February 2006
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Game/AI: Crowd Densities revisited
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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.
![](http://blogmarks.net/screenshots/2006/02/25/a6af98f8d810ad6fe1775b2ea3a592a2.png)
Game/AI: AI Planning for games and characters CFP
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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.
January 2006
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Game/AI: Presentation Details
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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. :)
![](http://blogmarks.net/screenshots/2006/01/16/14b03eb0173600101c14448d66b7af1e.png)
Game/AI: Hidden Markov Models
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This article introduces hidden Markov models, an inexpensive and intuitive method for modeling stochastic processes.
November 2005
![](http://blogmarks.net/screenshots/2005/11/14/989b56368b0c2ebc77cdf4dd10e18da4.png)
Game/AI: Great Expectation-Formations
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"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!"
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