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A COMPARATIVE ANALYSIS BETWEEN CONTEXT-BASED REASONING (CXBR) AND CONTEXTUAL GRAPHS (CXGS).

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Date Issued:
2005
Abstract/Description:
Context-based Reasoning (CxBR) and Contextual Graphs (CxGs) involve the modeling of human behavior in autonomous and decision-support situations in which optimal human decision-making is of utmost importance. Both formalisms use the notion of contexts to allow the implementation of intelligent agents equipped with a context sensitive knowledge base. However, CxBR uses a set of discrete contexts, implying that models created using CxBR operate within one context at a given time interval. CxGs use a continuous context-based representation for a given problem-solving scenario for decision-support processes. Both formalisms use contexts dynamically by continuously changing between necessary contexts as needed in appropriate instances. This thesis identifies a synergy between these two formalisms by looking into their similarities and differences. It became clear during the research that each paradigm was designed with a very specific family of problems in mind. Thus, CXBR best implements models of autonomous agents in environment, while CxGs is best implemented in a decision support setting that requires the development of decision-making procedures. Cross applications were implemented on each and the results are discussed.
Title: A COMPARATIVE ANALYSIS BETWEEN CONTEXT-BASED REASONING (CXBR) AND CONTEXTUAL GRAPHS (CXGS).
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Name(s): Lorins, Peterson, Author
Gonzalez, Avelino, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2005
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Context-based Reasoning (CxBR) and Contextual Graphs (CxGs) involve the modeling of human behavior in autonomous and decision-support situations in which optimal human decision-making is of utmost importance. Both formalisms use the notion of contexts to allow the implementation of intelligent agents equipped with a context sensitive knowledge base. However, CxBR uses a set of discrete contexts, implying that models created using CxBR operate within one context at a given time interval. CxGs use a continuous context-based representation for a given problem-solving scenario for decision-support processes. Both formalisms use contexts dynamically by continuously changing between necessary contexts as needed in appropriate instances. This thesis identifies a synergy between these two formalisms by looking into their similarities and differences. It became clear during the research that each paradigm was designed with a very specific family of problems in mind. Thus, CXBR best implements models of autonomous agents in environment, while CxGs is best implemented in a decision support setting that requires the development of decision-making procedures. Cross applications were implemented on each and the results are discussed.
Identifier: CFE0000577 (IID), ucf:46433 (fedora)
Note(s): 2005-08-01
M.S.Cp.E.
Engineering and Computer Science, Department of Electrical and Computer Engineering
Masters
This record was generated from author submitted information.
Subject(s): Conxtext-Based Reasoning (CxBR)
Contextual Graphs (CxGs)
Computer Generated Forces (CGFs)
Human Behavior Representation (HBR)
Genetic Programming (GP)
Subject Matter Expert (SME)
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0000577
Restrictions on Access: campus 2008-01-31
Host Institution: UCF

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