diff --git a/_data/index.json b/_data/index.json index dbebfbe48c84ec4d1ec4e8ac45e101da422e0a5c..b343915eb3be101154a78e6f54e93de80fe08fc3 100644 --- a/_data/index.json +++ b/_data/index.json @@ -77,6 +77,8 @@ "name": "2", "topic": "AI Experiments and candidate games", "links": { + "metrics": "code/AppMetrics.java", + "intro":"slides_pdf/ce810-metrics.pres.pdf" } }, { diff --git a/slides/content/metrics.tex b/slides/content/metrics.tex index 3515d7cc891d3af1a5bce245185bdbeed8f3c6fe..0350ca7514f7859134e422a77c6801d590b5ba95 100644 --- a/slides/content/metrics.tex +++ b/slides/content/metrics.tex @@ -54,7 +54,7 @@ \begin{itemize}[<+->] \item We did these yesterday. \item We should load the AIFactory - \item Then use the factory in the evaluate + \item Then use the factory to build agents in the evaluate \item ai.buildAI("ProRuleRushRangedBlue", settings), \end{itemize} \end{frame} @@ -83,7 +83,7 @@ public Double evaluate(GameSettings settings) { \end{minted} \end{frame} -\begin{frame}[fragile]{Basic} +\begin{frame}[fragile]{Less Basic} \begin{minted}[breaklines,tabsize=2,fontsize=\footnotesize]{Java} public Double evaluate(GameSettings settings) { GameState start = map.buildState(settings); @@ -101,10 +101,6 @@ public Double evaluate(GameSettings settings) { winCounts[winner]++; } } - - double score = winCounts[1] - winCounts[0]; - fitnessScores.put(settings, score); - return score; } \end{minted} \end{frame} @@ -126,5 +122,51 @@ public Double evaluate(GameSettings settings) { \end{figure} \end{frame} +\begin{frame}{Results} + \begin{table} + \begin{tabular}{l|l|l} + defRanged&atkRanged&fitness\\\hline + 1 & 5 & 30\\ + 10 & 6 & 20\\ + 4 & 2 & 20\\ + 10 & 2 & 20 + \end{tabular} + + \pause So not a very good fitness function then... + \end{table} +\end{frame} + +\section{User Testing} + +\begin{frame}{A|B Testing} + \begin{itemize}[<+->] + \item Put two versions of the game in front of someone + \item Ask them what one they enjoyed most + \item Ideally should use a large sample size (or stats) + \item But this isn't meant to be rigorous + \end{itemize} +\end{frame} + +\begin{frame}{Qualitative Data} + \begin{itemize}[<+->] + \item In inform out conclusions we could ask other things + \begin{itemize} + \item Basic demographic data, + \item Experience with games, + \item Open ended questions, + \item Game Metrics... + \end{itemize} + \end{itemize} +\end{frame} + +\section{Advice} + +\begin{frame}{AI experiments} + \begin{itemize}[<+->] + \item Fast iterations - don't spend too long waiting for stuff + \item Drop turn limits, repeats, generation counts... + \item See what kind of data you're getting and adapt your fitness. + \end{itemize} +\end{frame} \end{document}