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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.

Rule Responder is a Pragmatic Web infrastructure for distributed rule-based event processing multi-agent eco-systems. This allows specifying virtual organizations — with their shared and individual (semantic and pragmatic) contexts, decisions, and actions/events for rule-based collaboration between the distributed members. The (semi-)autonomous agents use rule engines and Semantic Web rules to describe and execute derivation and reaction logic which declaratively implements the organizational semiotics and the different distributed system/agent topologies with their negotiation/coordination mechanisms. They employ ontologies in their knowledge bases to represent semantic domain vocabularies, normative pragmatics and pragmatic context of event-based conversations and actions.

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-Agent/Cooperative Distributed Problem Solving Paradigm for image understanding.

A multi-agent system (MAS) is usually used in cases where the problem is complex, data is decentralized, and computation is asynchronous. And the complexity, capability, and robust nature of natural immune systems make artificial immune systems an inviting choice as a platform for developing fault diagnosis systems based on MAS. These properties are pertinent to fault diagnosis systems. In this paper, a novel approach to AIS based MAS fault diagnosis methodology for complex system is presented. The diagnosis scheme consist three kinds of agents: detecting agent, vaccine generation agent and interface agent. Through the three kinds of agent cooperating, it is proved that the system has the capabilities of robust, continuous study and effectiveness.