Representational axes and temporal cooperative processes
Vision, brain, and cooperative computation
From image sequences towards conceptual descriptions
Image and Vision Computing
Fundamentals of matrix computations
Fundamentals of matrix computations
Intelligence without representation
Artificial Intelligence
The computational perception of scene dynamics
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Agent interoperation across multiagent system boundaries
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Whistling in the dark: cooperative trail following in uncertain localization space
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Protocols and intentional specifications of multi-party agent conversions for brokerage and auctions
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Derivation of glue code for agent interoperation
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
IEEE Intelligent Systems
Temporal scene analysis: conceptual descriptions of object movements.
Temporal scene analysis: conceptual descriptions of object movements.
An Ontology for Observation of Multiagent Based Simulation
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
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The web of interconnected intelligent software agents as well as intelligent hardware agents will be seamlessly embedded in everywhere of our lives and constantly sensing and reacting to the environment. The dynamic and heterogeneous interactions among these agents will provide great opportunities for agent-based services. One of the challenging issues in this agent-based service environment is the task of collective perception: how to make sense of complex sensed data at the conceptual level by a group of collaborative agents. This paper proposes a strategy for collective perception when the agents involved may not share the same knowledge representation or ontology. To avoid the syntax, semantics, and ontological complexities in communicating and understanding among agents, the synthesizing agent collects only the analyzed and categorized results from other agents in the form of a natural number or a vector of natural numbers. It then perform collective perception on top of these categorized results. An eigenspace method is proposed to model and perceive events. Experimental results are presented to show the effectiveness of our mechanism.