A Infectious Diseases Control Multi-Agent System using Artificial Intelligence Learning Algorithm

2019 ◽  
Vol 20 (10) ◽  
pp. 1925-1931
Author(s):  
Keun-Kwang Lee ◽  
Hee-Sook Kim ◽  
Min-Hi Lee
Keyword(s):  
Artificial Intelligence ◽  
Infectious Diseases ◽  
Learning Algorithm ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

Determination of Factors Affecting Safety Stress in Machine Design by Using Artificial Intelligence Technologies

2008 ◽  
Vol 144 ◽  
pp. 232-237
Author(s):  
Durmus Karayel ◽  
Sinan Serdar Ozkan ◽  
Fahri Vatansever
Keyword(s):  
Artificial Intelligence ◽  
Information Technologies ◽  
Intelligent System ◽  
System Model ◽  
Multi Agent System ◽  
Safety Factors ◽  
Agent System ◽  
Factors Affecting ◽  
Classical Design ◽  
Multi Agent

In this study, an intelligent system model that can evaluate experimental material properties and safety factors is developed. The model contains Artificial Intelligence Technologies such as Artificial Neural Network (ANN) and Fuzzy Logic. It consists of sub modules into interaction. Also, the model can obtain more precision values than interpolation techniques used to classical design. The study contributes to define safety factors, design criterions and safety stress according to a new approach based on information technologies. So, this study can be seen as one of the sub modules of Intelligence Multi Agent System and it can be integrated with Multi Agent System Model for design. Also, it can be used for classical design studies so that results can be quickly obtained. It is expected that this approach will be widely used by designers.

scholarly journals Multi-agent cooperation Q-learning algorithm based on constrained Markov Game

2020 ◽  
Vol 17 (2) ◽  
pp. 647-664
Author(s):  
Yangyang Ge ◽  
Fei Zhu ◽  
Wei Huang ◽  
Peiyao Zhao ◽  
Quan Liu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent System ◽  
Agent System ◽  
Action Function ◽  
Q Learning ◽  
State Action ◽  
Markov Game ◽  
Safety Constraints ◽  
Multi Agent

Multi-Agent system has broad application in real world, whose security performance, however, is barely considered. Reinforcement learning is one of the most important methods to resolve Multi-Agent problems. At present, certain progress has been made in applying Multi-Agent reinforcement learning to robot system, man-machine match, and automatic, etc. However, in the above area, an agent may fall into unsafe states where the agent may find it difficult to bypass obstacles, to receive information from other agents and so on. Ensuring the safety of Multi-Agent system is of great importance in the above areas where an agent may fall into dangerous states that are irreversible, causing great damage. To solve the safety problem, in this paper we introduce a Multi-Agent Cooperation Q-Learning Algorithm based on Constrained Markov Game. In this method, safety constraints are added to the set of actions, and each agent, when interacting with the environment to search for optimal values, should be restricted by the safety rules, so as to obtain an optimal policy that satisfies the security requirements. Since traditional Multi-Agent reinforcement learning algorithm is no more suitable for the proposed model in this paper, a new solution is introduced for calculating the global optimum state-action function that satisfies the safety constraints. We take advantage of the Lagrange multiplier method to determine the optimal action that can be performed in the current state based on the premise of linearizing constraint functions, under conditions that the state-action function and the constraint function are both differentiable, which not only improves the efficiency and accuracy of the algorithm, but also guarantees to obtain the global optimal solution. The experiments verify the effectiveness of the algorithm.

Reward-based epigenetic learning algorithm for a decentralised multi-agent system

2020 ◽  
Vol 8 (3) ◽  
pp. 201-224
Author(s):  
Faqihza Mukhlish ◽  
John Page ◽  
Michael Bain
Keyword(s):  
Swarm Robotics ◽  
Learning Algorithm ◽  
Future Research ◽  
Evolutionary Learning ◽  
Multi Agent System ◽  
Test Functions ◽  
Agent System ◽  
Content Type ◽  
Multi Agent ◽  
Future Research Directions

PurposeThis paper aims to propose a novel epigenetic learning (EpiLearn) algorithm, which is designed specifically for a decentralised multi-agent system such as swarm robotics.Design/methodology/approachFirst, this paper begins with overview of swarm robotics and the challenges in designing swarm behaviour automatically. This should indicate the direction of improvements required to enhance an automatic swarm design. Second, the evolutionary learning (EpiLearn) algorithm for a swarm system using an epigenetic layer is formulated and discussed. The algorithm is then tested through various test functions to investigate its performance. Finally, the results are discussed along with possible future research directions.FindingsThrough various test functions, the algorithm can solve non-local and many local minima problems. This article also shows that by using a reward system, the algorithm can handle the deceptive problem which often occurs in dynamic problems. Moreover, utilization of rewards from the environment in the form of a methylation process on the epigenetic layer improves the performance of traditional evolutionary algorithms applied to automatic swarm design. Finally, this article shows that a regeneration process that embeds an epigenetic layer in the inheritance process performs better than a traditional crossover operator in a swarm system.Originality/valueThis paper proposes a novel method for automatic swarm design by taking into account the importance of multi-agent settings and environmental characteristics surrounding the swarm. The novel evolutionary learning (EpiLearn) algorithm using an epigenetic layer gives the swarm the ability to perform co-evolution and co-learning.

scholarly journals AiD-EM: Adaptive Decision Support for Electricity Markets Negotiations

2019 ◽  
Author(s):  
Tiago Pinto ◽  
Zita Vale
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Electricity Markets ◽  
Evolutionary Computing ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Based ◽  
Multi Agent ◽  
Competitive Electricity Markets

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

scholarly journals Extreme Learning Machine Neural Networks for Multi-Agent System in Power Generation

2018 ◽  
Vol 7 (4.35) ◽  
pp. 347
Author(s):  
Chong Tak Yaw ◽  
Shen Yuong Wong ◽  
Keem Siah Yap ◽  
Chin Hooi Tan
Keyword(s):  
Power Generation ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Satellite Image ◽  
Multi Agent System ◽  
Classification Problems ◽  
Agent System ◽  
Individual Agent ◽  
Multi Agent ◽  
Learning Machine

Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. The hidden neurons are optional in neuron alike whereas the weights are the criteria required to study the linking among the output layer as well as hidden layers. On the other hand, the ensemble model to integrate every independent prediction of several ELMs to produce a final output. This particular approach was included in a Multi-Agent System (MAS). By hybrid those two approached, a novel extreme learning machine based multi-agent systems (ELM-MAS) for handling classification problems is presented in this paper. It contains two layers of ELMs, i.e., individual agent layer and parent agent layer. Several activation functions using benchmark datasets and real-world applications, i.e., satellite image, image segmentation, fault diagnosis in power generation (including circulating water systems as well as GAST governor) were used to test the ELM-MAS developed. Our experimental results suggest that ELM-MAS is capable of achieving good accuracy rates relative to others approaches.

Summary on Research of Agent and Multi-Agent System

2012 ◽  
Vol 542-543 ◽  
pp. 1380-1383 ◽  
Author(s):  
You Jie Ma ◽  
Fan Ting Kong ◽  
Xue Song Zhou
Keyword(s):  
Artificial Intelligence ◽  
Development Trend ◽  
Distributed Artificial Intelligence ◽  
Multi Agent System ◽  
Coordination Problem ◽  
Agent System ◽  
Artificial Intelligence Research ◽  
Multi Agent ◽  
The Development Trend ◽  
Important Branch

Distributed artificial intelligence is an important branch of the artificial intelligence， Agent and Multi-agent system are important aspects of distributed artificial intelligence research. This paper mainly introduce some research and exiting problems about Agent and Multi-agent system in recent years, including the Agent concept, characteristics and structure, Multi-agent system concept, structure and the coordination problem. Finally, I look forward to the development trend of Agent and Multi-agent system.

Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security

2018 ◽  
Vol 86 ◽  
pp. 1106-1117 ◽  
Author(s):  
Daniel Grzonka ◽  
Agnieszka Jakóbik ◽  
Joanna Kołodziej ◽  
Sabri Pllana
Keyword(s):  
Artificial Intelligence ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document