Envisionment-Based Scheduling Using Time Interval Petri Networks: Representation, Inference, and Learning
Bulitko, Vadim V.
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Permalink
https://hdl.handle.net/2142/81968
Description
Title
Envisionment-Based Scheduling Using Time Interval Petri Networks: Representation, Inference, and Learning
Author(s)
Bulitko, Vadim V.
Issue Date
2000
Doctoral Committee Chair(s)
Wilkins, David C.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Language
eng
Abstract
This interdisciplinary research has made the following five main contributions: (1) A Petri Nets based approach to decision-making scheduling through environment modeling is presented. Petri Nets formalism is known for its solid theoretical base, clear syntax and semantics, intuitive graphic representation, and native concurrency support. (2) The classical Petri Net model is extended in various ways to make it suitable for AI scheduling tasks. The main extensions concern explicit temporal reasoning, context, and operator support. The new formalism is hence called Time Interval Petri Nets (TIPNs). (3) TIPN properties and relation to other Al and Petri Nets formalisms are studied. We also present analysis methods facilitating verification and refinement of Petri Net models. (4) Two machine learning algorithms are developed to synthesize Petri Net models automatically or semi-automatically. One learning algorithm exploits the connection between Petri Nets and Horn clauses by using inductive logic programming methods (ILP) to learn Horn-clauses first and then convert them to TIPNs. The other algorithm employs a general-to-specific search in the space of Petri Net topologies starting with a given initial topology. (5) The framework is applied in the real-time decision-making domain of ship damage control for the tasks of automated problem-solving and intelligent tutoring (advising, critiquing, and scoring). In a large exercise involving approximately 500 simulated ship crisis scenarios, our decision-making expert system showed a 318% improvement over Navy officers by saving 89 more ships.
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