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Behavioral Modeling and Computationa...

Humann, James.

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Behavioral Modeling and Computational Synthesis of Self-Organizing Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Behavioral Modeling and Computational Synthesis of Self-Organizing Systems./
作者:	Humann, James.
面頁冊數:	209 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-07(E), Section: B.
Contained By:	Dissertation Abstracts International77-07B(E).
標題:	Mechanical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10014557
ISBN:	9781339485676

Behavioral Modeling and Computational Synthesis of Self-Organizing Systems.
Humann, James.

Behavioral Modeling and Computational Synthesis of Self-Organizing Systems. - 209 p.

Source: Dissertation Abstracts International, Volume: 77-07(E), Section: B.

Thesis (Ph.D.)--University of Southern California, 2015.

Engineered systems are facing requirements for increased adaptability, the capacity to cope with change. This includes flexibility to fulfill multiple purposes over long lifespans, robustness to environmental changes, and resilience to system change and damage. This dissertation investigates the use of self-organization as a tool for the design of adaptable systems.

ISBN: 9781339485676Subjects--Topical Terms:

649730
Mechanical engineering.

Behavioral Modeling and Computational Synthesis of Self-Organizing Systems.
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245 1 0 $a Behavioral Modeling and Computational Synthesis of Self-Organizing Systems.
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500 $a Source: Dissertation Abstracts International, Volume: 77-07(E), Section: B.
500 $a Adviser: Yan Jin.
502 $a Thesis (Ph.D.)--University of Southern California, 2015.
520 $a Engineered systems are facing requirements for increased adaptability, the capacity to cope with change. This includes flexibility to fulfill multiple purposes over long lifespans, robustness to environmental changes, and resilience to system change and damage. This dissertation investigates the use of self-organization as a tool for the design of adaptable systems.
520 $a Self-organizing systems have no central or outside controller. They are built up from the interactions of autonomous agents, such as a swarm of robots. Because the agents are autonomous and self-interested, they can fulfill complex functional requirements. They are able to grow and rearrange themselves; different segments of the system can adapt locally to nonuniform terrain; if the systems are made of homogeneous agents, they can be resilient to failure of several components as other identical agents can take their place. Moreover, this complex functionality can be found in the interactions of fairly simple agents, decreasing the cost of manufacturing these systems at large scales.
520 $a The main challenge in the design of self-organizing systems is designing an agent's behavior at a local level such that the system fulfills its function at a higher level. In order to overcome these challenges, this dissertation presents a design ontology and a computational synthesis framework. The design ontology identifies the fundamental elements in the design of self-organizing systems and groups them into a cohesive methodology. This ontology can guide designers at the conceptual design stage to create parametric behavioral models for self-organizing agents. Computational synthesis, based on multi-agent simulation for analysis and a genetic algorithm for optimization, can complete the detail design work. The optimized systems can then be deployed in diverse simulated scenarios.
520 $a This dissertation presents four case studies on the design of self-organizing systems: a flocking system, a protective convoy, a foraging system, and a box-pushing system. The results of the case studies validate the design approach. They show that there are significant tradeoffs in the design of adaptable systems. Designers must sacrifice some efficiency and repeatability for adaptability. The ability to scale systems with constant conceptual designs was shown to be possible, but scaling with a constant detail design was shown to incur large fitness penalties. These penalties were more severe when systems scaled up in size rather than down.
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