Behavior Selection Using Utility-Based Reinforcement Learning in Irregular Warfare Simulation Models

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Authors
Papadopoulos, Sotiris
Baez, Francisco
Alt, Jonathan
Darken, Christian
Subjects
Agent Based Social Simulations
Commodity Consumption
Cultural Geography Model
Reinforcement Learning
Theory of Planned Behavior (TPB)
Advisors
Date of Issue
2013
Date
Publisher
IGI Global
Language
Abstract
The Theory of Planned Behavior (TPB) provides a conceptual model for use in assessing behavioral intentions of humans. Agent based social simulations seek to represent the behavior of individuals in societies in order to understand the impact of a variety of interventions on the population in a given area. Previous work has described the implementation of the TPB in agent based social simulation using Bayesian networks. This paper describes the implementation of the TPB using novel learning techniques related to reinforcement learning. This paper provides case study results from an agent based simulation for behavior related to commodity consumption. Initial results demonstrate behavior more closely related to observable human behavior. This work contributes to the body of knowledge on adaptive learning behavior in agent based simulations.
Type
Article
Description
The article of record as published may be found at http://dx.doi.org/10.4018/joris.2013070105
URI
https://hdl.handle.net/10945/60716
Series/Report No
Department
Computer Science (CS)
Organization
Naval Postgraduate School (U.S.)
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Format
18 p.
Citation
Papadopoulos, Sotiris, et al. "Behavior selection using utility-based reinforcement learning in irregular warfare simulation models." International Journal of Operations Research and Information Systems (IJORIS) 4.3 (2013): 61-78.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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