Eating an octopus tentacle
in Hakodate, Japan, May 2006

Mark J. Nelson

Georgia Tech College of Computing
mnelson@cc (.gatech.edu)

I'm a PhD student in computer science working in artificial intelligence.

My dissertation work-in-progress is on automated support for game design. This has a number of targets, from enabling game-design experts to prototype and refine design ideas more quickly; to enabling novices to build interesting small games; to even allowing games or parts of games to be automatically generated.

I've done some work on drama management, and longer ago on the periphery of computer music. Lurking in the todo pile is some as-yet-unpublished stuff on machine learning, mainly local regression methods and regression forests.

I'm working in Michael Mateas's Expressive Intelligence Studio (EIS), now located at the University of California, Santa Cruz. I've also collaborated with Charles Isbell's pfunk research group, and Alex Gray's FASTlab.

You might also be interested in an index of newsgames I maintain.

Publications

Automated Support for Game Design

We feel that there's huge potential for automated methods to assist in game design, from helping experts prototype and design games faster; to helping novices design games at all; to allowing games to dynamically customize themselves. Some previous research has looked at automatically designing variations on certain classes of games (mainly chess variants), but we'd like to look at the whole problem: game mechanics, concrete audiovisual representation, real-world references, and player input. In addition, we'd like to stress the interactive support aspect: spitting out a variant on a game is not the only (or even, necessarily, the most useful) application of automated game design. The papers below are preliminary work outlining some of the ideas in the context of reasoning about a game's real-world references. A paper on reasoning about a game's mechanics should be forthcoming shortly.

Mark J. Nelson and Michael Mateas.
An interactive game-design assistant.
In Proceedings of the 2008 International Conference on Intelligent User Interfaces (IUI 2008), pages 90–98, Maspalomas, Gran Canaria, Spain, January 2008.
Mark J. Nelson and Michael Mateas.
Towards automated game design.
In AI*IA 2007: Artificial Intelligence and Human-Oriented Computing, pages 626–637. Springer, 2007.
Lecture Notes in Computer Science 4733.

Declarative Optimization-Based Drama Management (DODM)

Drama managers watch a game (or other interactive experience) as it progresses, and intervene when necessary to keep the experience interesting and in line with an author's goals. DODM is a particular approach in which an author declaratively specifies what it would mean for the experience to go well, along with an abstraction of the story and a set of interventions the system can make. The system then optimizes its interventions according to the given criteria.

The basic approach was proposed by Joe Bates in 1992 and developed by Peter Weyhrauch in 1997. We've since made a number of modifications, both to the conceptual formulation and to the technical implementation.

Mark J. Nelson and Michael Mateas.
Another look at search-based drama management.
In Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI 2008), Chicago, USA, July 2008.
A shorter version appeared in the proceedings of AAMAS 2008.
Sooraj Bhat, David L. Roberts, Mark J. Nelson, Charles L. Isbell, Jr., and Michael Mateas.
A globally optimal algorithm for TTD-MDPs.
In Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007), Honolulu, Hawaii, USA, May 2007.
Mark J. Nelson, Calvin Ashmore, and Michael Mateas.
Authoring an interactive narrative with declarative optimization-based drama management.
In Proceedings of the 2nd Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2006), Marina del Rey, California, USA, June 2006.
Mark J. Nelson, Michael Mateas, David L. Roberts, and Charles L. Isbell, Jr.
Declarative optimization-based drama management in interactive fiction.
IEEE Computer Graphics & Applications, 26(3):30–39, 2006.
Mark J. Nelson, David L. Roberts, Charles L. Isbell, Jr., and Michael Mateas.
Reinforcement learning for declarative optimization-based drama management.
In Proceedings of the 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), Hakodate, Japan, May 2006.
David L. Roberts, Mark J. Nelson, Charles L. Isbell, Jr., Michael Mateas, and Michael L. Littman.
Targeting specific distributions of trajectories in MDPs.
In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 2006), Boston, USA, July 2006.
Mark J. Nelson and Michael Mateas.
Search-based drama management in the interactive fiction Anchorhead.
In Proceedings of the First Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2005), Marina del Rey, California, USA, June 2005.

MIDI Performance

I evaluated MIDI performance as part of previous work (2003–2004) with Belinda Thom at Harvey Mudd College. We were trying to discover interesting things about jazz improvisation, but got sidetracked into measuring the performance of our MIDI equipment, since we needed to be able to put error bars on the data. You may also want to take a look at the project's site, which has code, circuit diagrams, and everything else you need to run your own tests, as well as the detailed results of our tests.

Mark J. Nelson and Belinda Thom.
A survey of real-time MIDI performance.
In Proceedings of the 2004 International Conference on New Interfaces for Musical Expression (NIME 2004), pages 35–38, Hamamatsu, Japan, June 2004.
Belinda Thom and Mark J. Nelson.
An in-depth analysis of real-time MIDI performance.
In Proceedings of the 2004 International Computer Music Conference (ICMC 2004), pages 430–437, Miami, USA, November 2004.

This information was current as of: May 2008

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