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Flocking / Formations / Coordinated Movement


Attractors and Repulsors
John Olsen (Microsoft)
Game Programming Gems 4, 2004.
Topics: Pathfinding, Movement, Flocking; Genres: General
Abstract: Helping your AI-controlled entities know what to stay close to and what to avoid can go a long way in helping to generate realistic simulated behaviors. A large part of tasks such as walking through a crowd, racing down a track, or flying through space consists of staying close to some objects and avoiding others. Attractors and repulsors can be used for many purposes, including simulating flocking behaviors, collision avoidance for racing, and tracking opponents in 2D or 3D environments. We can build attraction curves - functions that determine the level of push and pull between objects - to influence the movement of our AI-controlled objects. We can also combine simple curves into more complex composite curves to build interesting emergent behaviors.

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 otherís 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."

Simple Swarms as an Alternative to Flocking
Tom Scutt (Gatehouse Games)
AI Game Programming Wisdom, 2002.
Topics: Flocking, Movement; Genres: General
Abstract: Craig Reynold's flocking algorithms have been well documented and are highly successful at producing natural-looking movement in groups of agents. However, the algorithms can be computationally expensive, especially where there are a large number of agents or a complex environment to detect against. For this reason, they are not always suited to real-time applications such as video games. This article details a much simpler algorithm for producing natural-looking movement in large swarms of creatures involving tens or hundreds of agents. Although this algorithm cannot guarentee separation of creatures within the swarm, the overall impression organic movement is very convincing.

Formations
Chad Dawson (Stainless Steel Studios)
AI Game Programming Wisdom, 2002.
Topics: Formations, Coordinated Movement; Genres: RPS, RTS, FPS, Action
Abstract: In games today, formations are expected for any type of cohesive group movement. From squad-based first-person shooters to sports sims to real-time strategy games, anytime that a group is moving or working together it is expected to do so in an orderly, intelligent fashion. This article will cover standard military formations, facing issues, mixed formations, spacing distance, ranks, unit mobility, group pathfindng, and dealing with obstacles.

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.

Simulating Real Animal Behavior
Sandeep V. Kharkar (Microsoft)
AI Game Programming Wisdom, 2002.
Topics: Animal AI, Flocking; Genres: Hunting, General
Abstract:

Social Activities: Implementing Wittgenstein
Tom Barnet-Lamb (Lionhead Studios), Richard Evans (Lionhead Studios)
Game Developers Conference Proceedings, 2002.
Topics: Cooperation, Coordinated Movement; Genres: General
Abstract: In Black & White, a number of different "group minds" were implemented, such as reactions, towns, and dances. These activities had a lot in common, but were implemented completely separately. What was in common between them was not captured explicitly. This meant that adding a new type of activity was quite difficult and time-consuming: all the bookkeeping had to be re-implemented each time. This lecture proposes a new system that captures what is common between different activities and makes it as easy as possible to add a new type. Furthermore, and more excitingly, this new system allows the mod community to add new activities after the game has been released. This is a good idea because the perceived "depth" of an AI agent is largely a function of the number of different sorts of activities it can engage in. By making it optimally easy to add new activities, we increase the total number of activities we can implement. In Black & White, the number of activities implemented was rather small. With this new system, we hope to have hundreds of different activities.

Flocking with Teeth: Predators and Prey

Steven Woodcock (Wyrd Wyrks)
Game Programming Gems 2, 2001.
Topics: Flocking, Movement; Genres: General
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.

Flocking: A Simple Technique for Simulating Group Behavior

Steven Woodcock (Wyrd Wyrks)
Game Programming Gems, 2000.
Topics: Movement, Flocking; Genres: General
Abstract:

Interaction with Groups of Autonomous Characters
Craig Reynolds
Game Developers Conference Proceedings, 2000.
Topics: Movement, Flocking, General; Genres: General
Abstract: This lecture presents a methodology for contructing large groups of autonomous characters which respond to the user's interaction (as well as to each other and their environment) in real time. The characters are based on steering controllers under the direction of a simple mental model which mediates between several conflicting behavioral goals. The characters are represented graphically by 3D models with a library of animated motions which must be choreographed by the behavioral controllers.

Formation-Based Pathfinding with Real-World Vehicles
Jim Van Verth, Victor Brueggemann, Jon Owen, Peter McMurry
Game Developers Conference Proceedings, 2000.
Topics: Pathfinding, Movement, Formations; Genres: General
Abstract: A number of papers and articles have been written about formation-based pathfinding. Many of them, however, make the assumption that the units involved can move in any direction and can turn on a dime. This lecture presents our solution in Force 21 to the problem of controlling real-world vehicles in formation, what we learned from it, and what we will do differently the next time.

Implementing Coordinated Movement
Dave Pottinger (Ensemble Studios)
Game Developer Magazine, February 1999.
Available Online at Gamasutra, 1999.
Topics: Formations, Pathfinding, Coordinated Movement; Genres: RTS, General
Abstract:

Coordinated Unit Movement
Dave Pottinger (Ensemble Studios)
Game Developer Magazine, January 1999.
Available Online at Gamasutra, 1999.
Topics: Formations, Pathfinding, Coordinated Movement; Genres: RTS, General
Abstract:

Steering Behaviors for Autonomous Characters
Craig Reynolds
Game Developers Conference Proceedings, 1999.
PDF and HTML versions of the paper
Topics: Movement, Flocking; Genres: General
Abstract: This lecture presents solutions for one requirement of autonomous characters in animation and games: the ability to navigate around their world in a lifelife and improvisational manner. These steering behaviors are largely independent of the particulars of the character's means of locomotion. Combinations of steering behaviors can be used to achieve higher-level goals (for example, get from here to there while avoiding obstacles, go down this corridor, join that group of characters). This lecture divides motion behavior into three levels. It focuses on the middle level of steering behaviors, briefly describes the lower level of locomotion, and touches lightly on the higher level of goal setting and strategy.

 
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