Ms Pac-Man Competition
|Entry / Author(s) (click link for authors)||Affiliation||High Score|
|Emilio - Pac-mAnt||Universidad Carlos III de Madrid||21,250|
ICE Pambush 4 (also see IEEE CIG 2010 Paper)
|Jave||University of Nottingham, UK||14,660|
|Bruce||City University of Hong Kong||10,820|
Shiraz University, Tehran
|CoboPac||University of Würzburg, Germany||5790|
Sejong University, Seoul
The full results are shown below (see note above about the Kim entry).
The winning entry is the one that achieved the highest score given ten runs each. The averages are also shown for interest, and this time the winning entry had only the third-highest average.
Some obvious weaknesses of all the entries:
they all have a fairly greedy short term strategy, and in many cases single food pills are left behind in the pursuit of more direct rewards, which makes life difficult towards the end of the level when these must be consumed in the absence of the relative security provided by the energiser pills.
they are not fully competent: they sometimes make unforced errors, and die by failing to make an obvious turn for example. In some cases this appears to be due to screen parsing and/or control software failing to send the correct key response in sufficient time
There are now some diverse and interesting ideas behind these entries, which need some refinement and to be coupled to an improved screen-capture and game control system. We look forward to improved entries for IEEE CIG 2011, and there is a good chance that we'll see some of these breaking the 50k barrier.
May be coming soon...
There are two planned competitions for 2011 (see links below for confirmation).
The screen-capture version is planned to be run at IEEE CIG 2011.
Ms Pac-Man versus Ghost Team Competition is planned to be run in conjunction with IEEE CEC 2011 (this will be confirmed by October 1st 2010)
When you have a technique that works well, you may want to write it up as a paper for the IEEE Transactions on Computational Intelligence and AI in Games.
We encourage entrants to try evolutionary and machine learning methods. The papers below might provide a useful starting point.
Also see the Pac Man papers in IEEE CIG 2008 and in IEEE CIG 2009 and 2010.
 Simon M. Lucas, Evolving a Neural Network Location Evaluator to Play Ms. Pac-Man, IEEE Symposium on Computational Intelligence and Games (2005), pages: 203 -- 210 [pdf]
 Szita and A. Lorincz, Learning to Play Using Low-Complexity Rule-Based Policies: Illustrations through Ms. Pac-Man, Journal of Artificial Intelligence Research (2007), Volume 30, pages 659-684 [pdf].
end of page