AI 101

The AI 101 series aims to bring you up to speed on how artificial intelligence works.  To do this, we base all of our examples within games.  While some technical literacy is expected, I strive to make this writing as accessible as possible. 

The Foundations Series

Our first series of AI 101 is focused on covering the absolute basics: what do we mean when we refer to AI systems?  What is considered to be an artificially intelligent piece of software?  We cover all of this and get into some foundation AI theory while relating it to games along the way!

The Introduction


This is where we set the scene for AI 101: discussing what the series is about and additional materials that students may find useful.

Part 1: Why Are You Here?


Where we discuss the elephant in this virtual room: you're only interested in reading this article because you expect to gain something from the experience.

Part 2: Actions Rewards & Video Games


So how did everything in part 1 about actions and rewards relate to games?  Here we look at how reward structures are embedded in video games to make you better at playing them.

Part 3: Intelligent Agents


Where we discuss the notion of an agent - a key AI concept. In addition, we discuss what characteristics are expected of software to be considered agents.

Part 4: Game AI - A Unique Challenge


We take a moment to highlight how AI applications in commercial video games, present  unique challenges when compared to other problem areas.

Part 5: Understanding Your Environment


Looking at how we model the environment around us: understanding key features of how a game plays and the impact this can have on an AI solver.

Part 6: Start Searching!


Exploring the fundamentals of search space terminology and the challenges of exploring these environments.

Part 7: Uninformed Search


An overview of uninformed search algorithms breadth-first and depth-first search.

Part 8: Cost-Driven Search


Expanding from our uninformed search, we start to consider how to make a greedy algorithm by factoring the costs of actions.

Part 9: Considering Heuristics


Having previously considered costs, we now consider another metric called a 'heuristic'; allowing us to measure roughly how far away we are from the goal.

Part 10: A* Search


We conclude this foundation series by bringing together everything from the earlier stages into the A* search algorithm.