Keywords

Conxtext-Based Reasoning (CxBR), Contextual Graphs (CxGs), Computer Generated Forces (CGFs), Human Behavior Representation (HBR), Genetic Programming (GP), Subject Matter Expert (SME)

Abstract

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.

Notes

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Graduation Date

2005

Semester

Summer

Advisor

Gonzalez, Avelino

Degree

Master of Science in Computer Engineering (M.S.Cp.E.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Computer Engineering

Format

application/pdf

Identifier

CFE0000577

URL

http://purl.fcla.edu/fcla/etd/CFE0000577

Language

English

Release Date

August 2008

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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