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Eyorokon Thesis.pdf (2.16 MB)
ETD Abstract Container
Abstract Header
Measuring Goal Similarity Using Concept, Context and Task Features
Author Info
Eyorokon, Vahid
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=wright1534084289041091
Abstract Details
Year and Degree
2018, Master of Science (MS), Wright State University, Computer Science.
Abstract
Goals can be described as the user’s desired state of the agent and the world and are satisfied when the agent and the world are altered in such a way that the present state matches the desired state. For physical agents, they must act in the world to alter it in a series of individual atomic actions. Traditionally, agents use planning to create a chain of actions each of which altering the current world state and yielding a new one until the final action yields the desired goal state. Once this goal state has been achieved, the goal is said to have been satisfied. Since these goals involve physical actions, we can describe these goals as being physical goals. Our work focuses on a special type of goal that doesn’t exist physically and are knowledge goals. Much like physical goals, knowledge goals also have a desired state but this desired state is of the user’s understanding. Once the user has learned the missing information, the knowledge goal has been satisfied. While physical goals are given to agents who must then produce a plan of actions to alter the world, knowledge goals are given to an agent who must then produce a sequence of intermediate knowledge goals to alter the user’s state of knowledge. Much like how individual actions comprise a plan to alter the physical world, individual questions comprise a goal trajectory and alter the state of a user’s knowledge. This overall path of inquiry is much like that of an investigation for knowledge not unlike those of a detective or investigator. Given that not all users learn the same way, creating a plan to solve a knowledge goal is not a trivial task. Furthermore, in complex domains, it is not immediately clear to user themselves what their knowledge goal is as they continue to understand how to phrase the correct questions. As the user continues to refine their questions, their search grows in length and often in complexity as questions become increasingly specific. To address these issues, we created and evaluated a case-based goal reasoning system with the ability to measure similarity between goals.
Committee
Michelle Cheatham, Ph.D. (Advisor)
Michael Cox, Ph.D. (Committee Member)
Michael Raymer, Ph.D. (Committee Member)
Pages
79 p.
Subject Headings
Computer Science
Keywords
Case-based reasoning
;
artificial intelligence
;
knowledge goals
;
case retrieval
;
goal features
;
conversational case-based reasoning
;
textual case-based reasoning
;
natural language processing
;
machine learning
;
case reuse
;
tangent recognition
;
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Refworks
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Mendeley
Citations
Eyorokon, V. (2018).
Measuring Goal Similarity Using Concept, Context and Task Features
[Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1534084289041091
APA Style (7th edition)
Eyorokon, Vahid.
Measuring Goal Similarity Using Concept, Context and Task Features.
2018. Wright State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=wright1534084289041091.
MLA Style (8th edition)
Eyorokon, Vahid. "Measuring Goal Similarity Using Concept, Context and Task Features." Master's thesis, Wright State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1534084289041091
Chicago Manual of Style (17th edition)
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Document number:
wright1534084289041091
Download Count:
298
Copyright Info
© 2018, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.