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Multi-Agent Cooperation
Simple Techniques for Coordinated Behavior
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 |  |
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.
Squad Tactics: Team AI and Emergent Maneuvers
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
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 |  |
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.
Agent Cooperation in FSMs for Baseball
Topics: FSM, State Machines, Cooperation; Genres: Baseball, Sports
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.
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.
Making the Play: Team Cooperation in Microsoft Baseball 3D
Steve Rabin (WizBang Software Productions)
Computer Game Developers Conference Proceedings, 1998.
Topics: Architecture, Cooperation; Genres: Sports, Baseball
Abstract: Explains a technique for coordinating 9 fielders during defensive play so that they cooperate and can dynamically cover each other's positions if needed. The technique involves using a manager entity to assign jobs by priority based on the ball and baserunner positions, instead of each player having a static job, like cover first base.
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