Distributed Problem Solving using Evolutionary Learning in Multi-Agent Systems

2007 ◽  
pp. 211-227 ◽  
Author(s):  
Dipti Srinivasan ◽  
Min Chee Choy
Keyword(s):  
Problem Solving ◽  
Evolutionary Learning ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Distributed Problem Solving ◽  
Multi Agent

Society of Agents

2020 ◽  
pp. 160-183
Author(s):  
Steven Walczak
Keyword(s):  
Problem Solving ◽  
Information Sharing ◽  
Multi Agent Systems ◽  
New Classification ◽  
Conceptual Foundation ◽  
Agent Systems ◽  
Multi Agent ◽  
Coordination And Collaboration ◽  
Agent Coordination ◽  
New Framework

The development of multiple agent systems faces many challenges, including agent coordination and collaboration on tasks. Minsky's The Society of Mind provides a conceptual view for addressing these multi-agent system problems. A new classification ontology is introduced for comparing multi-agent systems. Next, a new framework called the Society of Agents is developed from Minsky's conceptual foundation. A Society of Agents framework-based problem-solving and a Game Society is developed and applied to the domain of single player logic puzzles and two player games. The Game Society solved 100% of presented Sudoku and Kakuro problems and never lost a tic-tac-toe game. The advantage of the Society of Agents approach is the efficient re-utilization of agents across multiple independent game domain problems and a centralized problem-solving architecture with efficient cross-agent information sharing.

Society of Agents

2018 ◽  
Vol 14 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Problem Solving ◽  
Multi Agent Systems ◽  
New Classification ◽  
Conceptual Foundation ◽  
Agent Systems ◽  
Multi Agent ◽  
Coordination And Collaboration ◽  
Agent Coordination ◽  
New Framework ◽  
Society Of Mind

The development of multiple agent systems faces many challenges, including agent coordination and collaboration on tasks. Minsky's The Society of Mind provides a conceptual view for addressing these multi-agent system problems. A new classification ontology is introduced for comparing multi-agent systems. Next, a new framework called the Society of Agents is developed from Minsky's conceptual foundation. A Society of Agents framework-based problem-solving and a Game Society is developed and applied to the domain of single player logic puzzles and two player games. The Game Society solved 100% of presented Sudoku and Kakuro problems and never lost a tic-tac-toe game. The advantage of the Society of Agents approach is the efficient re-utilization of agents across multiple independent game domain problems and a centralized problem-solving architecture with efficient cross-agent information sharing.

Strategic Reorganization in Multi-Agent Systems: Inspired by Intelligent Human Organizations

2014 ◽  
Vol 23 (04) ◽  
pp. 1450009 ◽  
Author(s):  
Maryamossadat N. Mahani ◽  
Arvin Agah
Keyword(s):  
Organization Theory ◽  
Organizational Design ◽  
Operative Management ◽  
Organizational Control ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Performance Improvements ◽  
Distributed Problem Solving ◽  
Multi Agent ◽  
Strategic Reorganization

The field of organizational design in multi-agent systems is still in need of powerful tools and methods for enabling effective design, control, and transformation of organizations of different kinds. In this work, we address the problem of reorganization in a distributed problem solving model as a typical multi-agent system application. We look into adaptation of concepts and theories from social organization theory. In particular, we get insights from Schwaninger's model of Intelligent Human Organizations. To this goal, we implement and utilize a multi-level organizational control model which uses a strategic management and an operative management layer to exert organizational control and also to make interactions between these two levels of control possible. Experimental evaluations are performed using a modified model of pursuit game. The results indicate that the proposed model allows the system to stay ahead of the organizational change, resulting in performance improvements, despite the additional costs associated to reorganization.

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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