mscroggs.co.uk
mscroggs.co.uk

subscribe

Blog

 2019-12-27 
In tonight's Royal Institution Christmas lecture, Hannah Fry and Matt Parker demonstrated how machine learning works using MENACE.
The copy of MENACE that appeared in the lecture was build and trained by me. During the training, I logged all the moved made by MENACE and the humans playing against them, and using this data I have created some visualisations of the machine's learning.
First up, here's a visualisation of the likelihood of MENACE choosing different moves as they play games. The thickness of each arrow represented the number of beads in the box corresponding to that move, so thicker arrows represent more likely moves.
The likelihood that MENACE will play each move.
There's an awful lot of arrows in this diagram, so it's clearer if we just visualise a few boxes. This animation shows how the number of beads in the first box changes over time.
The beads in the first box.
You can see that MENACE learnt that they should always play in the centre first, an ends up with a large number of green beads and almost none of the other colours. The following animations show the number of beads changing in some other boxes.
MENACE learns that the top left is a good move.
MENACE learns that the middle right is a good move.
MENACE is very likely to draw from this position so learns that almost all the possible moves are good moves.
The numbers in these change less often, as they are not used in every game: they are only used when the game reached the positions shown on the boxes.
We can visualise MENACE's learning progress by plotting how the number of beads in the first box changes over time.
The number of beads in MENACE's first box.
Alternatively, we could plot how the number of wins, loses and draws changes over time or view this as an animated bar chart.
The number of games MENACE wins, loses and draws.
The number of games MENACE has won, lost and drawn.
If you have any ideas for other interesting ways to present this data, let me know in the comments below.

Similar posts

Building MENACEs for other games
MENACE at Manchester Science Festival
MENACE
MENACE in fiction

Comments

Comments in green were written by me. Comments in blue were not written by me.
@(anonymous): Have you been refreshing the page? Every time you refresh it resets MENACE to before it has learnt anything.

It takes around 80 games for MENACE to learn against the perfect AI. So it could be you've not left it playing for long enough? (Try turning the speed up to watch MENACE get better.)
Matthew
                 Reply
I have played around menace a bit and frankly it doesnt seem to be learning i occasionally play with it and it draws but againt the perfect ai you dont see as many draws, the perfect ai wins alot more
(anonymous)
                 Reply
@Colin: You can set MENACE playing against MENACE2 (MENACE that plays second) on the interactive MENACE. MENACE2's starting numbers of beads and incentives may need some tweaking to give it a chance though; I've been meaning to look into this in more detail at some point...
Matthew
                 Reply
Idle pondering (and something you may have covered elsewhere): what's the evolution as MENACE plays against itself? (Assuming MENACE can play both sides.)
Colin
                 Reply
 Add a Comment 


I will only use your email address to reply to your comment (if a reply is needed).

Allowed HTML tags: <br> <a> <small> <b> <i> <s> <sup> <sub> <u> <spoiler> <ul> <ol> <li>
To prove you are not a spam bot, please type "y-axis" in the box below (case sensitive):

Archive

Show me a random blog post
 2020 

Mar 2020

Log-scaled axes

Feb 2020

PhD thesis, chapter ∞
PhD thesis, chapter 5
PhD thesis, chapter 4
PhD thesis, chapter 3
Inverting a matrix
PhD thesis, chapter 2

Jan 2020

PhD thesis, chapter 1
Gaussian elimination
Matrix multiplication
Christmas (2019) is over
 2019 
▼ show ▼
 2018 
▼ show ▼
 2017 
▼ show ▼
 2016 
▼ show ▼
 2015 
▼ show ▼
 2014 
▼ show ▼
 2013 
▼ show ▼
 2012 
▼ show ▼

Tags

computational complexity chalkdust magazine pythagoras manchester big internet math-off pac-man craft python numerical analysis sound folding tube maps braiding geometry estimation football javascript draughts graphs asteroids latex london gaussian elimination sport cross stitch london underground the aperiodical raspberry pi mathsjam national lottery data visualisation matt parker map projections approximation accuracy talking maths in public machine learning bubble bobble propositional calculus graph theory mathslogicbot programming plastic ratio dates ucl weather station dragon curves data news tmip game show probability martin gardner matrices european cup ternary frobel mathsteroids reddit phd hexapawn harriss spiral golden spiral platonic solids twitter hannah fry exponential growth convergence php pizza cutting a gamut of games statistics hats christmas card trigonometry cambridge preconditioning curvature speed logs stickers rhombicuboctahedron reuleaux polygons go world cup menace misleading statistics final fantasy nine men's morris noughts and crosses flexagons countdown royal institution electromagnetic field inline code golden ratio sorting rugby determinants chebyshev wool light royal baby puzzles manchester science festival realhats wave scattering matrix multiplication matrix of cofactors game of life error bars coins dataset gerry anderson tennis boundary element methods logic video games binary folding paper radio 4 simultaneous equations probability books signorini conditions arithmetic inverse matrices oeis people maths christmas advent calendar polynomials sobolev spaces captain scarlet bodmas chess finite element method weak imposition palindromes fractals interpolation matrix of minors bempp games triangles

Archive

Show me a random blog post
▼ show ▼
© Matthew Scroggs 2012–2020