Greg Helton greghelton

TODO

I'm using the Calva extension in IntelliJ IDEA and sometimes I run the game from the REPL.

(play-until-winner initial-game-state)

Sometimes I run it from the bash command line.

java -cp "lib/spec.alpha-0.6.249.jar:lib/core.specs.alpha-0.5.81.jar:lib/clojure-1.12.0.jar:src/main/clojure" clojure.main -i src/main/clojure/backgammon/core.clj -e "(in-ns 'backgammon.core)" -e "(play-until-winner initial-game-state)"

Install emacs on a Mac
Commands Run:
brew install emacs
/opt/homebrew/opt/emacs/bin/emacs --fg-daemon
open new terminal tab
emacsclient -c

Using emacs
Note that on the Mac keyboard, Meta is ⌥ and Control is ^

Whenever I write some code to deal with data about people then functional programming seems to work best. Whenever I write some code to simulate people then object-oriented programming seems to work best.
— Michael Fogus

Object oriented programming makes code understandable by encapsulating moving parts. Functional programming makes code understandable by minimizing moving parts.
— Michael Feathers

• A function doesn’t have to be 100% pure to get many of the benefits of purity.
• Of course you don’t want to use the fact that it’s not 100% pure as an excuse for making it even less pure.
• A more functional programming style often results in more parameters to a function.
• A functional style does lead to more parameters but, it is much easier to test the functions in isolation.

	package org.vanillaweb;

	import java.io.*;
	import java.net.ServerSocket;
	import java.net.Socket;
	import java.net.URLDecoder;
	import java.nio.charset.StandardCharsets;
	import java.util.concurrent.*;

	public class FullDuplexRelay {

	import java.io.*;
	import java.net.ServerSocket;
	import java.net.Socket;
	import java.net.URLDecoder;
	import java.nio.charset.StandardCharsets;
	import java.util.concurrent.ArrayBlockingQueue;
	import java.util.concurrent.BlockingQueue;

	public class BrowserRelay {

	import com.ibm.as400.access.AS400
	import com.ibm.as400.access.DataQueue
	import com.ibm.as400.access.DataQueueEntry
	import com.ibm.as400.access.KeyedDataQueue
	import java.nio.charset.Charset

	fun main() {

	// Connect to IBM i
	val system = AS400(

	/// Selects the best legal move for the current player using a trained NeuralNetwork.
	public enum AIPlayer {

	/// Returns the move that maximises P(currentPlayer wins) according to the network.
	public static func bestMove(for state: GameState, network: NeuralNetwork) -> Move {
	let moves = uniqueByResult(MoveGenerator.legalMoves(for: state), from: state)
	let player = state.currentPlayer
	return moves.max { a, b in
	score(move: a, state: state, network: network, player: player)
	< score(move: b, state: state, network: network, player: player)

	(ns backgammon.ai
	"TD-Gammon inspired reinforcement learning AI for backgammon.

	Architecture:
	- Neural network: input(198) → hidden(40) → output(1)
	- Input: Tesauro's 198-feature encoding of board state
	- Output: estimated probability that White wins
	- Training: TD(λ) temporal difference learning
	- Eligibility traces for credit assignment across moves

	(ns my-project.core
	(:require [clojure.spec.alpha :as s]))

	;; Define a simple spec for a 'User' map
	(s/def ::name string?)
	(s/def ::age int?)
	(s/def ::user (s/keys :req [::name ::age]))

	(defn -main [& args]
	(let [valid-data {::name "Greg" ::age 30}