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Artificial Intelligence: Genre - RPG and Adventure

Stop Getting Side-Tracked by Side-Quests

Curtis Onuczko, Duane Szafron, and Jonathan Schaeffer (University of Alberta)
AI Game Programming Wisdom 4, 2008.
Abstract: Computer role-playing games often contain a complex main story-line and a series of smaller optional independent mini-stories called side-quests. Side-quests create an open world feeling, provide rewards and experience to the player for exploring optional game content, and build upon the background of the main story without affecting it. The more side-quests you add to your game, the richer the game experience will be for the player. However, more side-quests means more work generating content. This article discusses a Side-QUEst GENerator (SQUEGE) tool that will minimize the amount of time and effort needed to add side-quests to a game story. By using the patterns that exist in stories, SQUEGE intelligently provides an interesting and meaningful structure to the side-quests it produces. The result is a set of automatically generated side-quest outlines. The outlines can then be adapted, giving the game author authorial control over the side-quests generated. Finally, a programmer can use the adapted outlines to create the necessary game scripts in a straightforward manner. This process makes the creation of a large number of side-quests both easy and efficient, saving precious time and resources. The generator is easily extendable to allow for the addition of new patterns.

Spoken Dialogue Systems

Hugo Pinto and Roberta Catizone (University of Sheffield)
AI Game Programming Wisdom 4, 2008.
Abstract: This article provides an overview of modern dialog systems. We start by presenting the issues of voice recognition, language processing, dialog management, language generation and speech synthesis. Next, we analyze two robust speech-based interactive systems, NICE and TRIPS, examining how they solved each of the issues involved in spoken dialog processing. Finally, we examine the particulars of the game domain and provide suggestions on how to approach it, with illustrations from the case studies.

Implementing Story-Driven Games with the Aid of Dynamical Policy Models

Fabio Zambetta (School of Computer Science & IT, RMIT University)
AI Game Programming Wisdom 4, 2008.
Abstract: In this article we introduce a mathematical model of conflict that enhances Richardson's model of Arms Race accounting for interactive scenarios, such as the ones provided by Computer Role Playing Games. Accordingly, an HCP (Hybrid Control Process) is devised that can be combined with fuzzy rules to provide help in modeling non-linear interactive stories. The model presented here can be adopted by game AI programmers to better support the game designers' job, and to provide an interesting and unconventional type of gameplay to players. We also introduce the multi-disciplinary project Two Familes: A Tale of New Florence, which will illustrate the applications of our model.

Individualized NPC Attitudes with Social Networks

Christian J. Darken (The MOVES Institute), John D. Kelly (U.S. Navy)
AI Game Programming Wisdom 4, 2008.
Abstract: This article introduces a method for largely automating NPC changes in attitude due to a player action. The method resolves the conflicting loyalties of the NPC's to produce a single number per NPC that can be used to update the NPC's feelings toward the player and drive future player-NPC interactions. The mechanics of the method are based on a constrained linear system, so it is computationally efficient, requiring only a single matrix multiplication in many applications.

Dialogue Managers

Hugo Pinto (University of Sheffield)
AI Game Programming Wisdom 4, 2008.
Abstract: This article presents the main techniques and paradigms of dialog management, with references to games, industrial applications and academic research. We cover dialog managers based on stacks, finite-state machines, frames, inference-engines and planners. For each technique, we point its strengths, applicability and issues when integrating into a broader dialog system in a game setting.

Player Modeling for Interactive Storytelling: A Practical Approach

David Thue, Vadim Bulitko, and Marcia Spetch (University of Alberta)
AI Game Programming Wisdom 4, 2008.
Abstract: As computer graphics becomes less of a differentiator in the video game market, many developers are turning to AI and storytelling to ensure that their title stands out from the rest. To date, these have been approached as separate, incompatible tasks; AI engineers feel shackled by the constraints imposed by a story, and the story's authors fear the day that an AI character grabs their leading actor and throws him off a bridge. In this article, we attempt to set aside these differences, bringing AI engineers together with authors through a key intermediary: a player model. Following an overview of the present state of storytelling in commercial games, we present PaSSAGE (Player-Specific Stories via Automatically Generated Events), a storytelling AI that both learns and uses a player model to dynamically adapt a game's story. By combining the knowledge and expertise of authors with a learned player model, PaSSAGE automatically creates engaging and personalized stories that are adapted to appeal to each individual player.

Opinion Systems

Adam Russell (Lionhead Studios)
AI Game Programming Wisdom 3, 2006.
Abstract: Modeling the formation and effect of opinions about the player character in a simulated social environment is a difficult problem for game AI, but one increasingly worth tackling. This article discusses some of the wisdom gained during the construction of perhaps the most complex opinion system ever seen in a commercial game, that of Lionhead Studios' Fable.

Ant Colony Organization for MMORPG and RTS Creature Resource Gathering

Jason Dunn (H2Code)
AI Game Programming Wisdom 3, 2006.
Abstract: This article provides details about the implementation of ant colonies for pathfinding in massively multiplayer and real-time strategy games. Details include the effects of pheromones and individual ant behavior, as well as what variables to focus on when adapting the provided source code. Readers are taught how to control the elasticity of path seeking and path reinforcement.

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.

Building a Massively Multiplayer Game Simulation Framework, Part 2: Behavioral Modeling

Thor Alexander (Hard Coded Games)
Massively Multiplayer Game Development, 2003.

Level-Of-Detail AI for a Large Role-Playing Game

Mark Brockington (BioWare)
AI Game Programming Wisdom, 2002.
Abstract: With thousands of objects demanding AI time slices in Neverwinter Nights, it would be difficult to satisfy all creatures and maintain a playable frame rate. The level-of-detail AI schemes used allowed the game to achieve the perception of thousands of actors thinking simultaneously. The article discusses how to subdivide your game objects into categories, and how certain time-intensive actions (such as pathfinding and combat) can be reduced to make more efficient use of the time available to AI.

A Dynamic Reputation System Based on Event Knowledge

Greg Alt (Surreal Software), Kristin King
AI Game Programming Wisdom, 2002.
Abstract: This article describes a non-player character (NPC) reputation system (a mechanism for dynamically managing NPCs' opinions of each other and of the player in order to influence the NPCs' actions). Most existing reputation systems manage NPCs' opinions globally. The reputation system this article describes instead changes a specific NPC's opinions only if the NPC has direct or indirect knowledge of events that trigger a change. The article describes the data structures required for the reputation system, the way they work together to make the complete system, and the way the system fits into the overall design of NPC behavior.

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