SAIG Overview  23rd March 2011
Project Areas    • Research      ‣ Execution Systems      ‣ Multi-agent Reasoning      ‣ Opponent Modelling    • Teaching ...
Research    • Research in Games at Strathclyde dates back a     number of years.    • More recently it’s shifted to be Dr ...
Execution Systems    • Principal research area for the group currently.    • Dovetails with the Strathclyde Planning Group...
REAPER    • Combines Automated Planning with pre-trained     Artificial Neural Networks.    • Uses the ANN for situations n...
Integrated Influence    • Attempts to state the world in terms usable by     planners and reactive systems.    • Intelligen...
Opponent Modelling    • Predicting an opponents actions in advance allows     us to adjust our plans accordingly.    • Pla...
StrathPoker    • Project to create an AI agent for Poker.    • Uses Monte Carlo rollout and UCT to estimate     value of a...
SPREE    • StrathPoker ran into two main issues:      ‣ Existing datasets are incomplete information.      ‣ Too much time...
SPREE Next Phase     • First generation of simple bots now complete.       ‣ Not particularly sophisticated, models a play...
Undergrad Teaching     • A good amount of our work in AI and passion for      games filters through to our teaching.     • ...
EvoTanks     • EvoTanks was the precursor to the REAPER      achitecture.     • Uses genetic algorithms to evolve controll...
Dots and Boxes     • Classic 2 player pencil and paper game.     • Excellent example of combinatorial explosion.       ‣ F...
General Games     • Beyond Dots and Boxes and other specific games,         General Games describes games in GDL.     •14
Competitions     • Many academic conferences have associated      competition tracks in a range of game formats.     • Oft...
Starcraft     • Access to Starcraft via the Brood Wars API allows      the creation of AI agents.     • Competition run in...
Ms Pac-Man     • Based on a screen-scraping framework       ‣ Screenshot analysis to ascertain gamestate       ‣ Passed to...
Ms Pac-Man vs Ghosts     • Alternative competition uses a reproduction of the      game in Java.     • Allows for the deve...
The Future     • More prospective students looking for projects.     • Always on the lookout for collaboration potential. ...
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SAIG Overview March 2011

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

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SAIG Overview March 2011

  1. 1. SAIG Overview 23rd March 2011
  2. 2. Project Areas • Research ‣ Execution Systems ‣ Multi-agent Reasoning ‣ Opponent Modelling • Teaching ‣ Genetic Algorithms ‣ Reactive Systems ‣ Game Theory / General Games2
  3. 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 Modelling3
  4. 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?4
  5. 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.5
  6. 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.6
  7. 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 systems7
  8. 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.8
  9. 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 development9
  10. 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.10
  11. 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.11
  12. 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.12
  13. 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 guidance13
  14. 14. General Games • Beyond Dots and Boxes and other specific games, General Games describes games in GDL. •14
  15. 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.15
  16. 16. Starcraft • Access to Starcraft via the Brood Wars API allows the creation of AI agents. • Competition run in parallel with AIIDE16
  17. 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.17
  18. 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.18
  19. 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.19

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