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MM-NEAT Applied to Partial Observability Ms. Pac-Man

This page presents research in a partially observable variant of Ms. Pac-Man done by undergraduate student Will Price as part of Southwestern University's Summer research program SCOPE. Our entry into the Ms. Pac-Man Vs. Ghost Team Competition at the 2018 Computational Intelligence and Games conference won first place in the Ms. Pac-Man track of the competition under the name Squillyprice01. This research extends Dr. Schrum's dissertation research in the standard fully observable version of Ms. Pac-Man and uses the MM-NEAT software package (an extension of NEAT). Although the code for evolving these agents is in the MM-NEAT repository, the source code for the competition entry is at this link. The key enhancement needed in the partial observability domain are models of the pills and ghosts that allow for predictions about unseen state which are fed into the sensors of the neural network controllers. The videos on this page can be viewed in a playlist here.

Three Module Multitask Beats All Levels

The left side shows the maze as experienced by Ms. Pac-Man, who cannot see ghosts in the gray areas (partial observability). However, it maintains a model of where it believes ghosts could be, which is indicated by red and blue squares. The agent is actually controlled by an evolved neural network that has three output modules (that create colored movement paths): one for when it senses no ghosts (green), one for when it senses any threats (red), and one for when it senses only edible ghosts (blue). This evaluation cuts off once every level is beaten once. The final score of this agent is 16,120 points.

One Module Network Reaches Second Level

This network uses only one output module and only reaches the second level (final score 7,750). This is a standard neural network that behaves relatively poorly. Some one-module networks actually perform reasonably well, though generally not as well as three module multitask.

Two Module Network Reaches Fourth Level

This network performs much better, and reaches the fourth level with a score of 13,640. However, although the network has two modules that it can switch between using preference neurons, it has evolved to only use one module all of the time (this is why there is always a green trail behind Ms. Pac-Man). Strangely, preference neuron networks focus on only one module in the partially observable domain, in part because the sensors that are used (split sensors) make it easy to treat threat and edible ghosts differently without using different modules.

Three Module Network Reaches Second Level

This network has three preference modules, but like the two module network above only uses one module. However, unlike the network above, it performs very poorly, only reaching the second level with a score of 1,161. Some three module networks perform much better than this, though all generally only use one module.

Module Mutation Duplicate Network Reaches Fourth Level

This network is another example of a preference neuron network doing well by only using one module. This network reaches the fourth level with a score of 15,670. Module Mutation Duplicate is able to create and use new modules based on previous modules, but evolution simply focuses on one module instead.

Associated Publications


Peer-Reviewed Journal Articles

Peer-Reviewed Conference Publications

Associated Movies

Miscellaneous Content

  • Spring 2019: Evolving an Intelligent Ms. Pac-Man Agent Under Partially Observable Conditions, presentation by Will Price at the Southwestern University Undergraduate Research & Creative Works Symposium
  • Fall 2018: Evolutionary Computation Applied to Digital Entertainment and the Arts, poster presented at the President's Appreciation Celebration for Southwestern University donors.
  • Fall 2018: SCOPE student Will Price (Squillyprice01) won first place in the Ms. Pac-Man track of the Ms. Pac-Man Vs. Ghost Team Competition for our MsPacManEntry.
  • Summer 2018: Neuroevolution in Video Games: "Mad Science Monday" presentation made by my SCOPE Summer research students to present to other SCOPE students
  • Fall 2014: An article on my dissertation has appeared in SIGEVOlution Volume 7, Issue 2-3, the newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation.

  • Last Updated: 5/28/2019