Construction and Adaptation of AI Behaviors in Computer Games

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

Real-Time Case-Based Reasoning for Interactive Digital Entertainment

(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:

• The social gaming application is at: http://MakeMePlayMe.com and is free to play!
• The AI engine (based on real-time case-based reasoning) is open source for non-commercial use: https://sourceforge.net/projects/darmok2
• Games are at: https://sourceforge.net/projects/mmpmgames and it’s easy to contribute your own.

Learn more about the algorithms:

• Planning: http://cognitivecomputing.wordpress.com/2009/01/20/on-line-case-based-planning
• Learning: http://cognitivecomputing.wordpress.com/2009/07/12/learning-from-human-demonstrations-for-real-time-case-based-planning

View the talk:

www.sais.se/blog/?p=57

View the slides:

User-Generated AI for Interactive Digital Entertainment

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 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 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 argue that User-Generated AI is the next big frontier in the rapidly growing Social Gaming area. From Sims to Risk to World of Warcraft, end users want to create, modify, and share not only the appearance but the “minds” of their characters. I present my recent research on intelligent technologies to assist Game AI authors, and 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 future of AI-based Interactive Digital Entertainment.

CMU Robotics & Intelligence Seminar, September 28, 2009
Carnegie-Mellon University, Pittsburgh, PA.

MIT Media Lab Colloquium, January 25, 2010
Massachusetts Institute of Technology, Cambridge, MA.

Stanford Media X Philips Seminar, February 1, 2010
Stanford University, Stanford, CA.

Pixar Research Seminar, February 2, 2010

Try it yourself:

• The social gaming application is at: http://MakeMePlayMe.com and is free to play!
• The AI engine (based on real-time case-based reasoning) is open source for non-commercial use: https://sourceforge.net/projects/darmok2
• Games are at: https://sourceforge.net/projects/mmpmgames and it’s easy to contribute your own.

Learn more about the algorithms:

• Planning: http://cognitivecomputing.wordpress.com/2009/01/20/on-line-case-based-planning
• Learning: http://cognitivecomputing.wordpress.com/2009/07/12/learning-from-human-demonstrations-for-real-time-case-based-planning

View the talk:

www.sais.se/blog/?p=57

View the slides:

Drama Management and Player Modeling for Interactive Fiction Games

A growing research community is working towards employing drama management components in story-based games. These components gently guide the story towards a narrative arc that improves the player’s gaming experience. In this paper we evaluate a novel drama management approach deployed in an interactive fiction game called Anchorhead. This approach uses player’s feedback as the basis for guiding the personalization of the interaction.

The results indicate that adding our Case-based Drama manaGer (C-DraGer) to the game guides the players through the interaction and provides a better overall player experience. Unlike previous approaches to drama management, this paper focuses on exhibiting the success of our approach by evaluating results using human players in a real game implementation. Based on this work, we report several insights on drama management which were possible only due to an evaluation with real players.

Read the paper:

Drama Management and Player Modeling for Interactive Fiction Games

by Manu Sharma, Santi Ontañón, Manish Mehta, Ashwin Ram

Computational Intelligence, 26(2):183-211, 2010.

www.cc.gatech.edu/faculty/ashwin/papers/er-09-10.pdf

www3.interscience.wiley.com/journal/123387570/abstract

An Ensemble Learning and Problem Solving Architecture for Airspace Management

In this paper we describe the application of a novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspace need to be reconciled and managed automatically. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of integrated learning and reasoning (ILR) systems coordinated by a central meta-reasoning executive (MRE). Each ILR learns independently from the same training example and contributes to problem-solving in concert with other ILRs as directed by the MRE. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Further, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.

Read the paper:

An Ensemble Learning and Problem Solving Architecture for Airspace Management

by XS Zhang et al.

International Conference on Innovative Applications of Artificial Intelligence (IAAI-09), Pasadena, CA, July 2009

www.cc.gatech.edu/faculty/ashwin/papers/er-09-03.pdf

Goal-Driven Learning in the GILA Integrated Intelligence Architecture

Goal Driven Learning (GDL) focuses on systems that determine by themselves what has to be learned and how to learn it. Typically GDL systems use meta-reasoning capabilities over a base reasoner, identifying learning goals and devising strategies. In this paper we present a novel GDL technique to deal with complex AI systems where the meta-reasoning module has to analyze the reasoning trace of multiple components with potentially different learning paradigms. Our approach works by distributing the generation of learning strategies among the different modules instead of centralizing it in the meta-reasoner. We implemented our technique in the GILA system, that works in the airspace task orders domain, showing an increase in performance.

Read the paper:

Goal-Driven Learning in the GILA Integrated Intelligence Architecture

by Jai Radhakrishnan, Santi Ontañón, Ashwin Ram

International Joint Conference on Artificial Intelligence (IJCAI-09), Pasadena, CA, July 2009

www.cc.gatech.edu/faculty/ashwin/papers/er-09-02.pdf

Argumentation-Based Information Exchange in Prediction Markets

We investigate how argumentation processes among a group of agents may affect the outcome of group judgments. In particular we focus on prediction markets (also called information markets). We investigate how the existence of social networks (that allow agents to argue with one another to improve their individual predictions) effect on group judgments.

Social networks allow agents to exchange information about the group judgment by arguing about the most likely choice based on their individual experience. We develop an argumentation-based deliberation process by which the agents acquire new and relevant information. Finally, we experimentally assess how different social network connectivity affect group judgment.

Read the paper:

Argumentation-based Information Exchange in Prediction Markets

by Santi Ontañón and Enric Plaza

in ArgMAS 2008, pp. 181 – 196

www.cc.gatech.edu/faculty/ashwin/papers/er-08-12.pdf

Case-Based Reasoning for Gas Turbine Diagnostics

General Electric used case-based reasoning for gas turbine diagnostics at their monitoring and diagnostics center in Atlanta, GA. This application had requirements that included accuracy, maintainability, modularity, parameterization, robustness, and integration of the system into an existing infrastructure. The CBR system has a modular “plug and play” architecture to facilitate experimentation and optimization. It was integrated into the production environment in 2004. The CBR system is currently in a trial deployment where diagnoses made by the system are created along with the previous process of using human-generated diagnosis.

Case-Based Reasoning for Gas Turbine Diagnostics

by Mark Devaney, Bill Cheetham

18th International FLAIRS Conference (FLAIRS-05), Clearwater, FL, May 2005

www.cc.gatech.edu/faculty/ashwin/papers/er-05-05.pdf

Plan Recognition in Large-Scale Multi-Agent Tactical Domains

This research addresses the task of representing and recognizing events in a tactical domain from large-scale spatio-temporal data under conditions of limited observability and high noise with real-time response constraints.  These assumptions differ from those traditionally made in  plan recognition and produce a problem that combines aspects of plan recognition, pattern recognition and object tracking. This research provides evidence that parsimonious qualitative representations used to represent pair-wise interactions among agents can be combined to identify large-scale group behaviors that form the basis of increasingly complex patterns of activity.

A comprehensive software application was constructed to demonstrate the claims of the thesis by evaluating performance on a real-world problem involving the recognition of a tactical maneuver in actual US Army training battles.  Evaluations were conducted and performance evaluated by both novices and active military subject matter experts.

Plan Recognition in Large-Scale Multi-Agent Tactical Domains

by Mark Devaney

PhD Thesis, College of Computing, Georgia Institute of Technology, Atlanta, GA, 2003

www.cc.gatech.edu/faculty/ashwin/papers/git-cc-03-01.ps.zip

IRIA: The Information Research Intelligent Assistant

The explosion of information in the modern environment demands the ability to collect, organize, manage, and search large amounts of information across a wide variety of real-world applications. The primary tools available for such tasks are large-scale database systems and keyword-based document search techniques. However, such tools are rapidly proving inadequate: traditional database systems do not enable ready access to relevant knowledge, prompting a market of add-ons and existing search techniques are insufficiently precise or selective to support such tasks, leading to consumer exasperation. In the end users are left unsatisfied, confronted with a sea of unorganized and unhelpful data. A new approach is needed.

The Information Research Intelligent Assistant (IRIA) is an integrated information retrieval architecture that addresses this problem. IRIA enables a user or workgroup to build a personalized map of the relevant information available in a database, intranet, or internet, and the ability to find, add, and use information quickly and easily. An IRIA-based intelligent information management system acts as an autonomous assistant to a user working on a task, working unobtrusively in the background to learn both the user’s interests and the resources available to satisfy those interests. This approach enables “reminding engines” which monitor a user’s work to proactively find and recommend useful information as well as “workgroup memories” which learn from a user’s behavior to build a comprehensive knowledge map of a particular area of interest.

In empirical tests, IRIA has demonstrated the ability to monitor a user’s progress on a task (specifically, web search) and proactively find and recommend information relevant to that task based on the context and history of the user’s interactions with the system. IRIA further demonstrated that it could provide collaborative facilities to the workgroup and that it could learn and improve its knowledge map over time.

Read the paper:

IRIA: The Information Research Intelligent Assistant

by Anthony Francis, Mark Devaney, Ashwin Ram

International Conference on Artificial Intelligence (ICAI-00), Las Vegas, Nevada

www.dresan.com/research/publications/icai-2000.html