Increasing Replayability with Deliberative and Reactive Planning

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 replayabil...

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Hauptverfasser: Lent, Michael van, Riedl, Mark O, Carpenter, Paul, McAlinden, Ryan, Brobst, Paul
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Riedl, Mark O
Carpenter, Paul
McAlinden, Ryan
Brobst, Paul
description 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. The original document contains color images.
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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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source DTIC Technical Reports
subjects ADAPTIVE OPPONENT ARCHITECTURE
ADAPTIVE SYSTEMS
ARTIFICIAL INTELLIGENCE
BEHAVIOR
COGNITION
COGNITIVE SKILLS
COMBAT SIMULATION
COMPUTER GAMES
Computer Programming and Software
COMPUTER PROGRAMS
Cybernetics
DELIBERATIVE PLANNING
DPOCL(DECOMPOSITIONAL PARTIAL ORDER CAUSAL LINK)
FSC(FULL SPECTRUM COMMAND)
GAME THEORY
Military Operations, Strategy and Tactics
MILITARY PLANNING
MILITARY STRATEGY
MILITARY TRAINING
MISSIONS
NOVEL STRATEGIES
OPPONENT BEHAVIOR
REACTION(PSYCHOLOGY)
REACTIVE PLANNING
REAL TIME
REPLAYABILITY
SKILLS
SOAR COMPUTER PROGRAM
SOFTWARE ARCHITECTURE
STRATEGIC ARTIFICIAL INTELLIGENCE
STRATEGIC WARFARE
TACTICAL ARTIFICIAL INTELLIGENCE
TACTICAL WARFARE
UNPREDICTABILITY
WAR GAMES
title Increasing Replayability with Deliberative and Reactive Planning
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