The Gaming Mind
Research and Engineering on AI, Psychology and Gaming
Think gaming is bad for kids? Read
this.
This site was created by, and is maintained by
Selmer Bringsjord, Director of
RPI's
Minds & Machines Program, and project leader of The Gaming Mind.
Copyright 1997, Selmer Bringsjord.
Research and Engineering Areas
-
Omega Worlds and RAW. This project is devoted to building
the world's most robust on-line game, and the R&D engine, Rensselaer's
Artificial Worlds (RAW), that will produce Omega Worlds and other games
and gaming technolgies.
- Arcade Gaming Studied Through our Sim3. Our simulator, Sim3,
(so-named because it allows for air/space, marine, and land environments)
is being configured to operate as a reconfigurable arcade game "cockpit"
through which games can be created and evaluated (with
neuropsychological data gathered
courtesy of our Waverider).
- Learning by Reverse Engineering. Get a game; take it apart; parse
its pieces into structures built from known AI techniques and technologies. This
is a great way to learn about gaming and AI, and a great way, specifically,
to get one's hands dirty. We have started w/ Quake, The Logical Journey of
the Zoombinis, and The Dig. More and more source code is available from actual
games (e.g., the full source code for DOOM is forthcoming from id Software) --
which makes reverse engineering, and new enhancements, a lot easier.
-
MindCracker, for gamers who fancy themselves really
good...
- Exploiting Knowledge of Motivation in Psychology. A lot is known
in contemporary psychology about what motivates people, and what kills
motivation. A successful game is one that motivates human players to continue.
This research area is devoted to building games that motivate human players.
- Addiction. A lot is known
in contemporary psychology about addiction. This research area revolves
around the attempt to bring this knowledge to bear on the diagnosis of extant
games and the design of new ones. What is addiction? What is the difference
between addiction to substances and addiction to activities? Can an
information-processing model of addiction ever be fully accurate? Could a
machine ever be addicted?
- Rule-Based Approaches. How can rule-based AI systems be exploited
in gaming environments? Can simulation and planning be anchored to these
techniques? How can games profitably deploy expert system technology?
- Games that Learn. The use of neural networks, decision-tree
learning, and inductive logic programming in order to give game systems the
ability to learn as the action moves forward.
- User Modeling. Smart games should be able to create a
model of the user(s). What are their tendencies? What sort of personalities do
they have? How old are they? And so on. Such models can be created as the
system learns from interacting with the user in a game, and also via more
direct methods, e.g., a form the user fills out.
- Story Generation. Good games almost invariably involve narrative.
But what makes a good story, and can the answer to this question be put in
computational terms? How can games give human players freedom, and yet ensure
that an interesting story is unfolding as the action proceeds?
- Image Generation and Elicitation. Is it possible for an intelligent
gaming system to create new images as the game unfolds in a genuine creative
way? Can the system use the preferences of human players to craft such images?
This cutting-edge area is devoted building games that are "visually creative
on-the-fly."
Jobs
Publications
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Visions are trademarks of Vicarious Visions, Inc.