AI and Games Introduction What Is AI? Model of Game AI Algorithms, Data Structures, and Representations On the Website Layout of the Book Game AI The Complexity Fallacy The Kind of AI in Games Speed and Memory The AI Engine Techniques Movement The Basics of Movement Algorithms Kinematic Movement Algorithms Steering Behaviors Combining Steering Behaviors Predicting Physics Jumping Coordinated Movement Motor Control Movement in the Third Dimension Pathfinding The Pathfinding Graph Dijkstra A* World Representations Improving on A* Hierarchical Pathfinding Other Ideas in Pathfinding Continuous Time Pathfinding Movement Planning Decision Making Overview of Decision Making Decision Trees State Machines Behavior Trees Fuzzy Logic Markov Systems Goal-Oriented Behavior Rule-Based Systems Blackboard Architectures Scripting Action Execution Tactical and Strategic AI Waypoint Tactics Tactical Analyses Tactical Pathfinding Coordinated Action Learning Learning Basics Parameter Modification Action Prediction Decision Learning Naive Bayes Classifiers Decision Tree Learning Reinforcement Learning Artificial Neural Networks Board Games Game Theory Minimaxing Transposition Tables and Memory Memory-Enhanced Test Algorithms Opening Books and Other Set Plays Further Optimizations Turn-Based Strategy Games Supporting Technologies Execution Management Scheduling Anytime Algorithms Level of Detail World Interfacing Communication Getting Knowledge Efficiently Event Managers Polling Stations Sense Management Tools and Content Creation Knowledge for Pathfinding and Waypoint Tactics Knowledge for Movement Knowledge for Decision Making The Toolchain Designing Game AI Designing Game AI The Design Shooters Driving Real-Time Strategy Sports Turn-Based Strategy Games AI-Based Game Genres Teaching Characters Flocking and Herding Games Appendix Books, Periodicals, and Papers Games

Artificial Intelligence for Games

This paper presents an overview of the existing work on AI for real-time strategy (RTS) games. Specifically, we focus on the work around the game StarCraft, which has emerged in the past few years as the unified test bed for this research. We describe the specific AI challenges posed by RTS games, and overview the solutions that have been explored to address them. Additionally, we also present a summary of the results of the recent StarCraft AI competitions, describing the architectures used by the participants. Finally, we conclude with a discussion emphasizing which problems in the context of RTS game AI have been solved, and which remain open.

/pdf/a-survey-of-real-time-strategy-game-ai-research-and-obid322jza.pdf

A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft

Artificial Intelligence techniques have been successfully applied to several computer games. However in some kinds of computer games, like real-time strategy (RTS) games, traditional artificial intelligence techniques fail to play at a human level because of the vast search spaces that they entail. In this paper we present a real-time case based planning and execution approach designed to deal with RTS games. We propose to extract behavioral knowledge from expert demonstrations in form of individual cases. This knowledge can be reused via a case based behavior generator that proposes behaviors to achieve the specific open goals in the current plan. Specifically, we applied our technique to the W ARGUS domain with promising results.

https://www.cc.gatech.edu/faculty/ashwin/papers/er-07-11.pdf

Case-Based Planning and Execution for Real-Time Strategy Games

We have seen ten years of the application of AI planning to the problem of narrative generation in Interactive Storytelling (IS). In that time planning has emerged as the dominant technology and has featured in a number of prototype systems. Nevertheless key issues remain, such as how best to control the shape of the narrative that is generated (e.g., by using narrative control knowledge, i.e., knowledge about narrative features that enhance user experience) and also how best to provide support for real-time interactive performance in order to scale up to more realistic sized systems. Recent progress in planning technology has opened up new avenues for IS and we have developed a novel approach to narrative generation that builds on this. Our approach is to specify narrative control knowledge for a given story world using state trajectory constraints and then to treat these state constraints as landmarks and to use them to decompose narrative generation in order to address scalability issues and the goal of real-time performance in larger story domains. This approach to narrative generation is fully implemented in an interactive narrative based on the “Merchant of Venice.” The contribution of the work lies both in our novel use of state constraints to specify narrative control knowledge for interactive storytelling and also our development of an approach to narrative generation that exploits such constraints. In the article we show how the use of state constraints can provide a unified perspective on important problems faced in IS.

/pdf/applying-planning-to-interactive-storytelling-narrative-51xbmoml32.pdf

Applying planning to interactive storytelling: Narrative control using state constraints

If the audience of GDC was polled to list the most common A.I. techniques applied to games, undoubtedly the top two answers would be A* and Finite State Machines (FSMs). Nearly every game that exhibits any A.I. at all uses some form of an FSM to control character behavior, and A* to plan paths. F.E.A.R. uses these techniques too, but in unconventional ways. The FSM for characters in F.E.A.R. has only three states, and we use A* to plan sequences of actions as well as to plan paths. This paper focuses on applying planning in practice, using F.E.A.R. as a case study. The emphasis is demonstrating how the planning system improved the process of developing character behaviors for F.E.A.R.

/pdf/three-states-and-a-plan-the-a-i-of-f-e-a-r-1m0wwc0d7l.pdf

Three States and a Plan: The A.I. of F.E.A.R.

In this paper we explore the use of Hierarchical-Task-Network (HTN) representations to model strategic game AI. We will present two case studies. The first one reports on an experiment using HTNs to model strategies for Unreal Tournament® (UT) bots. We will argue that it is possible to encode strategies that coordinate teams of bots in first-person shooter games using HTNs. The second one compares an alternative to HTNs called Task-Method-Knowledge (TMK) process models. TMK models are of interest to game AI because, as we will show, they are as expressive as HTNs but have more convenient syntax. Therefore, HTN planners can be used to generate correct plans for coordinated team AI behavior modeled with TMK representations.

/pdf/hierarchical-plan-representations-for-encoding-strategic-dmj7827oau.pdf

Hai Hoang

Papers

Hierarchical plan representations for encoding strategic game AI