An overview of work being undertaken currently or recently by the Strathclyde AI and Games Research Group

1. SAIG Overview
23rd March 2011

2. Project Areas
• Research
‣ Execution Systems
‣ Multi-agent Reasoning
‣ Opponent Modelling
• Teaching
‣ Genetic Algorithms
‣ Reactive Systems
‣ Game Theory / General Games
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3. Research
• Research in Games at Strathclyde dates back a
number of years.
• More recently it’s shifted to be Dr Levine’s primary
focus.
• Two principle focuses of research :
‣ Execution
‣ Opponent Modelling
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4. Execution Systems
• Principal research area for the group currently.
• Dovetails with the Strathclyde Planning Group’s
work:
‣ What happens once a plan has been generated?
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5. REAPER
• Combines Automated Planning with pre-trained
Artificial Neural Networks.
• Uses the ANN for situations not foreseen in the
plan.
• Relies on a subsumption architecture to select
between ANN for e.g. Fight or Flight response to
enemies.
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6. Integrated Influence
• Attempts to state the world in terms usable by
planners and reactive systems.
• Intelligent plan repair, influenced by aspects of the
environment.
• Lifts abstract representations of the world out to
manage horizon problem dynamically.
• Inherently parallel techniques.
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7. Opponent Modelling
• Predicting an opponents actions in advance allows
us to adjust our plans accordingly.
• Planning has no mechanism for representing third
parties, but games invariably involve them.
• As such we try to infer models of opponents that
we can use inform out execution systems
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8. StrathPoker
• Project to create an AI agent for Poker.
• Uses Monte Carlo rollout and UCT to estimate
value of actions at this decision point based on
sampled search space.
• Idea is to use dataset of previous games to classify
players.
‣ More accurate rollout is action prediction is accurate.
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9. SPREE
• StrathPoker ran into two main issues:
‣ Existing datasets are incomplete information.
‣ Too much time and energy went into coding up Poker
system for the agent to play in.
• Strathclyde Poker Research Environment was
developed to solve these issues.
‣ GUI client for actual play, complete information gathering
‣ Standalone networked server for rapid AI development
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10. SPREE Next Phase
• First generation of simple bots now complete.
‣ Not particularly sophisticated, models a player as a tuple
and plays accordingly
• Future work to start exploring specific techniques
in the context of Poker.
• Also scope for various “Player Experience” style
experiments.
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11. Undergrad Teaching
• A good amount of our work in AI and passion for
games filters through to our teaching.
• Typically, we will teach AI in the context of games to
2nd year and 3rd year students.
• Work with undergraduates for Game AI final year
projects and Summer internships.
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12. EvoTanks
• EvoTanks was the precursor to the REAPER
achitecture.
• Uses genetic algorithms to evolve controllers for
tank agents.
• Packaged now as an evolution toolkit, allowing
students to explore evolved controllers and
scripting their own.
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13. Dots and Boxes
• Classic 2 player pencil and paper game.
• Excellent example of combinatorial explosion.
‣ For large grids (>5x5), intractable for minimax in a
reasonable time.
• Developed a version with a graphical front-end to
allow students to explore game-tree search and
heuristic guidance
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14. General Games
• Beyond Dots and Boxes and other specific games,
General Games describes games in GDL.
•
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15. Competitions
• Many academic conferences have associated
competition tracks in a range of game formats.
• Often, different approaches to these lead to new
applications of techniques to games.
• Sometimes lead to a final “solution”
‣ E.g. Baumgarten’s A* implementation for Mario-style
games.
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16. Starcraft
• Access to Starcraft via the Brood Wars API allows
the creation of AI agents.
• Competition run in parallel with AIIDE
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17. Ms Pac-Man
• Based on a screen-scraping framework
‣ Screenshot analysis to ascertain gamestate
‣ Passed to AI logic which simulates a key press
‣ Fed back into external game
• Ugly approach, but “honest” in as much as the game
is compartmentalised.
• Strong bias towards speed of response rather than
quality.
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18. Ms Pac-Man vs Ghosts
• Alternative competition uses a reproduction of the
game in Java.
• Allows for the development of either a Pac-Man AI
or a Ghost Team AI.
• Game pauses while AI code executes, allows for
more deliberative techniques, but is a less true
representation.
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19. The Future
• More prospective students looking for projects.
• Always on the lookout for collaboration potential.
• Importantly, we want future work to emphasise
solving current problems in industry.
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