Towards Learned Anticipation in Complex Stochastic Environments

Abstract

We describe a novel methodology by which a software agent can learn to predict future events in complex stochastic environments. It is particularly relevant to environments that are constructed specifically so as to be able to support high-performance software agents, such as video games. We present results gathered from a first prototype of our approach. The technique presented may have applications that range beyond improving agent performance, in particular to user modeling in the service of automated game testing.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2005
Accession Number
ADA575043

Entities

People

  • Christian J. Darken

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Applied Computer Science
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computer Science
  • Computers
  • Environment
  • Machine Learning
  • Models
  • Predictive Modeling
  • Probability
  • Probability Distributions
  • Software Agents
  • Video
  • Video Games

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Game Theory.
  • Software Engineering.