Abstract
We present work in progress that addresses a formalism and a system for computationally modeling human behavior where humans collaborate and communicate with other humans or automated systems with significant problem-solving ability. We conceptualize the simulated participants as agents. The fundamental notion is that of a role. An agent may assume one or more roles, and optimization measures are associated with roles. To represent coordination explicitly, we use an adaptation of Pazzi’s Part-Whole Statecharts, which encapsulate coordination behavior. We include violation transitions to capture non-ideal behavior and violation states for repair. Fuzzy logic control is added to handle large domains and to resolve nondeterminism. A Statechart is represented internally with weighted rules, and an XCS classifier system is used to update the rule base in light of the outcomes of simulations; this provides a form of machine learning.
Keywords
- Fuzzy Logic Controller
- Optimization Measure
- Orthogonal Component
- Intelligent Tutoring System
- Violation State
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Kimiaghalam, B., Homaifar, A., Esterline, A.C. (2003). A Statechart Framework for Agent Roles that Captures Expertise and Learns Improved Behavior. In: Hinchey, M.G., Rash, J.L., Truszkowski, W.F., Rouff, C., Gordon-Spears, D. (eds) Formal Approaches to Agent-Based Systems. FAABS 2002. Lecture Notes in Computer Science(), vol 2699. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45133-4_3
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DOI: https://doi.org/10.1007/978-3-540-45133-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40665-5
Online ISBN: 978-3-540-45133-4
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