An Image-Based Vocabulary Learning System Based on Multi-Agent System

2019 ◽  
Author(s):  
Preecha Tangworakitthaworn ◽  
Preeyapol Owatsuwan ◽  
Nutsima Nongyai ◽  
Nongnapas Arayapong
Keyword(s):  
Vocabulary Learning ◽  
Learning System ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

scholarly journals PACA-ITS: A Multi-Agent System for Intelligent Virtual Laboratory Courses

2019 ◽  
Vol 9 (23) ◽  
pp. 5084
Author(s):  
Saima Munawar ◽  
Saba Khalil Toor ◽  
Muhammad Aslam ◽  
Esma Aimeur
Keyword(s):  
Practical Knowledge ◽  
Unified Modeling Language ◽  
Cognitive Model ◽  
Learning System ◽  
Virtual Laboratory ◽  
Multi Agent System ◽  
Agent System ◽  
Laboratory Courses ◽  
Development Framework ◽  
Multi Agent

This paper describes an intensive design leading to the implementation of an intelligent lab companion (ILC) agent for an intelligent virtual laboratory (IVL) platform. An IVL enables virtual labs (VL) to be used as online research laboratories, thereby facilitating and improving the analytical skills of students using agent technology. A multi-agent system enhances the capability of the learning system and solves students’ problems automatically. To ensure an exhaustive Agent Unified Modeling Language (AUML) design, identification of the agents’ types and responsibilities on well-organized AUML strategies is carried out. This work also traces the design challenge of IVL modeling and the ILC agent functionality of six basic agents: the practical coaching agent (PCA), practical dispatcher agent (PDA), practical interaction and coordination agent (PICA), practical expert agent (PEA), practical knowledge management agent (PKMA), and practical inspection agent (PIA). Furthermore, this modeling technique is compatible with ontology mapping based on an enabling technology using the Java Agent Development Framework (JADE), Cognitive Tutor Authoring Tools (CTAT), and Protégé platform integration. The potential Java Expert System Shell (Jess) programming implements the cognitive model algorithm criteria that are applied to measure progress through the CTAT for C++ programming concept task on IVL and successfully deployed on the TutorShop web server for evaluation. The results are estimated through the learning curve to assess the preceding knowledge, error rate, and performance profiler to engage cognitive Jess agent efficiency as well as practicable and active decisions to improve student learning.

Acquisition of Competitive Behaviors in Multi-Agent System Based on a Modular Learning System

2009 ◽  
Vol 27 (3) ◽  
pp. 350-357 ◽  
Author(s):  
Yasutake Takahashi ◽  
Kazuhiro Edazawa ◽  
Kentaro Noma ◽  
Minoru Asada
Keyword(s):  
Learning System ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

scholarly journals Research on decision-making strategy of soccer robot based on multi-agent reinforcement learning

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091696
Author(s):  
Xiaoli Liu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Division Of Labour ◽  
Learning System ◽  
Selection Strategy ◽  
Multi Agent System ◽  
Soccer Robot ◽  
Agent System ◽  
Multi Agent

This article studies a multi-agent reinforcement learning algorithm based on agent action prediction. In multi-agent system, the action of learning agent selection is inevitably affected by the action of other agents, so the reinforcement learning system needs to consider the joint state and joint action of multi-agent based on this. In addition, the application of this method in the cooperative strategy learning of soccer robot is studied, so that the multi-agent system can pass through the environment. To realize the division of labour and cooperation of multi-robots, the interactive learning is used to master the behaviour strategy. Combined with the characteristics of decision-making of soccer robot, this article analyses the role transformation and experience sharing of multi-agent reinforcement learning, and applies it to the local attack strategy of soccer robot, uses this algorithm to learn the action selection strategy of the main robot in the team, and uses Matlab platform for simulation verification. The experimental results prove the effectiveness of the research method, and the superiority of the proposed method is validated compared with some simple methods.

scholarly journals A Multi-Agent Based Intelligent Training System for Unmanned Surface Vehicles

2019 ◽  
Vol 9 (6) ◽  
pp. 1089 ◽  
Author(s):  
Wei Han ◽  
Bing Zhang ◽  
Qianyi Wang ◽  
Jun Luo ◽  
Weizhi Ran ◽  
...  
Keyword(s):  
Fuzzy Rule ◽  
Training System ◽  
Learning System ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Agent Based ◽  
Unmanned Surface Vehicles ◽  
Multi Agent ◽  
Cooperative Decision Making

The modeling and design of multi-agent systems is imperative for applications in the evolving intelligence of unmanned systems. In this paper, we propose a multi-agent system design that is used to build a system for training a team of unmanned surface vehicles (USVs) where no historical data concerning the behavior is available. In this approach, agents are built as the physical controller of each USV and their cooperative decisions used for the USVs’ group coordination. To make our multi-agent system intelligently coordinate USVs, we built a multi-agent-based learning system. First, an agent-based data collection platform is deployed to gather competition data from agents’ observation for on-line learning tasks. Second, we design a genetic-based fuzzy rule training algorithm that is capable of optimizing agents’ coordination decisions in an accumulated manner. The simulation results of this study demonstrate that our proposed training approach is feasible and able to converge to a stable action selection policy towards efficient multi-USVs’ cooperative decision making.

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