This tutorial will cover how to write AI to coordinate a search procedure among multiple characters. Looking at different implementations based on a grid or a graph, you'll see the benefits of these representations for searching building-style levels efficiently. You'll also see different possible algorithms to use, as well as more advanced techniques you'll need to make it convincing.
This interview with Dave Churchill covers his winning entry in the Starcraft Competition 2013, known as UAlberta Bot. In particular, you'll hear about a real-time solver that can determine near-optimal build orders given specific goals. You'll also dizcover about different ways to do large scale unit battle management of units (from UCT to greedy search), and the efficient simulator called SparCraft required to ...
Monte-Carlo Tree Search is a promising technique that is revolutionizing board game AI. In this interview, find out how MCTS can be applied to The Octagon Theory, a mobile game which combines challenges similar to Othello or Go. What's necessary to build a competitive AI with probabilistic search techniques? How do you take into account opponent models into the process?
How does a small team of two programmers embrace change and build an AI that's fun to play against? In this interview with Kenneth Harder from BetaDwarf, you'll find out how the AI was iteratively made to be more challenging yet easy to understand by the player. You'll also learn about the techniques used to implement third-person tactical action AI with tight constraints in Unity.
In this masterclass with Alexander Shafranov, winner of the Capture The Flag competition with an HTN planner, you'll learn about plan compilation to C++ using a JSHOP2-inspired architecture. Find out how to reduce time spent planning for team and individual coordination using domain specific compiled code rather than an interpreted solution.