Increasing Replayability with Deliberative and Reactive Planning

Abstract

Opponent behavior in today's computer games is often the result of a static set of Artificial Intelligence (AI) behaviors or a fixed AI script. While this ensures that the behavior is reasonably intelligent, it also results in very predictable behavior. This can have an impact on the replayability of entertainment-based games and the educational value of training-based games. This paper proposes a move away from static, scripted AI by using a combination of deliberative and reactive planning. The deliberative planning (or Strategic AI) system creates a novel strategy for the AI opponent before each gaming session. The reactive planning (or Tactical AI) system executes this strategy in real-time and adapts to the player and the environment. These two systems, in conjunction with a future automated director module, form the Adaptive Opponent Architecture. This paper describes the architecture and the details of the deliberative and reactive planning components.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA459203

Entities

People

  • Mark O. Riedl
  • Michael Van Lent
  • Paul Brobst
  • Paul Carpenter
  • Ryan Mcalinden

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Cognitive Science
  • Computational Complexity
  • Computers
  • Decomposition
  • Doctrine
  • Education
  • Environment
  • Grenade Launchers
  • Military Doctrine
  • Motion Planning
  • Sequences
  • Simulations
  • Students
  • Training
  • Video Games

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy