PUBLIC MARKS with tag gameai

I’ve been steadily plugging away at my list of bugs and enhancements for the upcoming release and finally tackled one area that I had been avoiding: improving the AI of some of the bugs.

And the man behind the curtain: 1000s of kids in China sitting at “dungeon master” consoles controlling the game environment, AI states, and other game variables.

The creators of the PathEngine SDK,a toolkit for implementing intelligent agent movement, based on a 'points of visibility' pathfinding solution implemented over arbitrary 3D ground meshes, have announced the release of V5.01. Key features are pathfinding through overlapping geometry, dynamic obstacle management, and content processing functionality.

APHID is an acronym for Asycnhronous Parallel Hierarchical Iterative Deepening. The APHID game-tree search algorithm attempts to accomplish many goals: * to test out the viability of asynchronous search methods for parallel alpha-beta based search. * to develop an algorithm that works well across all hardware platforms and applications. * to implement a tool that can be easily inserted into legacy sequential search algorithms.

In the comments thread on Greg's welcome post back last September, we had a bit of a chat about achieving real-world crowd densities on next-gen platforms. If you haven't yet seen Capcom's Dead Rising, it's worth checking out the new official trailer released last Friday - I count possibly 100+ NPCs on screen in dense urban environments, with no apparent interpenetrations and quite a bit of animation going on.

However though AI Planning has much to contribute to both these fields, particularly in producing more convincing Non-Player Characters and autonomous intelligent characters, few AI planning researchers have been involved in this work, and the technology, where applied at all, has often been used in a somewhat ad hoc way. In addition, games company use of AI planning has so far been limited - A*-based motion planning the main exception - with practitioners feeling that the technology is too computationally expensive or risky for integration into computer games.

Bulletsmorph is genetic programing(GP) based the barrage of bullets creating engine (To learn GP, please see genetic-prgramming.org). The bulletsmorph crosses between the existing patterns of the barrage of bullets and creates the new pattern.

Much of the work in game AI has focused on the ‘big' problems: path planning, squad planning, goal-directed behavior, etc. The result is characters that are capable of increasingly intelligent behavior. However, acting intelligently and acting aware and sentient is not the same thing. But if we are to create the kind of compelling and emotional characters upon which the next generation of computer games will be based, we must solve the latter problem, namely how to build characters that seem aware and sentient.

A shooting game with vertical scroll. In this game, there are only you and the boss ship. The more you beat a boss, the more next boss becomes strong. You have 180 seconds in first time, and it decreases. Time will increase, if a boss is beaten, and if you die, it will more decrease. Beat more bosses, before time is lost.

The IGDA’s AI Interface Standards Committee is recruiting new members, from both industry and academia. See the call for applications below, open until February 20:

ACM is hosting a two-day video game symposium on 29 July and 30 July in 2006, co-located with SIGGRAPH 06 in Boston, MA, USA. The symposium will consist of keynotes, panels and papers. In addition, a "Hot Games" session will preview unreleased titles from major game companies and indie developers.

1. Introdução a jogos de computador. 2. Interação em jogos de computador: percepção, ação e reação. 3. Sistemas multiagentes. 4. Heurísticas e meta-heurísticas. 5. Aprendizado computacional. 6. Representação e compartilhamento de conhecimento. 7. Sistemas para desenvolvimento de jogos.

Our aesthetic agency for beauty and emotion is one of the most celebrated bastions of humanity. If machines could understand and affect our perceptions of beauty and happiness, they could touch people's lives in fantastic new ways. Drawing variously from work in diverse fields such as psychology, cognitive science, and philosophy, recent applications of artificial intelligence have begun their foray into the computation of, inter alia, art, music, poetry, and affect. Both the theory and praxis of aesthetics by computational means are seeing rapid advances, and the time is ripe for thematic integration. Hence, this workshop will bring together AI theorists and practitioners across various realms in study and celebration of its central thematic, COMPUTATIONAL AESTHETICS.

Gamespot's Best AI of 2005

Black and White 2 AI I played Black and White 2 for many hours yesterday. The computer player and I were in a stalemate. The computer kept sending armies against me and I kept defeating them. I had built my town with walls around it, and then put archers on top of the walls. I was building up my strength while defending myself, in preparation for a big attack. I felt pretty safe. After around 40 attacks, I realized that they weren't all the same. The computer wasn't using the same attackers each time. It tried the creature, archers, swordsmen, and catapults. It tried combinations of them. Sometimes it would come through my main entrance, and sometimes it would come around the back entrance to the city. The computer player also destroyed major sections of the city using the “earthquake” power, but I recovered from these too. After a while the enemy creature figured out that he should kick my wall in. His archers and swordsmen stayed back, out of range, while the creature came up and destroyed my wall, including the archers on it. After it breached the wall, the army swarmed into my town and killed half my people. I rebuilt my wall and started to recover, but the computer's newly discovered strategy worked well. It tried several variants but kept going back to the same approach: kick down the wall, then swarm the town. This forced me to try some new strategies. Although being on the wall has advantages, it leaves the archers vulnerable when the enemy creature attacks the wall. So I moved them behind the wall. I've also learned to open my gate, wait for the enemy army to get close, then close the gate and set their army on fire. I have no good strategy for the creature knocking down my wall though, and I'm constantly losing townspeople and then rebuilding. After a long stalemate, the computer AI learned how to attack more effectively, and now I'm having trouble keeping my city safe. I'm very impressed by the AI. I'm not sure how it's programmed, but it tried out many different things and learned which ones work the best. From the game AI techniques I've learned (genetic algorithms, neural networks, fuzzy logic, state machines, etc.), the AI in Black and White 2 seems to match most closely with what I know about reinforcement learning. It's a technique that uses online learning (observing results as the game is played) instead of training (from examples constructed ahead of time), allows both exploration (trying new things in order to learn) and exploitation (taking advantage of what you've learned), and associates rewards (like whether the attack was successful) with actions (like kicking down the wall and keeping the army away from my archers). I recommend Sutton and Barto's book if you want to learn more. It's entirely possible though that the game uses something much simpler that just happens to look impressive, but my guess is that it's using reinforcement learning. — Amit — Monday, December 12, 2005 Comments: Post a Comment Links to this post:

Starting on a full time AI job "in the industry" has been an interesting experience. My new secret project is very cool, and the AI is a lot of fun, but I had been very surprised by the sheer amount of engineering details that go into a game. All the stuff that you can just ignore when doing research - well, it all comes back, with a vengeance. :)

This article introduces hidden Markov models, an inexpensive and intuitive method for modeling stochastic processes.

"Forming expectations is a problem that is relatively easy to concretize: AIs have an internal knowledge model of the world. If the AI is able to provide an estimate for what that state is going to look like n ticks in the future, that’s an expectation – and naturally we’re going to want the AI to react not (or not just) to current world-state, but also to its expectation. React to its own expectation. I think that’s a neat idea, and architecturally it makes a lot of sense. Assuming we have a reactive behavior system of some sort already, we don’t, as a first pass, need to modify IT at all. All we need to do is provide future instead of current data. Great!"

In this paper, an implementation of "Stackless Python" (a Python which does not keep state on the C stack) is presented. Surprisingly, the necessary changes affect just a small number of C modules, and a major rewrite of the C library can be avoided. The key idea in this approach is a paradigm change for the Python code interpreter that is not easy to understand in the first place. Recursive interpreter calls are turned into tail recursion, which allows deferring evaluation by pushing frames to the frame stack, without the C stack involved.