What if the system could reproduce the learning patterns of the human player: starting inexperienced and, by being thought by actions, how to play better? After all, playing a game is reproducing simple patterns in an always more complex set of situations, something computers are made to do. What would it mean to make the system learn how to play better, as it's playing?
In this article, we'll explore a powerful, general technique to guiding "jump and run" style AI through a platforming course to reach an arbitrary destination. This method works with both static geometry and moving level elements, like elevators or wheels.
This post will cover the basics of Dijksta's shortest path algorithm and how it can apply to path finding for game development. It is my opinion that understanding this algorithm will aid in understanding more complex AI algorithms, such as A*. This post is aimed more towards developers starting out in game development or those curious about Dijkstra's algorithm, but this will be a some...
The current main approaches to the path finding issue are good for simple and classic games, but not for innovative ones, and often cause performance and human simulation issues. A new approach that starts from the findings of the evolution psychology could help making AI models for innovative games
Path Finding issues have the same models from several years: navigation meshes, navigation grid and waypoints navigation for building the graph, A*, Sample, Dijkstra as 'already classic' algorithm. The advent of a new awareness, whom states that the logical approach to simulate humans is not the best way to solve the PF issue, is bringing new freshness for the AI world. The topic of thi...
This is the last of three articles that treat a new approach for the Path Finding. It's a part of the studies and experience I made in the last years, mainly in the fields of the Artificial Intelligence for Games and Evolutionary Psychology.
The method is simple.
Red grid is block grid,Green grid can walk.
first,Pre process on map to get datas that we need:
1) The map is divided into many regions, each region consist of a number of grids, these regions must be convex polygon(int most cases,they are rectangulars), there are not block grids in these regions.
2) then generate the shortest path information between regions, such...
OverviewNavigation graph remains a useful alternative to navigation mesh and may offer certain advantages depending on a game's environment. This article discusses several approaches to navigation graph generation: from an automated one based on Delaunay triangulation to a completely automatic method derived from triangulation of navigable areas and dual graph of triangulation.The article u...
.Influence maps have been around since the very early days of game AI, tracing their history back to real-time strategy games over a decade ago. Since then, influence maps have become a cornerstone technique for game developers, and are even starting to become prevalent in first-person shooters as well (e.g. KILLZONE 2/3). In this tutorial, you'll learn about some of the motivation fo...
AbstractUsing a neural network for the brain, we want a vehicle to drive by itself avoiding obstacles. We accomplish this by choosing the appropriate inputs/outputs and by carefully training the neural net. We feed the network with distances of the closest obstacles around the vehicle to imitate what a human driver would see. The output is the acceleration and steering of the vehicle. We also n...