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.

scholarly journals Can models of agents be transferred between different areas?

2000 ◽  
Vol 15 (2) ◽  
pp. 197-203 ◽  
Author(s):  
RUTH AYLETT ◽  
KERSTIN DAUTENHAHN ◽  
JIM DORAN ◽  
MICHAEL LUCK ◽  
SCOTT MOSS ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Multi Agent Systems ◽  
Ongoing Research ◽  
Agent Based ◽  
Agent Systems ◽  
Sustained Activity ◽  
Diverse Environment ◽  
The World ◽  
Multi Agent

One of the main reasons for the sustained activity and interest in the field of agent-based systems, apart from the obvious recognition of its value as a natural and intuitive way of understanding the world, is its reach into very many different and distinct fields of investigation. Indeed, the notions of agents and multi-agent systems are relevant to fields ranging from economics to robotics, in contributing to the foundations of the field, being influenced by ongoing research, and in providing many domains of application. While these various disciplines constitute a rich and diverse environment for agent research, the way in which they may have been linked by it is a much less considered issue. The purpose of this panel was to examine just this concern, in the relationships between different areas that have resulted from agent research. Informed by the experience of the participants in the areas of robotics, social simulation, economics, computer science and artificial intelligence, the discussion was lively and sometimes heated.

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.

Multi-Agent Systems Integration in Enterprise Environments Using Web Services

2008 ◽  
pp. 174-189 ◽  
Author(s):  
Eduardo H. Ramírez ◽  
Ramón F. Brena
Keyword(s):  
Web Services ◽  
Case Studies ◽  
Software Agents ◽  
Systems Integration ◽  
Enterprise Systems ◽  
Multi Agent Systems ◽  
Time To Market ◽  
Agent Based ◽  
Agent Systems ◽  
Multi Agent

In this paper we present a decoupled architectural approach that allows software agents to interoperate with enterprise systems using Web services. The solution leverages existing technologies and standards in order to reduce the time-to-market and increase the adoption of agent-based applications. We present case studies of applications that have been enhanced by our proposal.

scholarly journals AGENT FABRICATION AND ITS IMPLEMENTATION FOR AGENT-BASED ELECTRONIC COMMERCE

2002 ◽  
Vol 01 (03) ◽  
pp. 473-489 ◽  
Author(s):  
SHENG-UEI GUAN ◽  
FANGMING ZHU
Keyword(s):  
Electronic Commerce ◽  
Potential Role ◽  
Software Agents ◽  
Conceptual Structure ◽  
Potential Solution ◽  
Next Generation ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
Multi Agent

In the last decade, agent-based e-commerce has emerged as a potential role for the next generation of e-commerce. How to create agents for e-commerce applications has become a serious consideration in this field. This paper proposes a new scheme named agent fabrication and elaborates its implementation in multi-agent systems based on the SAFER (Secure Agent Fabrication, Evolution and Roaming) architecture. First, a conceptual structure is proposed for software agents carrying out e-commerce activities. Furthermore, agent module suitcase is defined to facilitate agent fabrication. With these definitions and facilities in the SAFER architecture, the formalities of agent fabrication are elaborated. In order to enhance the security of agent-based e-commerce, an infrastructure of agent authorization and authentication is integrated in agent fabrication. Implementation and prototype applications show that the proposed agent fabrication scheme brings forth a potential solution for creating agents in agent-based e-commerce applications.

scholarly journals Commitments and conventions: The foundation of coordination in multi-agent systems

1993 ◽  
Vol 8 (3) ◽  
pp. 223-250 ◽  
Author(s):  
Nick R. Jennings
Keyword(s):  
Artificial Intelligence ◽  
Individual Performance ◽  
Distributed Artificial Intelligence ◽  
Multi Agent Systems ◽  
Diverse Range ◽  
Agent Systems ◽  
Course Of Action ◽  
Central Hypothesis ◽  
Multi Agent ◽  
Artificial Intelligence Systems

AbstractDistributed Artificial Intelligence systems, in which multiple agents interact to improve their individual performance and to enhance the systems' overall utility, are becoming an increasingly pervasive means of conceptualising a diverse range of applications. As the discipline matures, researchers are beginning to strive for the underlying theories and principles which guide the central processes of coordination and cooperation. Here agent communities are modelled using a distributed goal search formalism, and it is argued thatcommitments(pledges to undertake a specific course of action) andconventions(means of monitoring commitments in changing circumstances) are the foundation of coordination in multi-agent systems. An analysis of existing coordination models which use concepts akin to commitments and conventions is undertaken before a new unifying framework is presented. Finally, a number of prominent coordination techniques which do notexplicitlyinvolve commitments or conventions are reformulated in these terms to demonstrate their compliance with the central hypothesis of this paper.

scholarly journals Monitoring norms: a multi-disciplinary perspective

2018 ◽  
Vol 33 ◽  
Author(s):  
Mehdi Dastani ◽  
Paolo Torroni ◽  
Neil Yorke-Smith
Keyword(s):  
Artificial Intelligence ◽  
Cultural Studies ◽  
Computer Security ◽  
Social Setting ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Organizational Behaviour ◽  
Disciplinary Perspective ◽  
Multi Agent ◽  
Key Questions

AbstractThe concept of anormis found widely across fields including artificial intelligence, biology, computer security, cultural studies, economics, law, organizational behaviour and psychology. The concept is studied with different terminology and perspectives, including individual, social, legal and philosophical. If a norm is an expected behaviour in a social setting, then this article considers how it can be determined whether an individual is adhering to this expected behaviour. We call this processmonitoring, and again it is a concept known with different terminology in different fields. Monitoring of norms is foundational for processes of accountability, enforcement, regulation and sanctioning. Starting with a broad focus and narrowing to the multi-agent systems literature, this survey addresses four key questions: what is monitoring, what is monitored, who does the monitoring and how the monitoring is accomplished.

scholarly journals Use of the Multi-Agent Paradigm in Sustainable Tourism

2018 ◽  
Vol 7 (1) ◽  
pp. 5-24 ◽  
Author(s):  
Martina Husáková
Keyword(s):  
Complex Systems ◽  
Sustainable Tourism ◽  
Multi Agent Systems ◽  
Economic Prosperity ◽  
Cultural Economic ◽  
Bottom Up ◽  
Agent Based ◽  
Agent Systems ◽  
Multi Agent ◽  
In Silico Models

Abstract Complex systems are characterised by a huge amount of components, which are highly linked with each other. Tourism is one of the examples of complex systems collecting various activities leading to the enrichment of travellers in the view of receiving new experiences and increasing economic prosperity of specific destinations. The complex systems can be investigated with various bottom-up and top-down approaches. The multi-agent-based modelling is the bottom-up approach that is focused on the representation of individual entities for the exploration of possible interactions among them and their effects on surrounding environments. These systems are able to integrate knowledge of socio-cultural, economic, physical, biological or environmental systems for in-silico models development, which can be used for experimentation with a system. The main aim of the presented text is to introduce links between tourism, complexity and to advocate usefulness of the multi-agent-based systems for the exploration of tourism and its sustainability. The evaluation of suitability of the multi-agent systems in tourism is based on the investigation of fundamental characteristics of these two systems and on the review of specific applications of the multi-agent systems in sustainable tourism.

Sign in / Sign up

Export Citation Format

Share Document