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Artificial Intelligence: Genre - FPS and Action

The Engagement Decision

Baylor Wetzel (Brown College)
AI Game Programming Wisdom 4, 2008.
Abstract: Before every battle comes the question - can I win this battle? Should I attack or should I run? There are a variety of ways to answer this question. This article compares several, from simple power calculations through Monte Carlo simulations, discussing the pros and cons of each and the situations where each is appropriate.

Toward More Humanlike NPCs for First-/Third-Person Shooter Games

Darren Doherty and Colm O�Riordan (National University of Ireland Galway)
AI Game Programming Wisdom 4, 2008.
Abstract: This article presents ideas to provide NPCs with more humanlike qualities and a greater sense of individuality in order to create more immersive game-playing experiences that capture and hold the attention and interest of players. We discuss how providing NPCs with personality, emotions, human sensing and memory can enable them to behave in a more humanlike fashion and make NPCs more distinctive. In addition, we discuss the impact that physiological stressors might have on NPCs' behavior and the different weapon handling skills of NPCs, and how these factors can contribute to making the NPCs of FTPS games more individual and humanlike.

Learning Winning Policies in Team-Based First-Person Shooter Games

Stephen Lee-Urban, Megan Smith, and H�ctor Mu�oz-Avila (Lehigh University)
AI Game Programming Wisdom 4, 2008.
Abstract: This article presents the use of an online reinforcement learning algorithm, called RETALIATE, to automatically acquire team AI in FPS domination-style games. We present the learning problem and state model from which we draw some lessons for designing AI in these game genres.

A Practical Guide to Reinforcement Learning in First-Person Shooters

Michelle McPartland (University of Queensland)
AI Game Programming Wisdom 4, 2008.
Abstract: Reinforcement learning (RL) is well suited to FPS bots as it is able to learn short term reactivity as well as long term planning. This article briefly introduces the basics of RL and then describes a popular RL algorithm called Sarsa. It shows how RL can be used to allow FPS bots to learn some of the behaviors that are required to play deathmatch games and presents the results of several experiments.

Probabilistic Target Tracking and Search Using Occupancy Maps

Dami�n Isla (Bungie Studios)
AI Game Programming Wisdom 3, 2006.
Abstract: This article will introduce Occupancy Maps, a technique for probabilistically tracking object positions. Occupancy Maps, an application of a broader Expectation Theory, can result in more interesting and realistic searching behaviors, and can also be used to generate emotional reactions to search events, like surprise (at finding a target in an unexpected place) and confusion (at failing to find a target in an expected place). It is also argued that the use of more in-depth knowledge-modeling techniques such as Occupancy Maps can relieve some of the complexity of a traditional FSM or HFSM approach to search behavior.

Dynamic Tactical Position Evaluation

Remco Straatman and Arjen Beij (Guerrilla Games), William van der Sterren (CGF-AI)
AI Game Programming Wisdom 3, 2006.
Abstract: Dynamic tactical position evaluation is essential in making tactical shooters less linear and more responsive to the player and to changes in the game world. Designer placed hints for positioning and detailed scripting are impractical for games with unpredictable situations due to player freedom and dynamic environments. This article describes the techniques used to address these issues for Guerrilla's console titles Killzone and Shellshock Nam '67. The basic position evaluation mechanism is explained and its application when selecting tactical positions and finding tactical paths. Some alternative uses of the technique are given, such as generating intelligent scanning positions and suppressive fire, and the practical issues of configuration and performance are discussed.

Finding Cover in Dynamic Environments

Christian J. Darken (The MOVES Institute), Gregory H. Paull (Secret Level Inc.)
AI Game Programming Wisdom 3, 2006.
Abstract: In this article, we describe our approach to improved cover finding with an emphasis on adaptability to dynamic environments. The technique described here combines level annotation with the sensor grid algorithm. The strength of level annotation is its modest computational requirements. The strength of the sensor grid algorithm is its ability to handle dynamic environments and to find smaller cover opportunities in static environments. Each approach is useful by itself, but combining the two can provide much of the benefit of both. In a nutshell, our approach relies on cover information stored in the candidate cover positions placed throughout the level whenever possible and performs a focused run-time search in the immediate vicinity of the agent if the level annotation information is insufficient. This allows it to be fast and yet able to react to changes in the environment that occur during play.

Coordinating Teams of Bots with Hierarchical Task Network Planning

Hector Munoz-Avila and Hai Hoang (Lehigh University)
AI Game Programming Wisdom 3, 2006.
Abstract: This article presents the use of Hierarchical-Task-Network (HTN) representations to model strategic game AI. We demonstrate the use of hierarchical planning techniques to coordinate a team of bots in an FPS game.

Training Digital Monsters to Fight in the Real World

James Boer and John Corpening (ArenaNet)
AI Game Programming Wisdom 3, 2006.
Abstract: This article discusses how we approached and solved the problem of creating compelling AI agents for Digimon Rumble Arena 2, a one to four-player brawler. This consisted of two major challenges: How to pathfind through and respond intelligently to highly dynamic and interactive environments, and how to program a wide variety of characters to play effectively in ten different game types without incurring a combinatorial explosion of code complexity.

The Suffering: Game AI Lessons Learned

Greg Alt (Surreal Software)
AI Game Programming Wisdom 3, 2006.
Abstract: This article presents a collection of lessons learned through building and evolving an AI architecture through the development of three games for PS2, XBox, and PC: The Lord of The Rings: The Fellowship of the Ring; The Suffering; and The Suffering: Ties That Bind. The lessons cover alternate uses for A* and pathfinding, visualizations to aid AI development and debugging, benefits of a fine-grained hierarchical behavior system, and the combination of autonomy and scripted behavior for non-player characters (NPCs).

Environmental Awareness in Game Agents

Penny Sweetser (The University of Queensland)
AI Game Programming Wisdom 3, 2006.
Abstract: Agents make up an important part of game worlds, ranging from the characters and monsters that live in the world to the armies that the player controls. Despite their importance, agents in current games rarely display an awareness of their environment or react appropriately, which severely detracts from the believability of the game. Some games have included agents with a basic awareness of other agents, but they are still unaware of important game events or environmental conditions. This chapter describes an agent design that combines cellular automata for environmental modeling with influence maps for agent decision-making. The result is simple, flexible game agents that are able to respond to natural phenomena (e.g. rain or fire), while pursuing a goal.

Being a Better Buddy: Interpreting the Player's Behavior

William van der Sterren (CGF-AI)
AI Game Programming Wisdom 3, 2006.
Abstract: In shooter games, the player's activity can be interpreted by the AI to recognize certain tactical behaviors. Based on this, the AI can direct the friendly NPCs to better assist the player. To interpret and classify the player's activity, a na�ve Bayes classifier is used. With careful design of the inputs to this classifier, some post-processing of its output, and by gathering good training data, the player's activity can be interpreted in an efficient and robust way.

Using the Quantified Judgment Model for Engagement Analysis

Michael Ramsey
Game Programming Gems 6, 2006.

Automatic Cover Finding with Navigation Meshes

Borut Pfeifer (Radical Entertainment)
Game Programming Gems 5, 2005.

Fast Target Ranking Using an Artificial Potential Field

Markus Breyer (Factor 5)
Game Programming Gems 5, 2005.

Using Lanchester Attrition Models to Predict the Results of Combat

John Bolton (Page 44 Studios)
Game Programming Gems 5, 2005.

Narrative Combat: Using AI to Enhance Tension in an Action Game

Borut Pfeifer (Radical Entertainment)
Game Programming Gems 4, 2004.

Artificial Stupidity: The Art of Intentional Mistakes

Lars Lid�n
AI Game Programming Wisdom 2, 2003.
Abstract: What makes a game entertaining and fun does not necessarily correspond to making its opponent characters smarter. The player is, after all, supposed to win. However, letting a player win because of badly programmed artificial intelligence is unacceptable. Fun can be maximized when the mistakes made by computer opponents are intentional. By finely tuning opponent's mistakes, one can prevent computer opponents from looking dumb, while ensuring that the player is still capable of winning. Additionally by catching, identifying and appropriately handling genuine problems with an AI system, one can turn situations in which computer opponents would otherwise look dumb into entertainment assets. Surprisingly many game developers pay scant attention to such ideas. Developers' efforts are often so concentrated on making their computer opponents smart that they neglect to adequately address how the AI makes the game fun.

Arcade AI Doesn't Have to Be Dumb

Steven Woodcock (GameAI.com)
AI Game Programming Wisdom 2, 2003.
Abstract: Good game AI is tricky no write no matter what your resources are. When you're faced with limited CPU and RAM, such as with an arcade game or on a handheld, it can be nearly impossible. Arcade AI Doesn't Have to be Dumb covers various techniques used in the development of the Sega arcade game Behind Enemy Lines which helped give its AIs a bit more spontaneity and seeming intelligence than found in most shooters while not using up much memory or CPU in the process.

Jumping, Climbing, and Tactical Reasoning: How to Get More Out of a Navigation System

Christopher Reed, Benjamin Geisler (Raven Software / Activision)
AI Game Programming Wisdom 2, 2003.
Abstract: Few AI related systems are more common and pervasive in games than character navigation. As 3D game engines become more and more complex, characters will look best if they too adapt with equally complex behavior. From opening a door, to hopping over an errant boulder and crouching behind it, keeping AI tied to the environment of your game is often one of the most difficult and important challenges.

Typically these complex behaviors are handled by scripts or a hand coded decision maker. However, we will show that the points and edges within a navigation system are a natural place to store environment specific information. It is possible to automatically detect many properties about the area around a point or edge. This approach allows an AI character to make use of embedded environment information for tactical reasoning as well as low level animation and steering.

Hunting Down the Player in a Convincing Manner

Alex McLean (Pivotal Games Ltd.)
AI Game Programming Wisdom 2, 2003.
Abstract: This article is concerned with how to make a game character convincingly hunt or search towards a goal. Gamers expect intelligent behavior from opponents but sometimes it's all too easy to let the AI cheat a little too much. In order to bring about believable searching behavior it is often not sufficient to simply route a game character directly towards its goal; the path will be too direct, too contrived and generally afford little in the way of gameplay possibilities. We must ensure that the character explores and looks like it's trying to find its goal by a process of search rather than direct, shortest-path route following. This article shows how to do this effectively and with low processing cost. The end result is convincing searching and/or hunting behavior that gradually homes in on a goal.

Simple parameters are available to control how quickly goal discovery is likely to happen and also the directness of the resultant path. The method assumes the existence of a working pathfinding/routing system with the described technique being equally suited to 2D and 3D environments. The discussion will show the benefits and scope of indirect paths in terms of the opportunities offered for gameplay, perceived character intelligence and believability.

Constraining Autonomous Character Behavior with Human Concepts

Jeff Orkin (Monolith Productions)
AI Game Programming Wisdom 2, 2003.
Abstract: A current trend in Game AI is the move from scripted to autonomous character behavior. Autonomous behavior offers several benefits. Autonomous characters can handle unexpected events that a script might not have anticipated, producing emergent gameplay. Level designers can focus on creating worlds packed with opportunities for characters to showcase their behaviors, rather than getting bogged down scripting the actions of individual characters. Various articles have described how to design goal-based autonomous behavior, where characters select the most relevant behavior based on their desires, sensory input, and proximity to objects of interest. In theory it sounds simple enough to drop a character with a palette of goals into a level filled with tagged objects, and let him take care of himself. In practice, there are many additional factors that need to be considered to get believable behavior from an autonomous character. This article presents a number of factors that should be considered as inputs into the relevancy calculation of a character's goals, in order to produce the most believable decisions. These factors are based on findings encountered during the developement of Monolith Production's No One Lives Forever 2: A Spy in H.A.R.M.'s Way.

Simple Techniques for Coordinated Behavior

Jeff Orkin (Monolith Productions)
AI Game Programming Wisdom 2, 2003.
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.
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.

Using a Spatial Database for Runtime Spatial Analysis

Paul Tozour (Retro Studios)
AI Game Programming Wisdom 2, 2003.
Abstract: AI developers have employed a number of different techniques for performing spatial reasoning about a game world using precomputed "hints" placed by level designers or automated game-world analysis tools. However, as game worlds increasingly feature larger numbers of AI characters and moveable physically-modeled objects, it becomes increasingly important to model the ways that the dynamic aspects of the ever-changing game world influence an AI's spatial reasoning. We discuss a spatial database technique that allows you to perform spatial reasoning about any number of different factors that can potentially affect an AI agent's reasoning about the game environment and techniques for combining multiple factors together to construct desirability heuristics. A spatial database can also allow you to implicitly coordinate the activities of multiple AI agents simply by virtue of sharing the same data structure.

Strategic and Tactical Reasoning with Waypoints

Lars Lid�n (Valve Software)
AI Game Programming Wisdom, 2002.
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.

Squad Tactics: Team AI and Emergent Maneuvers

William van der Sterren (CGF-AI)
AI Game Programming Wisdom, 2002.
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.
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.

First-Person Shooter AI Architecture

Paul Tozour (Ion Storm Austin)
AI Game Programming Wisdom, 2002.
Abstract: This article provides a basic introduction to building an AI architecture for a first-person shooter game (such as Quake or Unreal) or a first-person sneaker (such as Thief: The Dark Project). We discuss the major components of an FPS AI (including specific subsystems for animation, movement and pathfinding, behavior, combat, sensory modelling, and scripting and trigger systems) and how those components should fit together.

The Basics of Ranged Weapon Combat

Paul Tozour (Ion Storm Austin)
AI Game Programming Wisdom, 2002.
Abstract: This article gives a brief introduction to the problems of firing ranged weapons. We discuss to-hit rolls, aim point selection, ray-testing, avoiding friendly fire incidents, dead reckoning, and calculating weapon trajectories for ballistic weapons.

Tactical Path-Finding with A*

William van der Sterren (CGF-AI)
Game Programming Gems 3, 2002.

Terrain Reasoning for 3D Action Games

William van der Sterren (CGF-AI)
Game Programming Gems 2, 2001.

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