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DTAACS: distributed task allocation for adaptive computational system based on organization knowledge

Doctor of Philosophy / Department of Computing and Information Sciences / Scott A. DeLoach / The Organization-Based Multi-Agent Systems (OMAS) paradigm is an approach to address
the challenges posed by complex systems. The complexity of these systems, the changing
environment where the systems are deployed, and satisfying higher user expectations are
some of current requirements when designing OMAS. For the agents in an OMAS to pursue
the achievement of a common goal or task, a certain level of coordination and collaboration
occurs among them. An objective in this coordination is to make the decision of who
does what. Several solutions have been proposed to answer this task allocation question.
The majority of the solutions proposed fall in the categories of marked-based approaches,
reactive systems, or game theory approaches. A common fact among these solutions is the
system information sharing among agents, which is used only to keep the participant agent
informed about other agents activities and mission status.
To further exploit and take advantage of this system information shared among agents,
a framework is proposed to use this information to answer the question who does what, and
reduce the communication among agents. DTAACS-OK is a distributed knowledge-based
framework that addresses the Single Agent Task Allocation Problem (SAT-AP) and the
Multiple Agent Task Allocation Problem (MAT-AP) in cooperative OMAS. The allocation of
tasks is based on an identical organization knowledge posses by all agents in the organization.
DTAACS-OK di ers with current solutions in that (a) it is not a marked-based approach
where task are auctioned among agents, or (b) it is not based on agents behaviour, where the
action or lack of action of an agent cause the reaction of other agents in the organization.

Identiferoai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/18247
Date January 1900
CreatorsValenzuela, Jorge L.
PublisherKansas State University
Source SetsK-State Research Exchange
Languageen_US
Detected LanguageEnglish
TypeDissertation

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