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Proceedings ArticleDOI

Towards Adaptive Multi-agent Planning in Cyber Physical Space

TLDR
The paper proposes a novel approach of generating online adaptive response in assisting search-and-rescue operations using situation awareness built from real-time heterogeneous spatio-temporal data streams.
Abstract
Cyber physical space is potentially hosting innumerable spatio-temporal data streams due to increasing use of social networking platforms as real-time information dissemination system and world-wide deployment of sensors for continuous monitoring of physical phenomena. In this paper we address the problem of how cyber physical space can be used for sensing and responding to global calamities such as earthquake. The paper proposes a novel approach of generating online adaptive response in assisting search-and-rescue operations using situation awareness built from real-time heterogeneous spatio-temporal data streams. Online adaptive response is achieved by using agent-based cooperative task sharing and modeling agent decision making as self-organized emergent behavior based on concepts of complex adaptive system. An implemented simulation platform use concepts of situation modeling, domain task network, contract net protocol based negotiation and complex adaptive system to generate adaptive plans. Preliminary simulation results are promising as we have been able to demonstrate a repertoire of self-organized emergent behaviors.

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Citations
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Semantic Sensor Web

TL;DR: The semantic sensor Web (SSW) proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge.
Proceedings ArticleDOI

Exploring Bio-inspired Self-Organization for Developing Emergent Intelligence in Urban Super Organism

TL;DR: A novel unified model of bio-inspired mechanism for modeling spatially situated pervasive computing scenarios and exploring emergence of self-adaptive behaviors in individuals within urban super organism is proposed and results demonstrate efficacy of the proposed approach.
Proceedings ArticleDOI

Framework for analysis of bio-inspired self-organization in urban pervasive system

TL;DR: This paper proposes a formal framework of bio-inspired model, including details of agent model, formal algorithm for distributed coordination and theoretical analysis of the algorithm, and presents simulation results to demonstrate adaptability, robustness and scalability of proposed algorithm.
References
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Journal ArticleDOI

Toward a Theory of Situation Awareness in Dynamic Systems

TL;DR: A theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains is presented and design implications for enhancing operator situation awareness and future directions for situation awareness research are explored.
Journal ArticleDOI

The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver

TL;DR: In this article, the contract net protocol has been developed to specify problem-solving communication and control for nodes in a distributed problem solver, where task distribution is affected by a negotiation process, a discussion carried on between nodes with tasks to be executed and nodes that may be able to execute those tasks.
Journal ArticleDOI

Semantic Sensor Web

TL;DR: In this paper, the semantic sensor web (SSW) proposes that sensor data be annotated with semantic metadata that will both increase interoperability and provide contextual information essential for situational knowledge.
ReportDOI

Irreducible Semi-Autonomous Adaptive Combat (ISAAC): An Artificial-Life Approach to Land Warfare.

TL;DR: In this paper, a follow-on effort to a recently completed project, sponsored by the Commanding General, Marine Corps Combat Development Command, that assessed the general applicability of the new sciences to land warfare is described.
Proceedings ArticleDOI

Situation recognition: an evolving problem for heterogeneous dynamic big multimedia data

TL;DR: This paper motivates and computationally grounds the problem of situation recognition, and describes a systematic approach for combining multimodal real-time big data into actionable situations.
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