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.
                  ×1      
(Click on one of these icons to react to this blog post)

You might also enjoy...

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> <logo>
To prove you are not a spam bot, please type "naidem" backwards in the box below (case sensitive):

Archive

Show me a random blog post
 2024 

Feb 2024

Zines, pt. 2

Jan 2024

Christmas (2023) is over
 2023 
▼ show ▼
 2022 
▼ show ▼
 2021 
▼ show ▼
 2020 
▼ show ▼
 2019 
▼ show ▼
 2018 
▼ show ▼
 2017 
▼ show ▼
 2016 
▼ show ▼
 2015 
▼ show ▼
 2014 
▼ show ▼
 2013 
▼ show ▼
 2012 
▼ show ▼

Tags

accuracy gaussian elimination rugby matrices captain scarlet advent calendar cross stitch national lottery london underground hats arithmetic stirling numbers ucl estimation go chess guest posts php graph theory braiding map projections cambridge pascal's triangle data folding paper sport pizza cutting gather town big internet math-off mean speed game show probability hyperbolic surfaces inverse matrices books christmas javascript asteroids programming london youtube pac-man electromagnetic field fonts football draughts geometry dinosaurs royal institution 24 hour maths machine learning flexagons pi exponential growth manchester matrix multiplication tmip folding tube maps computational complexity convergence news christmas card graphs harriss spiral signorini conditions simultaneous equations menace matrix of cofactors european cup video games puzzles games a gamut of games runge's phenomenon squares propositional calculus statistics mathslogicbot nine men's morris binary crochet sorting dragon curves preconditioning trigonometry mathsteroids stickers zines platonic solids final fantasy error bars craft talking maths in public boundary element methods dataset determinants phd radio 4 quadrilaterals hannah fry datasaurus dozen curvature recursion reddit sound frobel logo inline code finite group misleading statistics wave scattering palindromes reuleaux polygons edinburgh countdown royal baby martin gardner wool latex the aperiodical data visualisation finite element method oeis fractals turtles logic crossnumber weather station triangles polynomials weak imposition pi approximation day dates pythagoras newcastle realhats golden ratio databet numbers probability coins approximation bubble bobble chebyshev python mathsjam light noughts and crosses plastic ratio world cup numerical analysis matrix of minors sobolev spaces errors geogebra hexapawn bempp golden spiral logs manchester science festival ternary raspberry pi interpolation standard deviation bodmas anscombe's quartet rhombicuboctahedron tennis people maths matt parker game of life correlation fence posts live stream gerry anderson chalkdust magazine

Archive

Show me a random blog post
▼ show ▼
© Matthew Scroggs 2012–2024