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Artificial Intelligence: Group Cooperation

Company of Heroes Squad Formations Explained

Chris Jurney (Kaos Studios)
AI Game Programming Wisdom 4, 2008.
Abstract: This article describes all the techniques used to produce the squad formation movement in Company of Heroes. The squads controlled with this system have very tactical and visually interesting motion that handles obstacles and destructible environments with minimal impact on performance. A variety of techniques are described that, when used together, produce high quality squad motion.

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.

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.

Achieving Coordination with Autonomous NPCs

Diego Garc�s (FX Interactive)
Game Programming Gems 6, 2006.

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.

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.

Tactical Team AI Using a Command Hierarchy

John Reynolds (Creative Asylum)
AI Game Programming Wisdom, 2002.
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.

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