Machine Learning

The call for papers for the AIIDE ‘08 conference has just expired. This year I’m on the Program Committee, so today I got my hands on four papers to review based on my selected topics. There are some pretty great research projects this year…
However, I find the opening paragraphs that describe the motivation […]

This week’s Thursday Theory article looks at academic research in Neuroevolution of Augmenting Topologies (a.k.a. NEAT) developed at the University of Texas. In particular, you’ll learn how it’s applied at runtime in a video game to allow the actors to learn neat behaviors over time by evolution. (You can curse at the pun if […]

After a quiet week last week, there’s been an avalanche of news and great content in the world of game AI. In this roundup of smart links on AiGameDev.com, you’ll find book reviews and tips about machine learning, white papers about pathfinding and insights into a few games.
This post is brought to you by […]

While the spotlight for AI in games is often on first-person shooters or real-time strategy games, sports games also present some rather unique challenges and solutions. There certainly isn’t as much research or industry experience available to learn from, but enough to get a good overview of what happens behind closed doors at Electronic […]

Machine learning conceptually has many benefits for games, notably for reducing development times and creating AI that can adapt to the player. However, it is difficult to apply in the real-world! Transfer learning can help by improving the speed and quality of the learning. The idea is to use knowledge from previous experiences to […]

A new book recently came out explaining how to apply biologically inspired AI to games. It covers the classics in computational intelligence such as genetic algorithms, neural networks, artificial immune systems and particle swarm optimizations — not forgetting underrated techniques like reinforcement learning, independent component analysis, and radial basis functions.
I found out about […]

For the developers among you looking to take your knowledge of AI and machine learning to the next level of expertise, a new paperback edition of the hardback classic by Brian D. Ripley is scheduled for released in a few days (on the 10th of January to be precise). With an extra 22% […]

This is the third and final part of AiGameDev.com’s coverage of the research sessions relating to artificial intelligence at the GDC in Lyon. Be sure to read part 1 (about concurrent behaviors and Bayesian learning) and part 2 (about Embodied Communicational Agents).
This article in particular covers research into machine learning, and how it can […]

I’ll be in France on the 3rd and 4th of December at the Lyon GDC, giving a talk on using behavior trees for AI logic. There’s a surprisingly large number of game AI sessions generally, so it should be especially interesting for regular AiGameDev.com readers. Coverage will follow over the next couple of […]

If you’re interested in the idea of profiling and modeling player behaviors for better gameplay, then you may want to help out with Ben Cowley’s current Ph.D. research. He’s looking for volunteers to download and play a few games of Pacman. The data will be used to find correlations between playing styles and […]

This week’s Thursday Theory post on AiGameDev.com looks into applying reinforcement learning to bridge the gap between animation control and high-level AI logic. Specifically, this review covers autonomous characters that learn to move in a dynamic world, as developed by Leslie Ikemoto from the University of Berkeley.

The applicability of computational intelligence and neural networks (NN) in games is a touchy subject here at AiGameDev.com, and in game developer forums generally. This is particularly interesting because everyone seems to be approaching the issue from a different direction.

Like every Saturday, it’s time for a fresh batch of Smart Links from around the web. There are some great one this week. And, as always, if you have news or announcements, be sure to send them in.
Solving Games by Graph Search
Mark C. Chu-Carroll over at Good Math, Bad Math shows how […]

Adaptation is a big challenge for game AI, since it either requires a lot of work to implement the different options manually, or it involves using technology that’s hard to predict and control the outcome of the learning. The games industry has been struggling with this for a while now, with varying degrees of success. But one solution shows more promise than any other…

Siggraph 2007 is around the corner, and that means new technology for game developers to play with! In terms of game AI, the most interesting part of the conference is character animation, as it promises to deliver realistic motion for lower investments.
One paper from University of Washington Animation Research Labs presents an active learning […]

Games require behaviors that adapt to the player — otherwise they’d just be simulations. Typically, at the mention of the word “adaptation,” most developers think of machine learning (ML). However, even the more advanced AI designs can be implemented without runtime learning.