafang Wang, Jie Zhang, Gerard De Melo
2020 IEEE Access  
Finally, we consider a wide range of recently proposed knowledge graph-based recommender systems as baselines, mostly based on deep reinforcement learning and our method performs best.  ...  To address this, we propose a scheme to interpret the intent in multi-turn dialogue based on specific characteristics of the dialogue text.  ...  [17] proposed a knowledge graph question answering model based on end-to-end learning. [18] proposed a collaborative system which contains two agents.  ... 
doi:10.1109/access.2020.2991257 fatcat:wtgscficrzdozp25zy2arysxpi

Self-adaptive Support Vector Machine: A multi-agent optimization perspective

Nicolas Couellan, Sophie Jan, Tom Jorquera, Jean-Pierre Georgé
2015 Expert systems with applications  
Example 3: penalty parameters selection for multi-class learning In Example 3, we focus on multi-class learning.  ...  based descent, the AMAS system can be expressed in a very simple manner.  ... 
doi:10.1016/j.eswa.2015.01.028 fatcat:iirq7pyqwfb5zly4povrksjmoe

Scaling up deep reinforcement learning for multi-domain dialogue systems

Heriberto Cuayahuitl, Seunghak Yu, Ashley Williamson, Jacob Carse
2017 2017 International Joint Conference on Neural Networks (IJCNN)  
This paper proposes a three-stage method for multi-domain dialogue policy learning-termed NDQN, and applies it to an informationseeking spoken dialogue system in the domains of restaurants and hotels.  ...  In this method, the first stage does multi-policy learning via a network of DQN agents; the second makes use of compact state representations by compressing raw inputs; and the third stage applies a pre-training  ...  Network of Deep Q-Networks (NDQN) We propose to optimise multi-domain neural-based dialogue agents using a network of Deep Reinforcement Learners, for example a network of Deep Q-networks (DQN) -see [  ... 
doi:10.1109/ijcnn.2017.7966275 dblp:conf/ijcnn/CuayahuitlYWC17 fatcat:o4ijg3ryrfde5emfj2olnu62km

A Hybrid Algorithm in Reinforcement Learning for Crowd Simulation

2020 International journal of recent technology and engineering  
The proposed Hybrid Agent Reinforcement Learning (HARL) algorithm combines the Q-Learning off-policy value function and SARSA algorithm on-policy value function, which is used for dynamic crowd evacuation  ...  Exploiting the efficiency and stability of Dynamic Crowd, the paper proposes a hybrid crowd simulation algorithm that runs using multi agents and it mainly focuses on identifying the crowd to simulate.  ...  Therefore, a system with multiple agents' various semi-autonomous or autonomous modules is referred as multi-agent system.  ... 
doi:10.35940/ijrte.f9187.038620 fatcat:3a65mumhdzfchdtqvy4vrwkzku

An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Yan Li, Mengyu Zhao, Huazhi Zhang, Fuling Yang, Suyu Wang
2021 Electronics  
Most current studies on multi-agent evolution based on deep learning take a cooperative equilibrium strategy, while interactive self-learning is not always considered.  ...  An interactive self-learning game and evolution method based on non-cooperative equilibrium (ISGE-NCE) is proposed to take the benefits of both game theory and interactive learning for multi-agent confrontation  ...  Therefore, for the intelligent learning problem of multi-agent, a network framework based on DDPG can form a multi-agent interactive learning algorithm by assigning a set of DDPG networks to each agent  ... 
doi:10.3390/electronics10232977 fatcat:5cueeunuerbw5fehkqxrn6zehu

Papers by Title

2019 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)  
Multi-agent crowdsourcing model based on Q-learning Multi-agent Negotiation in Real-time Bidding Multi-Resident Activity Recognition using Multi-Label Classification in Ambient Sensing Smart Homes  ...  C D E F G H I L M N O P Q R S T U V W M 2 3 A B C D E F G H I L M N O P Q R S T U V W Machine Learning Based Handover Performance Improvement for LTE-R Machine Learning Techniques for Building and  ... 
doi:10.1109/icce-tw46550.2019.8991721 fatcat:62376ymadzge3g5xomicr5tesq
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