Fighty Dot

Fighty Dot is a Swift implementation of nine men's morris for iOS.

Screenshots

App store screenshots designed using Sketch.

Features

Player vs. AI
Player vs. Player (turn-based)
Analytics implemented with Firebase

Motivation

"You must begin by studying the endgame"

Nine men's morris is an interesting game. Get three pieces in a row (a "mill") and you can take an opponent's piece. Knock your opponent down to two pieces and you win. It seems simple, but rushing in and making mills right from the start can leave you boxed in while your opponent is free to move and get into position for a counter-attack.

In some ways, the game can be seen as a battle between strength and manoeuvrability.

The game dates back to Roman times, and is mentioned in Ovid.

Setup

Open FightyDot.xcworkspace in Xcode. The code should build cleanly, but you need to set up your own Firebase config :)

Firebase Setup (~5-10 minutes)

The Firebase pods are included in the project, so you don't need to download them.

You do need to get your own GoogleService-Info.plist file though. That way you can use your own API key, etc. There are instructions on how to get this file here.

Exploring the Code

Architecture

The code is built using a standard Swift delegation pattern.

The basic flow is as follows:

GameVC.swift handles users interactions and passes them off to Engine.swift
Engine.swift handles the game logic, and holds references to the board, players and game state
After Engine.swift has processed the game logic, it delegates view updates, animations and so on back to the view

The ModelViews encapsulate parts of the view that naturally belong together. For example, a PlayerView encapsulates all of the labels, counters, and icons for a player.

The board is described using an adjacency list, with the related view images connected in storyboard.

Animations.swift contains some custom Core Animation animations, and AudioPlayer.swift...well, I hope that's obvious :)

Both of these latter components are pretty generic and could be reused or extracted to a separate library.

AI

The AI player uses negamax with alpha-beta pruning to make moves. This is the same kind of algorithm that is used in chess or go AI to look ahead to all possible game states and pick the best possible move while restricting the best possible moves for the opponent (see more about the game theory here).

AI Challenges

There are three stages in nine men's morris: placing pieces, moving pieces, and flying pieces. Add to this the fact that players can choose to take from selection of opponent pieces after moving and things can get a little tricky. The problem with the minimax family of algorithms (which negamax is a part of) is that they tend to lead to combinatorial explosion, and all of these possibilies only exasperate matters.

Debugging and checking the correctness of minimax algorithms becomes difficult due to the size of the recursive call tree, and the fact you must factor in subjective heuristic weighting. I've left a simple class, TreeNode.swift, which you can insert into the algorithm and print a tree of all the possible states to check correctness, if you want.

Pruning

Pruning lets you make a decent lookahead while restraining combinatorial explosion: you can cut off ("prune") searches of possible game states by keeping track of the best possible values for the alternate players. By ordering the moves in terms of those that form mills, the pruning seems to work well, and I managed to get a decent lookahead in a reasonable time while running on the iPhone.

Difficulty

Overall, I would say the AI plays a good game. I've only managed to beat it once or twice even on normal. Setting the difficulty to easy will cut the lookahead distance to one -- probably the same as a beginner human player -- and should be relatively easy to beat if you've played a few games before.

Heuristic evalation

All minimax-type algorithms require a heuristic evalation function so that they can judge how good a game state is for a particular player. Nine men's morris is complex because the game takes place in three phases: placing, moving, and flying. A good move in one phase is not necessarily a good move in another. So the game uses different heuristics depending on the state the player is in.

Fighty Dot uses the following heuristics to evaluate a game state:

Whether a mill was closed
The number of mills
the number of blocked opponent pieces
The number of player pieces on the board
The number of two piece configurations (adding one more piece closes a mill)
The number of three piece configurations (2 two-piece configuration mills that intersect -- this is great during the placement phase because then your opponent can't stop you forming a mill)
The number of open mills (A two-piece configuration where the empty piece has a neighbour that is the same colour. When moving, that piece can then be moved into the empty spot, forming a mill.)
The number of double mills (a piece can be moved back and forth between two mills, completing one of them every turn)

These are based on the heuristics in this paper, though I tweaked the weights, which seemed to give a better game.

See HeuristicWeights.swift for the exact weights.

Design

I designed the app and icons using Sketch. I'm not much of a designer and this part probably took me longer than the code, if I'm honest. But I learnt a lot :) Trying to design something simple that looks good is hard.

Acknowledgements

Special thanks to stzzy.

Fonts

Lato fonts are licensed under the Open Font License

Icons

Help page gesture and tap icons by Maxim Kulikov, obtained from nounproject.com under the Creative Commons license, with shadows added by app developer.

If you like this app, you can check out another one I made here.

pekoto-zz / fightydot