1 / 18

Game Intelligence: The Future

Game Intelligence: The Future. Simon M. Lucas Game Intelligence Group School of CS & EE University of Essex. Meet Adrianne from nVidia. Beautiful, but not very bright, … … yet. Game Intelligence Group. Main Activity: General purpose intelligence for game agents 2 Academic staff

chione
Download Presentation

Game Intelligence: The Future

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Game Intelligence: The Future Simon M. Lucas Game Intelligence Group School of CS & EE University of Essex

  2. Meet Adrianne from nVidia

  3. Beautiful, but not very bright, …… yet.

  4. Game Intelligence Group • Main Activity: • General purpose intelligence for game agents • 2 Academic staff • 1 post-doc • 10 PhD students

  5. Approaches • Evolution • Reinforcement Learning • Monte Carlo Tree Search

  6. Conventional Game Tree Search • Minimax with alpha-beta pruning, transposition tables • Works well when: • A good heuristic value function is known • The branching factor is modest • E.g. Chess: Deep Blue, Rybka • Tree grows exponentially with search depth

  7. Go • Much tougher for computers • High branching factor • No good heuristic value function “Although progress has been steady, it will take many decades of research and development before world-championship–calibre go programs exist”. Jonathan Schaeffer, 2001

  8. MCTS Operation(fig from CadiaPlayer, Bjornsson and Finsson, IEEE T-CIAIG) • Each iteration starts at the root • Follows tree policy to reach a leaf node • Then perform a random roll-out from there • Node ‘N’ is then added to tree • Value of ‘T’ back-propagated up tree

  9. Upper Confidence Bounds on Trees (UCT) Node Selection Policy • From Kocsis and Szepesvari (2006) • Aim: optimal balance between exploration and exploitation • Converges to optimal policy given infinite number of roll-outs • Often not used in practice!

  10. Sample MCTS Tree (fig from CadiaPlayer, Bjornsson and Finsson, IEEE T-CIAIG)

  11. Learning Tree Policy and Roll-Out Policy • Results for Othello (IEEE CIG 2011)

  12. Research Leadership • Grants • Conference Series • Journal • Conference Special Sessions • Competitions • Software Toolkits (e.g. WOX, featured in MSDN Magazine)

  13. Research Grants • AI Games Network (EPSRC, 2007 – 2010; with Imperial and Bradford) • UCT for Games and Beyond (EPSRC, 2010 – 2014, joint with Imperial and Bradford; 1.5M total, 489k Essex) • Plus IEEE T-CIAIG Editorial Assistant + Travel

  14. IEEE Transactions on Computational Intelligence and AI in Games • Published quarterly since March 2009 • Journal has made an excellent start

  15. Competitions • Many, many competitions • Most recent: Ms Pac-Man versus Ghost Team: IEEE CEC 2011 • An interesting and fun AI challenge • Covered by New Scientist, Slashdotetc • By Philipp Rohlfshagen and David Robles

  16. Summary • AI + Games • Fantastic field to work in • The BEST test-bed for general intelligence • Monte-Carlo Tree Search + Reinforcement Learning: very promising! • Reasonable standard general game playing is already a reality for many games • Within the next 10 years we’ll enjoy interacting with life-like AI characters

  17. Sample Games

More Related