Conxtext-Based Reasoning (CxBR), Contextual Graphs (CxGs), Computer Generated Forces (CGFs), Human Behavior Representation (HBR), Genetic Programming (GP), Subject Matter Expert (SME)
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.
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Master of Science in Computer Engineering (M.S.Cp.E.)
College of Engineering and Computer Science
Electrical and Computer Engineering
Length of Campus-only Access
Masters Thesis (Open Access)
Lorins, Peterson Marthen, "A Comparative Analysis Between Context-based Reasoning (cxbr) And Contextual Graphs (cxgs)." (2005). Electronic Theses and Dissertations, 2004-2019. 465.