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

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

Sotiris Papadopoulos (MOVES Institute, Naval Postgraduate School, Monterey, CA, USA), Francisco Baez (MOVES Institute, Naval Postgraduate School, Monterey, CA, USA & U.S. Army TRADOC Analysis Center, Monterey, CA, USA), Jonathan Alt (U.S. Army TRADOC Analysis Center, Monterey, CA, USA & MOVES Institute, Naval Postgraduate School, Monterey, CA, USA) and Christian Darken (MOVES Institute, Naval Postgraduate School, Monterey, CA, USA)
DOI: 10.4018/joris.2013070105
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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.
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Background

This section provides an overview of cognitive social simulation, TPB, and reinforcement learning.

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