Automatic Generation of Agent Behavior Models from Raw Observational Data

  • Bridgette Parsons
  • José M. Vidal
  • Nathan Huynh
  • Rita Snyder
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9002)

Abstract

Agent-based modeling is used to simulate human behaviors in different fields. The process of building believable models of human behavior requires that domain experts and Artificial Intelligence experts work closely together to build custom models for each domain, which requires significant effort. The aim of this study is to automate at least some parts of this process. We present an algorithm called Open image in new window, which produces an agent behavioral model from raw observational data. It calculates transition probabilities between actions and identifies decision points at which the agent requires additional information in order to choose the appropriate action. Our experiments using synthetically-generated data and real-world data from a hospital setting show that the Open image in new window algorithm can automatically produce an agent decision process. The agent’s underlying behavior can then be modified by domain experts, thus reducing the complexity of producing believable agent behavior from field data.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Bridgette Parsons
    • 1
  • José M. Vidal
    • 1
  • Nathan Huynh
    • 2
  • Rita Snyder
    • 3
  1. 1.Department of Computer Science and EngineeringUniversity of South CarolinaColumbiaUSA
  2. 2.Department of Civil and Environmental EngineeringUniversity of South CarolinaColumbiaUSA
  3. 3.College of Nursing’s Healthcare Process Redesign CenterUniversity of South CarolinaColumbiaUSA

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