Proceedings Article•
Hierarchical plan representations for encoding strategic game AI
01 Jun 2005-pp 63-68
TL;DR: HTN planners can be used to generate correct plans for coordinated team AI behavior modeled with TMK representations, and TMK models are of interest to game AI because, as it is shown, they are as expressive as HTNs but have more convenient syntax.
Abstract: 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.
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06 Aug 2009
TL;DR: 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 Algorithm Level of Detail World Interfacing Communication Getting Knowledge Efficiently Event Managers Polling Stations Sense Management Tools and Content Creation.
Abstract: 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
472 citations
Cites background from "Hierarchical plan representations f..."
...direction [Hoang et al., 2005] that inspire the game AI of several commercial...
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TL;DR: An overview of the existing work on AI for real-time strategy (RTS) games focuses on the work around the game StarCraft, which has emerged in the past few years as the unified test bed for this research.
Abstract: 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.
401 citations
Cites background from "Hierarchical plan representations f..."
...Reactive planning [24], a decompositional planning similar to hierarchical task networks [11], allows for plans to be changed at different granularity levels and so for multiscale (hierarchical) goals integration of low-level control....
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...text of simpler first-person shooter games [11]....
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13 Aug 2007
TL;DR: This paper presents a real-time case based planning and execution approach designed to deal with RTS games and proposes to extract behavioral knowledge from expert demonstrations in form of individual cases via a case based behavior generator.
Abstract: 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.
152 citations
Additional excerpts
...[9] propose to use a hierarchical plan representation to encode strategic game AI....
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TL;DR: It is shown how the use of state constraints can provide a unified perspective on important problems faced in IS and the development of an approach to narrative generation that exploits such constraints are developed.
Abstract: 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.
141 citations
01 Jan 2006
TL;DR: The emphasis is demonstrating how the planning system improved the process of developing character behaviors for F.E.A.R.R as a case study, using F.A* to plan sequences of actions as well as to plan paths.
Abstract: 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.
139 citations
References
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TL;DR: In this paper, the authors describe a problem solver called STRIPS that attempts to find a sequence of operators in a space of world models to transform a given initial world model in which a given goal formula can be proven to be true.
2,883 citations
Proceedings Article•
01 Aug 1994TL;DR: How the complexity of HTN planning varies with various conditions on the task networks is described.
Abstract: Most practical work on AI planning systems during the last fifteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies with various conditions on the task networks.
747 citations
"Hierarchical plan representations f..." refers background in this paper
...Furthermore, representing HTNs in STRIPS operators is very cumbersome in general (Lotem & Nau, 2000) and sometimes even impossible (Erol et al., 1994)....
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...Hierarchical Task-Network (HTN) planning is another form of planning that advocates reasoning on the level of high-level tasks rather than on the level of the actions (Erol et al., 1994)....
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01 Jan 2001
TL;DR: In this paper, the authors developed and evaluated synthetic characters with multiple skill levels and with human-like behavior, including decision time, aggressiveness, number of tactics, and aiming skill.
Abstract: This paper reports on a preliminary attempt to develop and evaluate synthetic characters with multiple skill levels and with human-like behavior. Our goal was to determine which aspects of behavior have impact on skill level and humanness. We developed a bot that plays the computer game Quake against a human opponent. That bot can be parameterized along four dimensions: decision time, aggressiveness, number of tactics, and aiming skill. We then played variations of the bot against a human expert. Through empirical and human judgments we then evaluated the skill level and humanness of the variations. Our results suggest that both decision time and aiming skill are critical parameters when attempting to create human-like behavior. These two parameters could also be varied to modify the skill of the bots, and for some ranges, maintain the same level of humanness. The two primary goals of this research are to create synthetic characters with human-like behavior and varying levels of skill within the context of highly interactive tasks. The secondary goal is to develop and test a methodology for evaluating the humanness of synthetic characters in those types of tasks. Both primary goals require that we explore computational mechanisms for modeling human behavior and that we understand which behavioral parameters affect the humanness of the behavior and the skill of the behavior. Towards these ends, we have developed a parameterized AI system (called a bot) that plays the computer game Quake against humans. In developing the bot, we have tried to build an opponent that has the same strengths and weaknesses as human players. As part of its development, we parameterized the bot along four dimensions: decision time, aggressiveness, aiming skill, and tactical knowledge. This paper describes an evaluation of variations in those parameters in terms of different levels of skill and humanness. Our approach was to build versions of the bot with different parameter values and then play them against a "gold-standard" expert human player. Our results suggest that this methodology is useful, and that we could produce a range of skill levels using a subset of these parameters.
103 citations
"Hierarchical plan representations f..." refers methods in this paper
...The virtual training of soldiers for Military Operations on Urbanized Terrain (MOUT) is a system developed and used for actual training of military personnel (Laird & Duchim, 2000)....
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Proceedings Article•
01 Jul 1998
TL;DR: The latest world-championship competition for computer bridge programs was the Baron Barclay World Bridge Computer Challenge, hosted in July 1997 by the American Contract Bridge League, and the winner was a new version of Great Game Products' Bridge Baron program, which uses Hierarchical Task-Network planning techniques.
Abstract: The latest world-championship competition for computer bridge programs was the Baron Barclay World Bridge Computer Challenge, hosted in July 1997 by the American Contract Bridge League. As reported in The New York Times and The Washington Post, the competition's winner was a new version of Great Game Products' Bridge Baron program. This version, Bridge Baron 8, has since gone on the market; and during the last three months of 1997 it was purchased by more than 1000 customers.The Bridge Baron's success also represents a significant success for research on AI planning systems, because Bridge Baron 8 uses Hierarchical Task-Network (HTN) planning techniques to plan its declarer play. This paper gives an overview of those techniques and how they are used.
32 citations
"Hierarchical plan representations f..." refers background in this paper
...For example, Bridge Baron® 8 won the 1997 world-championship competition for computer programs using HTN planning techniques to plan its declarer play (Smith et al., 1998)....
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Proceedings Article•
14 Apr 2000TL;DR: A new method for extracting knowledge on Hierarchical Task-Network (HTN) planning problems for speeding up the search is described, gathered by propagating properties through an AND/OR tree that represents disjunctively all possible decompositions of an HTN planning problem.
Abstract: We describe in this paper a new method for extracting knowledge on Hierarchical Task-Network (HTN) planning problems for speeding up the search. This knowledge is gathered by propagating properties through an AND/OR tree that represents disjunctively all possible decompositions of an HTN planning problem. We show how to use this knowledge during the search process of our GraphHTN planner, to split the current refined planning problem into independent subproblems.
We also present new experimental results comparing GraphHTN with ordinary HTN decomposition (as implemented in the UMCP planner). The comparison is performed on a set of problems from the UM Translog domain - a large HTN transportation domain that is considerably more complicated than the well known "logistics" domain.
Finally, so that we could compare GraphHTN with action-based planners such as IPP and Blackbox, we translated the UM Translog domain into a STRIPS-style representation. We found that GraphHTN performed considerably better on UM Translog than IPP and Blackbox.
27 citations