Archive for the ‘Game AI’ Category

24 May

Learning from Demonstration to be a Good Team Member in a Role Playing Game

Posted by cognitivecomputing in Agents, Game AI. Tagged: , , , , , .

We present an approach that uses learning from demonstration in a computer role playing game. We describe a behavior engine that uses case-based reasoning. The behavior engine accepts observation traces of human playing decisions and produces a sequence of actions which can then be carried out by an artificial agent within the gaming environment. Our work focuses on team-based role playing games, where the agents produced by the behavior engine act as team members within a mixed human-agent team. We present the results of a study we conducted, where we assess both the quantitative and qualitative performance difference between human-only teams compared with hybrid human-agent teams.

Learning from Demonstration to be a Good Team Member in a Role Playing Game

by Michael Silva, Silas McCroskey, Jonathan Rubin, Michael Youngblood, Ashwin Ram

26th International FLAIRS Conference on Artificial Intelligence (FLAIRS-13).
www.cc.gatech.edu/faculty/ashwin/papers/er-13-01.pdf

30 Sep

Robust and Authorable Multiplayer Storytelling Experiences

Posted by cognitivecomputing in Game AI. Tagged: , , , .

Interactive narrative systems attempt to tell stories to players capable of changing the direction and/or outcome of the story. Despite the growing importance of multiplayer social experiences in games, little research has focused on multiplayer interactive narrative experiences. We performed a preliminary study to determine how human directors design and execute multiplayer interactive story experiences in online and real world environments. Based on our observations, we developed the Multiplayer Storytelling Engine that manages a story world at the individual and group levels. Our flexible story representation enables human authors to naturally model multiplayer narrative experiences. An intelligent execution algorithm detects when the author’s story representation fails to account for player behaviors and automatically generates a branch to restore the story to the authors’ original intent, thus balancing authorability against robust multiplayer execution.

Read the paper:

Robust and Authorable Multiplayer Storytelling Experiences

by  Mark Riedl, Boyang Li, Hua Ai, Ashwin Ram

in Seventh International Conference on AI and Interactive Digital Entertainment (AIIDE-2011).
www.cc.gatech.edu/faculty/ashwin/papers/er-11-06.pdf

19 Aug

Construction and Adaptation of AI Behaviors in Computer Games

Posted by cognitivecomputing in Agents, Game AI, Learning. Tagged: , , , , , , .

Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial digital games. AI for non playing characters (NPC) in computer games tends to come from people with computing skills well beyond the average user. The prime reason behind the lack of involvement of novice users in creating AI behaviors for NPC’s in computer games is that construction of high quality AI behaviors is a hard problem.

There are two reasons for it. First, creating a set of AI behavior requires specialized skills in design and programming. The nature of the process restricts it to certain individuals who have a certain expertise in this area. There is little understanding of how the behavior authoring process can be simplified with easy-to-use authoring environments so that novice users (without programming and design experience) can carry out the behavior authoring task. Second, the constructed AI behaviors have problems and bugs in them which cause a break in player expe- rience when the problematic behaviors repeatedly fail. It is harder for novice users to identify, modify and correct problems with the authored behavior sets as they do not have the necessary debugging and design experience.

The two issues give rise to a couple of interesting questions that need to be investigated: a) How can the AI behavior construction process be simplified so that a novice user (without program- ming and design experience) can easily conduct the authoring activity and b) How can the novice users be supported to help them identify and correct problems with the authored behavior sets? In this thesis, I explore the issues related to the problems highlighted and propose a solution to them within an application domain, named Second Mind(SM). In SM novice users who do not have expertise in computer programming employ an authoring interface to design behaviors for intelligent virtual characters performing a service in a virtual world. These services range from shopkeepers to museum hosts. The constructed behaviors are further repaired using an AI based approach.

To evaluate the construction and repair approach, we conduct experiments with human subjects. Based on developing and evaluating the solution, I claim that a design solution with behavior timeline based interaction design approach for behavior construction supported by an understandable vocabulary and reduced feature representation formalism enables novice users to author AI behaviors in an easy and understandable manner for NPCs performing a service in a virtual world. I further claim that an introspective reasoning approach based on comparison of successful and unsuccessful execution traces can be used as a means to successfully identify breaks in player experience and modify the failures to improve the experience of the player interacting with NPCs performing a service in a virtual world.

The work contributes in the following three ways by providing: 1) a novel introspective reasoning approach for successfully detecting and repairing failures in AI behaviors for NPCs performing a service in a virtual world.; 2) a novice user understandable authoring environment to help them create AI behaviors for NPCs performing a service in a virtual world in an easy and understandable manner; and 3) Design, debugging and testing scaffolding to help novice users modify their authored AI behaviors and achieve higher quality modified AI behaviors compared to their original unmodified behaviors.

Read the dissertation:

Construction and Adaptation of AI Behaviors in Computer Games

by Manish Mehta

PhD dissertation, College of Computing, Georgia Institute of Technology, August 2011.

smartech.gatech.edu/handle/1853/42724

17 May

A Case Base Planning Approach for Dialogue Generation in Digital Movie Design

Posted by cognitivecomputing in Game AI. Tagged: , , .

We apply case based reasoning techniques to build an intelligent authoring tool that can assist nontechnical users with authoring their own digital movies. In this paper, we focus on generating dialogue lines between two characters in a movie story. We use Darmok2, a case based planner, extended with a hierarchical plan adaptation module to generate movie characters’ dialogue acts with regard to their emotion changes. Then, we use an information state update approach to generate the actual content of each dialogue utterance. Our preliminary study shows that the extended planner can generate coherent dialogue lines which are consistent with user designed movie stories using a small case base authored by novice users. A preliminary user study shows that users like the overall quality of our system generated movie dialogue lines.

Read the paper:

A Case Base Planning Approach for Dialogue Generation in Digital Movie Design

by Sanjeet Hajarnis, Christina Leber, Hua Ai, Mark Riedl, Ashwin Ram

19th International Conference on Case-Based Reasoning (ICCBR-11), London. 
www.cc.gatech.edu/faculty/ashwin/papers/er-11-05.pdf

7 Apr

Augmenting Human Innovation with Social Cognition

Posted by cognitivecomputing in Agents, Game AI, Health & Wellness, Language, Learning, Opinion, Talks, Web / Web 2.0.

Social Media is everywhere: photos, videos, news, blogs, art, music, games… even business, finance, healthcare, government, design, and other serious applications are going social. These social media gave given rise to Social Cognition. What began with sharing has moved to creation. Consumers have become producers, and commerce has become a conversation.

Due to these conversations, individuals are no longer alone; whether you’re making a life decision, solving a critical business problem, or merely looking for a restaurant, your social graphs are available to augment your decision making process. These graphs have no geographic boundaries; professional networks are worldwide, and information streams from far corners of the globe into the palm of your hand.

Beyond media and commerce, the next big disruption is innovation. Humans everywhere want to innovate, and Social Cognition can augment human innovation in many everyday and expert domains.

I discuss three human capabilities that are amenable to social augmentation: problem solving, learning, and creativity. I illustrate them with challenge problems from my work: 1) healthcare: helping consumers find relevant health information without search; 2) energy: helping experts troubleshoot complex turbine failures; 3) learning: scaling education to a hundred million people; and 4) creativity: enabling average users to create artificial intelligence agents without programming, and 2) learning: scaling education to a hundred million people.

These technologies blend Cognitive Systems (artificial intelligence) and Cognitive Science (human cognition) in products that both exhibit and support cognition in large-scale social communities. This research not only provides scientific insight but also creates disruptive business opportunities.

Invited talk at PARC, Palo Alto, CA, April 7, 2011.
 
Invited talk at Wright State University, Center of Excellence in Human-Centered Innovation, Dayton, OH, October 24, 2010.
 

View the slides:

25 Feb

Case-Based Reasoning and User-Generated AI for Real-Time Strategy Games

Posted by cognitivecomputing in Game AI, Learning. Tagged: , , , .

Creating AI for complex computer games requires a great deal of technical knowledge as well as engineering effort on the part of game developers. This paper focuses on techniques that enable end-users to create AI for games without requiring technical knowledge by using case-based reasoning techniques.

AI creation for computer games typically involves two steps: a) generating a first version of the AI, and b) debugging and adapting it via experimentation. We will use the domain of real-time strategy games to illustrate how case-based reasoning can address both steps.

Read the paper:

Case-Based Reasoning and User-Generated AI for Real-Time Strategy Games

by Santi Ontañón and Ashwin Ram

In P. Gonzáles-Calero & M. Gomez-Martín (ed.), AI for Games: State of the Practice, 2011.
www.cc.gatech.edu/faculty/ashwin/papers/er-11-02.pdf

24 Feb

Learning Opponent Strategies through First Order Induction

Posted by cognitivecomputing in Agents, Game AI, Learning.

In a competitive game it is important to identify the opponent’s strategy as quickly and accurately as possible so that an effective response can be staged. In this vein, this paper summarizes our work in exploring the use of the first order inductive learning (FOIL) algorithm for learning rules which can be used to represent opponent strategies. Specifically, we use these learned rules to perform plan recognition and classify an opponent strategy as one of multiple learned strategies. Our experiments validate this novel approach in a simple real-time strategy game.

Read the paper:

Learning Opponent Strategies through First Order Induction

by Kathryn Genter, Santiago Ontañón, Ashwin Ram

FLAIRS-11 International Conference on Artificial Intelligence
www.cc.gatech.edu/faculty/ashwin/papers/er-11-03.pdf

20 Jul

MMPM: A Generic Platform for Case-Based Planning Research

Posted by cognitivecomputing in Game AI. Tagged: , , , .

Computer games are excellent domains for research and evaluation of AI and CBR techniques. The main drawback is the effort needed to connect AI systems to existing games. This paper presents MMPM, a middleware platform that supports easy connection of AI techniques with games. We will describe the MMPM architecture, and compare with related systems such as TIELT.

Read the paper:

MMPM: A Generic Platform for Case-Based Planning Research

by Pedro Pablo Gómez-Martín, David Llansó, Marco Antonio Gómez-Martín, Santiago Ontañón, Ashwin Ram

ICCBR-2010 Workshop on Case-Based Reasoning for Computer Games
www.cc.gatech.edu/faculty/ashwin/papers/er-10-03.pdf

19 Jul

Real-Time Case-Based Reasoning for Interactive Digital Entertainment

Posted by cognitivecomputing in Agents, Game AI, Learning, Talks, Web / Web 2.0. Tagged: , , , , , , , , , .

(Click image to view the video – it’s near the bottom of the new page.)

User-generated content is everywhere: photos, videos, news, blogs, art, music, and every other type of digital media on the Social Web. Games are no exception. From strategy games to immersive virtual worlds, game players are increasingly engaged in creating and sharing nearly all aspects of the gaming experience: maps, quests, artifacts, avatars, clothing, even games themselves. Yet, there is one aspect of computer games that is not created and shared by game players: the AI. Building sophisticated personalities, behaviors, and strategies requires expertise in both AI and programming, and remains outside the purview of the end user.

To understand why authoring Game AI is hard, we need to understand how it works. AI can take digital entertainment beyond scripted interactions into the arena of truly interactive systems that are responsive, adaptive, and intelligent. I will discuss examples of AI techniques for character-level AI (in embedded NPCs, for example) and game-level AI (in the drama manager, for example). These types of AI enhance the player experience in different ways. The techniques are complicated and are usually implemented by expert game designers.

I propose an alternative approach to designing Game AI: Real-Time CBR. This approach extends CBR to real-time systems that operate asynchronously during game play, planning, adapting, and learning in an online manner. Originally developed for robotic control, Real-Time CBR can be used for interactive games ranging from multiplayer strategy games to interactive believable avatars in virtual worlds.

As with any CBR technique, Real-Time CBR integrates problem solving with learning. This property can be used to address the authoring problem. I will show the first Web 2.0 application that allows average users to create AIs and challenge their friends to play them—without programming. I conclude with some thoughts about the role of CBR in AI-based Interactive Digital Entertainment.

Keynote talk at the Eighteenth Conference on Pattern Recognition and Artificial Intelligence (RFIA-12), Lyon, France, February 5, 2012.
Slides and video here: rfia2012.liris.cnrs.fr/doku.php?id=pub:ram
 
Keynote talk at the Eleventh Scandinavian Conference on Artificial Intelligence (SCAI-11), Trondheim, Norway, May 25, 2011.
 
Keynote talk at the 2010 International Conference on Case-Based Reasoning (ICCBR-10), Alessandria, Italy, July 22, 2010.
 
GVU Brown Bag talk, October 14, 2010. Watch the talk here: www.gvu.gatech.edu/node/4320 
 
Try it yourself:
Learn more about the algorithms:
View the talk:
www.sais.se/blog/?p=57

View the slides:

10 Jul

Meta-Level Behavior Adaptation in Real-Time Strategy Games

Posted by cognitivecomputing in Agents, Game AI, Learning. Tagged: , , , , , , .

AI agents designed for real-time settings need to adapt themselves to changing circumstances to improve their performance and remedy their faults. Agents typically designed for computer games, however, lack this ability. The lack of adaptivity causes a break in player experience when they repeatedly fail to behave properly in circumstances unforeseen by the game designers.

We present an AI technique for game-playing agents that helps them adapt to changing game circumstances. The agents carry out runtime adaptation of their behavior sets by monitoring and reasoning about their behavior execution and using this reasoning to dynamically revise their behaviors. The evaluation of the behavior adaptation approach in a complex real-time strategy game shows that the agents adapt themselves and improve their performance by revising their behavior sets appropriately.

Read the paper:

Meta-Level Behavior Adaptation in Real-Time Strategy Games

by Manish Mehta, Santi Ontañon, Ashwin Ram

ICCBR-10 Workshop on Case-Based Reasoning for Computer Games, Alessandria, Italy, 2010.
www.cc.gatech.edu/faculty/ashwin/papers/er-10-02.pdf
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