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Strategy / Tactical


Performing Qualitative Terrain Analysis in Master of Orion 3

Kevin Dill, Alex Sramek (Quicksilver Software, Inc.)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Tactical, Terrain Analysis; Genres: Strategy, RTS
Abstract: One challenge for many strategy game AIs is the need to perform qualitative terrain analysis. By qualitative we mean that the analysis is based on fundamental differences between different types of locations - for instance areas that are visible to our opponents, areas that are impassible, or areas vulnerable to enemy fire. In Master of Orion 3 we identify stars that are inside or outside of our empire's borders, those that are threatened by our opponents, and those that are contested (shared with an opponent). This information is used to identify locations where we need to concentrate our defenses and to help us expand into areas that minimize our defensive needs while maximizing the territory we control.

In this article we will present the algorithms used to make the qualitative distinctions given above and the ways in which the AI uses that information. The lessons we would most like the reader to take away from this article are not the specifics of the algorithms used but rather the thought processes involved in applying qualitative reasoning to terrain analysis. The important questions to address are: what are the qualitative distinctions we should look for, how can we recognize them, and what uses can the AI make of that information. Our algorithms are but a single example of how these questions can be answered.

The Unique Challenges of Turn-Based AI
Soren Johnson (Firaxis Games)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Tactical; Genres: Strategy
Abstract: Writing a turn-based AI presents a number of unique programming and game design challenges. The common thread uniting these challenges is the user's complete control over the game's speed. Players willing to invest extreme amounts of time into micro-management and players looking to streamline their gaming experience via automated decision-making present two very different problems for the AI to handle. Further, the ability to micro-analyze turn-based games makes predictability, cheating, and competitive balance extremely important issues. This article outlines how the Civilization III development team dealt with these challenges, using specific examples to illuminate some practical solutions useful to a programmer tasked with creating an AI for a turn-based game.

Random Map Generation for Strategy Games
Shawn Shoemaker (Stainless Steel Studios)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy; Genres: Strategy, RTS
Abstract: While there are numerous articles dedicated to the generation of random maps for games, there is little published information on random maps for strategy games in particular. This subset of map generation presents distinct challenges as evident by the relatively few games that implement them. While the techniques described here can be used to create maps suitable for any type of game, this system is specifically designed to create a variety of successful random maps for real-time strategy games. This article describes the random map generation implementation as found in the RTS game Empire Earth (EE) developed by Stainless Steel Studios.

Transport Unit AI for Strategy Games
Shawn Shoemaker (Stainless Steel Studios)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Tactical; Genres: Strategy, RTS
Abstract: Unit AI refers to the micro-level artificial intelligence that controls a specific unit in a game and how that unit reacts to input from the player and the game world. Transports present a particular challenge for unit AI as many units must work together to achieve their common goal, all the while attempting to minimize player frustration. This article discusses the general transport unit AI challenge and a successful solution. Land, air, naval, and building transports (such as fortresses and town centers) will be discussed and a class hierarchy implementation will be suggested. Algorithms for the loading (including the calculation for rendezvous points) and unloading of transports will be presented as well as warnings for particular pitfalls.

This article assumes some sort of finite-state-machine-based unit AI system and is applicable to any game in which there are multiple units in need of transporting. This article details the transport unit AI as found in the Real-Time Strategy (RTS) game Empire Earth (EE) developed by Stainless Steel Studios.

Wall Building for RTS Games

Mario Grimani (Sony Online Entertainment)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Tactical; Genres: RTS
Abstract: Most real-time strategy games include walls or similar defensive structures that act as barriers for unit movement. Having a general-purpose wall-building algorithm increases the competitiveness of computer opponents and provides a new set of options for the random mission generation. The article discusses a wall building algorithm that uses the greedy methodology to build a wall that fits the definition, protects the desired location, and meets the customizable acceptance criteria. The algorithm takes advantage of the natural barriers and map edges to minimize the cost of building a wall. The algorithm discussion focuses on importance of traversal and heuristic functions, details of implementation, and various real world problems. Advanced topics such as minimum/maximum distance requirements, placement of gates and an unusual wall configurations are elaborated on. Full source code and a demo are supplied.

Strategic Decision-Making with Neural Networks and Influence Maps

Penny Sweetser (School of ITEE, University of Queensland)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Tactical; Genres: Strategy, RTS
Abstract: Influence maps provide a strategic perspective in games that allows strategic assessment and decisions to be made based on the current game state. Influence maps consist of several layers, each representing different variables in the game, layered over a geographical representation of the game map. When a decision needs to be made by the AI player, some or all of these layers are combined via a weighted sum to provide an overall idea of the suitability of each area on the map for the current decision. However, the use of a weighted sum has certain limitations.

This article explains how a neural network can be used in place of a weighted sum, to analyze the data from the influence map and make a strategic decision. First, this article will summarize influence maps, describe the current application of a weighted sum and outline the associated advantages and disadvantages. Following this, it will explain how a neural network can be used in place of a weighted sum and the benefits and drawbacks associated with this alternative. Additionally, it will go into detail about how a neural network can be implemented for this application, illustrated with diagrams.

Multi-Tiered AI Layers and Terrain Analysis for RTS Games
Tom Kent (Freedom Games, Inc.)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Tactical, Terrain Analysis; Genres: Strategy, RTS
Abstract: RTS games tend to handle soldier AIs individually, giving each unit specific tasks from the computer player. Creating complicated, cooperative tactics are impossible for such systems without an immense effort in coding. To develop complex, large-scale plans, a mechanism is needed to reduce the planning devoted to the individual units. Some games already collect individual soldiers into squads. This reduces the planning necessary by a factor of ten, as one hundred soldiers can be collected into ten squads. However, this concept can be taken farther, with squads collected into platoons, platoons into companies, and so on. The versatility such groupings give an AI system are immense. This article will explore the implementation of a multi-tiered AI system in RTS-type games, including the various AI tiers, a set of related maps used by the AI tiers and an example to illustrate the system.

Designing a Multi-Tiered AI Framework

Michael Ramsey (2015, Inc.)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Tactical; Genres: Strategy, RTS
Abstract: The MTAIF allows an AI to be broken up into three concrete layers, strategic, operational and a tactical layer. This allows for an AI programmer to have various AIs focus on specific tasks, while at the same time having a consistent overall focus. The MTAIF allows for the strategic layer to be focused exclusively on matters that can affect an empire on a holistic scale, while at the operational level the AI is in tune with reports from the tactical level. A differing factor from many other architectures is that the MTAIF does not allow decisions to be made on a tactical scale that would violate the overall strategic policies. This in turn forces highlevel strategic policies to be enforced in tactical situations, without the AI devolving into a reactionary based AI.

Simple Techniques for Coordinated Behavior
Jeff Orkin (Monolith Productions)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Tactical, Coordinated Movement, Cooperation; Genres: FPS, Action
Abstract: There are a number of common problems that arise when developing AI systems for combat with multiple enemies. Agents block each other’s line of fire. Agents follow the exact same path to a target, and often clump up at a destination. Some agents are oblivious to a threat while others nearby are getting shot or even killed. Multiple agents decide to do the exact same action or animation simultaneously. It would seem that a group behavior layer of complex higher-level reasoning would be needed to solve these problems. In fact, these problems can be solved with simple techniques that use existing systems and leverage information that individual agents already have. This article describes simple techniques that can be used to solve coordination problems, using examples from Monolith Productions' "No One Lives Forever 2: A Spy in H.A.R.M.'s Way."

Team Member AI in an FPS

John Reynolds (Creative Asylum Ltd.)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Tactical, Coordinated Movement, Cooperation; Genres: FPS, Action
Abstract: The use of teammates has become very popular among the first and third person action genres in recent years, in both the simulation and arcade sub-genres. However, implementing convincing teammates who will not run in your path while you are shooting, nor disappear into a far corner of the map, is quite an involved process. By implementing some key rules it is possible to create teammates who can usefully back you up in the thick of the action, follow instructions reliably, and survive with you until the end of the game.

Applying Goal-Oriented Action Planning to Games
Jeff Orkin (Monolith Productions)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Planning; Genres: Strategy, RTS, FPS, Action
Abstract: A number of games have implemented characters with goal directed decision-making capabilities. A goal-directed character displays some measure of intelligence by autonomously deciding to activate the behavior that will satisfy the most relevant goal at any instance. Goal-Oriented Action Planning (GOAP) is a decision-making architecture that takes the next step, and allows characters to decide not only what to do, but how to do it. A character that formulates his own plan to satisfy his goals exhibits less repetitive, predictable behavior, and can adapt his actions to custom fit his current situation. In addition, the structured nature of a GOAP architecture facilitates authoring, maintaining, and re-using behaviors. This article explores how games can benefit from the addition of a real-time planning system, using problems encountered during the development of Monolith Production's No One Lives Forever 2: A Spy in H.A.R.M.'s Way to illustrate these points.

Hierarchical Planning in Dynamic Worlds
Neil Wallace (Black & White Studios / Lionhead Studios)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Planning; Genres: Strategy, RTS, FPS, Action
Abstract:

Goal Directed Behavior using Composite Tasks
Eric Dybsand (Glacier Edge Technology)
AI Game Programming Wisdom 2, 2003.
Topics: Strategy, Planning; Genres: Strategy, RTS, FPS, Action
Abstract: This article will introduce the reader to goal directed behavior and offers several examples of games that have used it to increase the believability of the agents in those games. The article then goes on to discuss the implementation of the Composite Task concept that was designed and developed to provide goal directed behavior for the agents in a military tactical combat training simulator. Finally, the simulator itself is briefly discussed and references to additional information on goal directed behavior are provided.

Strategic and Tactical Reasoning with Waypoints
Lars Lidén (Valve Software)
AI Game Programming Wisdom, 2002.
Topics: Movement, Strategy, Tactical; Genres: FPS, Action
Abstract: Non-player characters (NPCs) commonly use waypoints for navigation through their virtual world. This article will demonstrate how preprocessing the relationships between these waypoints can be used to dynamically generate combat tactics for NPCs in a first-person shooter or action adventure game. By precalculating and storing tactical information about the relationship between waypoints in a bit string class, NPCs can quickly find valuable tactical positions and exploit their environment. Issues discussed include fast map analysis, safe pathfinding, using visibility, intelligent attack positioning, flanking, static waypoint analysis, pinch points, squad tactics, limitations, and advanced issues.

Recognizing Strategic Dispositions: Engaging the Enemy
Steven Woodcock (Wyrd Wyrks)
AI Game Programming Wisdom, 2002.
Topics: Strategy, Tactical, Movement; Genres: RTS, Strategy
Abstract:

Squad Tactics: Team AI and Emergent Maneuvers
William van der Sterren (CGF-AI)
AI Game Programming Wisdom, 2002.
Topics: Strategy, Tactical, Coordinated Movement, Cooperation; Genres: FPS, Action
Abstract: AI squad behavior is made up of coordinated individual actions towards a joint goal. There are two basic coordination styles: centralized control by a leader, and decentralized cooperation between individuals. This chapter discusses the latter style in detail. Decentralized cooperation can already be realized with minor changes to "standard individual AI". This chapter illustrates how some tactical squad maneuvers can emerge from these coordinating individual AIs, using a squad assault as an example. The limitations of the approach are illustrated using a second example: a squad ambush. This chapter precedes and complements the chapter "Squad Tactics: Planned Maneuvers".

Squad Tactics: Planned Maneuvers
William van der Sterren (CGF-AI)
AI Game Programming Wisdom, 2002.
Topics: Strategy, Tactical, Coordinated Movement, Cooperation; Genres: FPS, Action
Abstract: AI squad behavior can also be realized by designing an explicit team leader, responsible for planning and managing the squad's maneuver. This AI team leader assesses the squad's state, picks and plans the most appropriate squad maneuver. He executes the squad maneuver by issuing orders, and by interpreting feedback and information from the squad members. This is illustrated using a bounding overwatch squad advance. This centralized style to squad AI is more complex than the emergent behavior in "Squad Tactics: Team AI and Emergent Maneuvers". However, it does provide largely autonomous operating squads, able to execute complex maneuvers, and often combines well with some decentralized cooperation among squad members.

Tactical Team AI Using a Command Hierarchy

John Reynolds (Creative Asylum)
AI Game Programming Wisdom, 2002.
Topics: Strategy, Tactical, Coordinated Movement, Cooperation; Genres: FPS, Action
Abstract: Team-based AI is becoming an increasingly trendy selling point for first- and third-person action games. Often, this is limited to scripted sequences or simple "I need backup" requests. However, by using a hierarchy of decision-making, it is possible to create some very convincing teams that make decisions in real time.

Terrain Analysis in an RTS-The Hidden Giant
Daniel Higgins (Stainless Steel Software)
Game Programming Gems 3, 2002.
Topics: Tactical; Genres: RTS
Abstract:

Tactical Path-Finding with A*

William van der Sterren (CGF-AI)
Game Programming Gems 3, 2002.
Topics: A* Pathfinding, Tactical; Genres: General, FPS, RTS
Abstract: Tactical paths consider cover and stealth in addition to travel time. Although costs for cover and stealth are easily added to the A* cost function, this alone does not result in convincing tactical paths. This chapter analyzes the defects in these paths, and discusses tactical improvements: taking into account exposure time and enemy aiming behavior, and anticipating likely enemy movement. The extensions to the A* cost functions introduce additional run-time costs. This chapter discusses the costs, and provides work-arounds and optimizations to make tactical pathfinding more efficient.

Influence Mapping
Paul Tozour (Ion Storm Austin)
Game Programming Gems 2, 2001.
Topics: Tactical; Genres: RTS, Strategy
Abstract: Influence mapping is a powerful and proven AI technique for reasoning about the world on a spatial level. Although influence maps are most often used in strategy games, they have many uses in other genres as well. Among other things, an influence map allows your AI to assess the major areas of control by different factions, precisely identify the boundary of control between opposing forces, identify "choke points" in the terrain, determine which areas require further exploration, and inform the base-construction AI systems to allow you to place buildings in the most appropriate locations.

Strategic Assessment Techniques
Paul Tozour (Ion Storm Austin)
Game Programming Gems 2, 2001.
Topics: Tactical; Genres: RTS, Strategy
Abstract: This article discusses two useful techniques for strategic decision-making. These are easiest to understand in the context of strategy game AI, but they have applications to other game genres as well. The resource allocation tree describes a data structure that allows an AI system to continuously compare its desired resource allocation to its actual current resources in order to determine what to build or purchase next. The dependency graph is a data structure that represents a game's "tech tree," and we discuss a number of ways that an AI can perform inference on the dependency graph in order to construct long-term strategic plans and perform human-like reasoning about what its opponents are attempting to accomplish.

Terrain Reasoning for 3D Action Games
William van der Sterren (CGF-AI)
Game Programming Gems 2, 2001.
Topics: Tactical, Movement; Genres: Action, FPS
Abstract:

The Basics of Team AI
Clark Gibson, John O'Brien (Red Storm Entertainment)
Game Developers Conference Proceedings, 2001.
Topics: Strategy, Tactical, Coordinated Movement, Cooperation; Genres: General
Abstract: With the popularity of network play growing every day, modern gameplay is moving away from single-player modes to team-based games with cooperative goals. This change in game styles has necessitated a change in AI, from individual AIs out to hunt down a single player to team AIs that either help the player or cooperate in the hunt. This lecture discusses the basics of team-based AI, drawing on the speakers' experience with games such as Tom Clancy's Rainbow Six and Shadow Watch.

Terrain Reasoning for 3D Action Games
William van der Sterren (CGF-AI)
Game Developers Conference Proceedings, 2001.
Available Online at Gamasutra, 2001.
Topics: Tactical, Movement; Genres: Action, FPS
Abstract: Terrain reasoning in 3D action games is about understanding concepts such as stronghold, cover, ambush, and sniper location. This lecture introduces a simple yet powerful approach for terrain reasoning, enabling the AI itself to compute these concepts. Traditionally, AI relies on hints and scripts manually added by level designers to grasp the terrain. However, an AI that actually understands the terrain will respond better to dynamic situations and directions issued by the player. The terrain reasoning presented uses a waypoint graph with a high spatial density of waypoints. Due to its density, it is a good abstraction of the terrain. Being a graph, it enables computations. The lecture shows numerous examples of how to express terrain concepts in terms of this graph. A case study illustrates how situational awareness, team formations, tactical pathfinding, and adaptive tactics are implemented using both in-game and pregame terrain reasoning.

Terrain Analysis in Realtime Strategy Games
Dave Pottinger (Ensemble Studios)
Game Developers Conference Proceedings, 2000.
Topics: Tactical; Genres: RTS
Abstract: Terrain analysis is like a black-art. Every real-time strategy (RTS) game with a decent AI, whether it knows it or not, does some type of terrain analysis. This lecture examines various methods for terrain analysis as they apply to RTS games. Starting with simple topics as chokepoint determination and building placement, the class moves on to more advanced topics such as frame-based terrain representation and integration of terrain knowledge throughout the rest of the AI. Copious examples are provided by the way of numberous 2D/3D demos and code snippets.

 
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