As Monte Carlo Tree Search (MCTS) is becoming more popular in the world of Game AI, AI programmers are starting to discover when, and more importantly when not to use it. A very basic outline of MCTS is that at each iteration it tries to expand on the best action known thus far, simulating till
So, debugging’s always fun. It’s the part of programming where we either have to acknowledge we’re fallible humans after all, or we can rage on some unknown piece of code written by someone else which “just doesn’t do what it should, gosh-darnit”. There are several ways you can make debugging a more friendly experience, and
One of the problems presented with Xenonauts is that the A.I. needs to construct squads of units ad-hoc. Besides the obvious examples where squads may split up or merge up to perform a task, we have the case where we do not want to define squads in the level definition. The A.I. should just be
After the somewhat disappointing impressions left by Admob, we turned to a different promotion service called AppCircle by the company Flurry. AppCircle differs from traditional advertising where you pay per view/click, by allowing the developer to pay per install. The AppCircle service (from a user perspective) is embedded in apps as an advertising service and
A traditional approach in A.I. is to search for an optimal strategy for the agent to follow. This is done by using traditional search techniques to search through the set of all possible actions (and consequences) an agent can make. Alpha-Beta Search and Monte-Carlo Tree Search are examples of these search techniques. However, Xenonauts is
Foreword Welcome to the first in a series of posts on the A.I. implementation of Xenonauts, a game by GoldHawk Interactive! In this initial post I will outline the game itself, the requirements of the A.I. and the initial setup of my approach. For those of you not familiar with Xenonauts, I recommend a quick