Cooperative Distributed Problem Solving in Epsilon

ESPRIT ’90 ◽  
1990 ◽  
pp. 177-193
Author(s):  
Günter Kniesel ◽  
Armin B. Cremers
Keyword(s):  
Problem Solving ◽  
Distributed Problem Solving ◽  
Cooperative Distributed Problem Solving

Trends in cooperative distributed problem solving

1989 ◽  
Vol 1 (1) ◽  
pp. 63-83 ◽  
Author(s):  
E.H. Durfee ◽  
V.R. Lesser ◽  
D.D. Corkill
Keyword(s):  
Problem Solving ◽  
Distributed Problem Solving ◽  
Cooperative Distributed Problem Solving

Cooperative Spatial Reasoning for Image Understanding

1997 ◽  
Vol 11 (01) ◽  
pp. 205-227 ◽  
Author(s):  
Takashi Matsuyama ◽  
Toshikazu Wada
Keyword(s):  
Problem Solving ◽  
Intelligent Agents ◽  
Spatial Reasoning ◽  
Spatial Information ◽  
Spatial Relations ◽  
Image Understanding ◽  
Aerial Image ◽  
Region Segmentation ◽  
Distributed Problem Solving ◽  
Cooperative Distributed Problem Solving

Spatial Reasoning, reasoning about spatial information (i.e. shape and spatial relations), is a crucial function of image understanding and computer vision systems. This paper proposes a novel spatial reasoning scheme for image understanding and demonstrates its utility and effectiveness in two different systems: region segmentation and aerial image understanding systems. The scheme is designed based on a so-called Multi-Agent/Cooperative Distributed Problem Solving Paradigm, where a group of intelligent agents cooperate with each other to fulfill a complicated task. The first part of the paper describes a cooperative distributed region segmentation system, where each region in an image is regarded as an agent. Starting from seed regions given at the initial stage, region agents deform their shapes dynamically so that the image is partitioned into mutually disjoint regions. The deformation of each individual region agent is realized by the snake algorithm14 and neighboring region agents cooperate with each other to find common region boundaries between them. In the latter part of the paper, we first give a brief description of the cooperative spatial reasoning method used in our aerial image understanding system SIGMA. In SIGMA, each recognized object such as a house and a road is regarded as an agent. Each agent generates hypotheses about its neighboring objects to establish spatial relations and to detect missing objects. Then, we compare its reasoning method with that used in the region segmentation system. We conclude the paper by showing further utilities of the Multi-gent/Cooperative Distributed Problem Solving Paradigm for image understanding.

Design and Implementation of an Object-Oriented Cooperative Distributed Problem Solving Shell

1996 ◽  
Author(s):  
Feng-Chao Yang ◽  
Yu-Kuen Ho
Keyword(s):  
Problem Solving ◽  
Layer Structure ◽  
Group Structure ◽  
Object Oriented ◽  
Distributed Artificial Intelligence ◽  
Prototype System ◽  
Test Bed ◽  
Application Systems ◽  
Distributed Problem Solving ◽  
Cooperative Distributed Problem Solving

Abstract In this paper an Object-Oriented Cooperative Distributed Problem Solving Shell (OOCDPSS) is presented. It is a convenient and effective building tool for the cooperative multi-agent application systems under distributed computer environment. By the Distributed Artificial Intelligence technique and the Object-Oriented concept, each agent is constructed by multi-layer structure to reduce the design complexity and give more flexibility, and they are arranged as a recurrent group structure for run-time effective management. Additionally, the idea of mediator is adopted to integrate new information systems with the existing inflexible legacy system, and the distributed whiteboard agent is used for the information interchange among the agents. This prototype system has been completed and used as the test bed of our other laboratory projects.

Sign in / Sign up

Export Citation Format

Share Document