Overview of SAIG Student Projects


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Presentation at the "Game Academics" Christmas Networking event at Glasgow Caledonian University. This presentation gives an overview of the work I'm supervising (and my own research which I sometimes get to) for the Strathclyde AI in Games Research Group

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  • Overview of SAIG Student Projects

    1. 1. Strathclyde AI in Games Some Sample Projects Luke Dicken
    2. 2. AI in Games • Games present an important challenge to Artificially Intelligent systems that we can’t yet overcome • We find reasoning about what another player will do difficult. Recognising their objectives and strategies is still non-trivial • Automatically determining good strategies for our player is also difficult 2
    3. 3. Student Projects • StrathPac ‣ A project investigating use of AI techniques to play the game Ms. Pac-Man • StrathPoker ‣ A Machine Learning approach to classification of Poker players • StrathPlayer ‣ An attempt to develop a player for General Games 3
    4. 4. StrathPac • Based on competition framework developed by Simon Lucas (U. Essex) for IEEE Symposium on Computational Intelligence in Games • Using screen scraping techniques, create an agent capable of playing Ms Pac-Man well • Current human high score stands at 920,000. AI highest is ~30,000 4
    5. 5. StrathPac • This was offered as coursework for Strathclyde’s Third Year AI class • Most promising approach was developed as a summer internship and entered in the 2009 CIG competition 5
    6. 6. StrathPoker • Understanding how an opponent plays Poker gives us insight into strategies to employ against them (or that they might employ against us) • Typical classification of players is based on limited stats analysis, into around 12 classes ‣ Categorised as : {VPiP, PFR, WaSD} • Can we do better using Machine Learning? 6
    7. 7. StrathPoker • Use PCA on a complete statistical overview (~30 dims) and cluster data • Feed this more insightful classification into an agent to allow for more beneficial play 7
    8. 8. StrathPlayer • General Games are an emerging area in AI. • Previously, players have used specific knowledge about a game to maximise their ability to play these games. • General Games are about moving towards game- independent methods of play, creating agents capable of playing any game. 8
    9. 9. StrathPlayer • Initial work has focused on two areas ‣ Firstly building a set of support tools such as “Games Master” server to allow games to be played between agents ‣ Secondly, developing a set of reference players utilising standard algorithms. • Aim is to develop a method for determining which algorithm best suits a game 9
    10. 10. My Research • Initial work with Ms Pac-Man highlighted how deficient current AI techniques are for tacking an entire class of problems. • Fast paced, dynamic environment that require long- term planning to achieve objectives. • We can currently make good decisions slowly, or bad decisions fast. 10
    11. 11. Integrated Influence • Aim is to create an architecture describing an agent that is driven by both short-term and long-term reasoning combined. ‣ Draws heavily on techniques from Automated Planning and Reactive Systems. • Video games provide a good testbed of the kind of environments we are trying to cope with 11
    12. 12. And Finally • I am also a Staff Writer for AIGameDev.com • Great resource for Game developers ‣ Forums ‣ Regular articles describing theory and applications of AI ‣ Masterclass sessions ‣ Interviews with industry figures 12
    13. 13. Contact • Strathclyde AI in Games Research Group : ‣ John Levine ( johnl@cis.strath.ac.uk ) • Projects presented during this talk : ‣ Luke Dicken ( luke@cis.strath.ac.uk ) 13