Personal Tour

2012 ◽  
pp. 178-189
Author(s):  
Fabiana Lorenzi ◽  
Stanley Loh ◽  
Mara Abel
Keyword(s):  
Artificial Intelligence ◽  
Incomplete Information ◽  
Recommender System ◽  
Local Knowledge ◽  
Knowledge Bases ◽  
Distributed Artificial Intelligence ◽  
Multiple Agents ◽  
Multi Agent ◽  
User Recommendation

This chapter describes the Personal Tour: a multi-agent recommender system designed to help users to find best travel packages according to their preferences. Personal Tour is based on the collaboration of multiple agents exchanging information stored in their local knowledge bases. Based on the paradigm of the Distributed Artificial Intelligence, a user recommendation request is divided into partial recommendations handled by different agents, each one maintaining incomplete information that may be useful to compose a recommendation.

On distributed artificial intelligence

1988 ◽  
Vol 3 (1) ◽  
pp. 21-57 ◽  
Author(s):  
Luis Eduardo ◽  
Castillo Hern
Keyword(s):  
Artificial Intelligence ◽  
Conceptual Model ◽  
Intelligent Agents ◽  
Distributed Artificial Intelligence ◽  
Agent Interaction ◽  
Multi Agent

AbstractDistributed Artificial Intelligence has been loosely defined in terms of computation by distributed, intelligent agents. Although a variety of projects employing widely ranging methodologies have been reported, work in the field has matured enough to reveal some consensus about its main characteristics and principles. A number of prominent projects are described in detail, and two general frameworks, theSystem conceptual modeland theagent conceptual model, are used to compare the different approaches. The paper concludes by reviewing approaches to formalizing some of the more critical capabilities required by multi-agent interaction.

scholarly journals Self-Adaptive Swarm System (SASS)

2021 ◽  
Author(s):  
Qin Yang
Keyword(s):  
Artificial Intelligence ◽  
Distributed Artificial Intelligence ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Collective Decisions ◽  
Fourth Level ◽  
Working Together ◽  
Multi Agent ◽  
Communication Planning ◽  
Self Adaptive

Distributed artificial intelligence (DAI) studies artificial intelligence entities working together to reason, plan, solve problems, organize behaviors and strategies, make collective decisions and learn. This Ph.D. research proposes a principled Multi-Agent Systems (MAS) cooperation framework -- Self-Adaptive Swarm System (SASS) -- to bridge the fourth level automation gap between perception, communication, planning, execution, decision-making, and learning.

scholarly journals Development strategy formation of the Committee on Artificial Intelligence in the Scientific and Educational Center "Engineering of the Future"

2021 ◽  
Vol 11 (3) ◽  
pp. 260-293
Author(s):  
I.I. Barinov ◽  
◽  
N.M. Borgest ◽  
S.Y. Borovik ◽  
O.N. Granichin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Development Strategy ◽  
Knowledge Bases ◽  
Enterprise Management ◽  
High Tech ◽  
Digital Twins ◽  
Management Processes ◽  
The Future ◽  
Multi Agent ◽  
A Chain

The Scientific and Educational Center "Engineering of the Future", created on the basis of the Institute of Regional Development of the Samara Region, has formed a number of important sectoral and subject committees, in which it is planned to develop breakthrough technologies in high-tech industries. The Committee on Artificial Intelligence, organized within the framework of the SEC "Engineering of the Future", forms its development strategy. The article outlines the vision for the prospects of such a strategy of the project team, consisting of specialists from universities, academia, design organizations, commercial companies and startups. The key in the proposed strategy is emergent artificial intelligence - it is a spontaneously arising, under the influence of external events or from internal causes or motives, a chain of coordinated state changes by agents who find a solution to a new problem or increase the value of an existing solution. The authors believe that the construction of emergent artificial intelligence is based on multi-agent technologies and ontologies of subject areas. The article formulates the main tasks of the Committee for the coming years and presents a technological project. The project includes the creation of mass production of intelligent resource management systems, personalized by creating digital twins of enterprise management processes, knowledge bases and multi-agent technologies. The essence of the proposed project, reflecting the important priorities of industrial partners, is to create a line of intelligent products and services for all stages of the life cycle of complex high-tech products and build a "factory" of such systems in the form of an open instrumental platform that will allow these enterprises to reduce dependence on the solution provider and on their own develop and modernize such systems. The principles of the Committee's work were proposed, its first potential projects and planned cooperation on these projects to achieve the first practical results were considered.

Power System Based on Multi-Agent Technology

2013 ◽  
Vol 273 ◽  
pp. 276-279
Author(s):  
Xiao Zhou Fan
Keyword(s):  
Artificial Intelligence ◽  
Power System ◽  
Open System ◽  
Large Scale ◽  
Distributed Artificial Intelligence ◽  
Computer Networking ◽  
Agent Technology ◽  
Large Power ◽  
Complex Problems ◽  
Multi Agent

Multi-agent technology combines the technology of computer, networking and distributed artificial intelligence together which is good at solving complex problems about large-scale distributed and open system. Multi-agent technology provides new ways for power system studies. This paper aims at finding a way to rationally use the multi-agent technology to solve complex problems due to improper protection or slow even wrong actions in the large power system.

scholarly journals A2CM: a new multi-agent algorithm

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 28
Author(s):  
Gabor Paczolay ◽  
Istvan Harmati
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Multiple Agents ◽  
Pursuit Evasion ◽  
The World ◽  
Multi Agent ◽  
New Algorithms ◽  
Modified Algorithm

<p class="Abstract">Reinforcement learning is currently one of the most researched fields of artificial intelligence. New algorithms are being developed that use neural networks to compute the selected action, especially for deep reinforcement learning. One subcategory of reinforcement learning is multi-agent reinforcement learning, in which multiple agents are present in the world. As it involves the simulation of an environment, it can be applied to robotics as well. In our paper, we use our modified version of the advantage actor–critic (A2C) algorithm, which is suitable for multi-agent scenarios. We test this modified algorithm on our testbed, a cooperative–competitive pursuit–evasion environment, and later we address the problem of collision avoidance.</p>

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.

Distributed Remote Monitoring System Key Technology Research on Agent

2013 ◽  
Vol 860-863 ◽  
pp. 2774-2782
Author(s):  
Peng Liang Lv ◽  
Guo Shun Chen
Keyword(s):  
Artificial Intelligence ◽  
Traffic Control ◽  
Computer Network ◽  
Distributed Artificial Intelligence ◽  
Agent Technology ◽  
High Tech ◽  
Technology Research ◽  
Artificial Intelligence Research ◽  
Remote Monitoring System ◽  
Multi Agent

Recent years, as computer science and the development of high-tech communications networks, distributed artificial intelligence field of artificial intelligence research as an important branch, more and more industry attention. One of the Agent and Multi-Agent technology research became a hot topic of distributed artificial intelligence research. Research on Multi-Agent technology is mainly a group of autonomous distributed open Agent in a dynamic environment, through interaction, cooperation, competition, negotiation and other acts perform complex control or task solving. Because of its better reflect human social intelligence, more suitable for an open, dynamic social environment, thus causing researchers in various fields of interest, and is widely used in scientific computing, computer network e-commerce, business management and traffic control, etc

ViSe2 - An Agent-Based Expert Consulting System with Efficient Cooperation

1998 ◽  
Vol 2 (3) ◽  
pp. 104-110
Author(s):  
Cheng-Gang Bian ◽  
◽  
Wen Cao ◽  
Gunnar Hartvigsen
Keyword(s):  
Artificial Intelligence ◽  
Software Agents ◽  
Distributed Artificial Intelligence ◽  
Multi Agent Systems ◽  
Current Version ◽  
Distributed Knowledge ◽  
Agent Based ◽  
Agent Systems ◽  
Distributed Problem Solving ◽  
Multi Agent

ViSe2 l is an expert consulting system which employs software agents to manage distributed knowledge sources. These individual software agents solve users’ problems either by themselves or via cooperation. The efficiency of cooperation plays a serious role in Distributed Problem Solving (DPS) and Multi-Agent Systems (MAS). We have focused on the development of a twin-base approach for agents to model the capabilities of each other, and thus achieve efficient cooperation. The current version of the ViSe2 implementation is an experimental model of an agent-based expert system. Compared with other cooperation approaches in Distributed Artificial Intelligence (DAI) area, the results received so far indicate that the ViSe2 agents serve their users in an efficient cooperation manner.

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