Increasing Replayability with Deliberative and Reactive Planning


  • Michael van Lent University of Southern California
  • Mark O. Riedl University of Southern California
  • Paul Carpenter University of Southern California
  • Ryan McAlinden University of Southern California
  • Paul Brobst University of Southern California



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.




How to Cite

van Lent, M., Riedl, M., Carpenter, P., McAlinden, R., & Brobst, P. (2021). Increasing Replayability with Deliberative and Reactive Planning. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 1(1), 135-140.