Game Intelligence: The Future Simon M. Lucas Game Intelligence Group School of CS & EE University of Essex
Game Intelligence: The Future Simon M. Lucas Game Intelligence Group School of CS & EE University of Essex.
Dec 19, 2015
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- Slide 1
- Game Intelligence: The Future Simon M. Lucas Game Intelligence Group School of CS & EE University of Essex
- Slide 2
- Meet Adrianne from nVidia
- Slide 3
- Beautiful, but not very bright, yet.
- Slide 4
- Game Intelligence Group Main Activity: General purpose intelligence for game agents 2 Academic staff 1 post-doc 10 PhD students
- Slide 5
- Approaches Evolution Reinforcement Learning Monte Carlo Tree Search
- Slide 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
- Slide 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
- Slide 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
- Slide 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!
- Slide 10
- Sample MCTS Tree (fig from CadiaPlayer, Bjornsson and Finsson, IEEE T-CIAIG)
- Slide 11
- Learning Tree Policy and Roll-Out Policy Results for Othello (IEEE CIG 2011)
- Slide 12
- Research Leadership Grants Conference Series Journal Conference Special Sessions Competitions Software Toolkits (e.g. WOX, featured in MSDN Magazine)
- Slide 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
- Slide 14
- Slide 15
- IEEE Transactions on Computational Intelligence and AI in Games Published quarterly since March 2009 Journal has made an excellent start
- Slide 16
- 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, Slashdot etc By Philipp Rohlfshagen and David Robles
- Slide 17
- 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 well enjoy interacting with life-like AI characters
- Slide 18
- Sample Games
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