January 7, 2010
Most readers should be familiar with George Santayana‘s quote about remembering the past (also called Santayana’s Law of Repetitive Consequences).
The rise of the BRMS over the last few years has brought lots of enthusiastic new members to our little rules world. These people are eager to contribute to the field and make their own mark. It is the job of the “old guard” to make sure the newcomers are properly aware of the prior research in our field. (Having only participated in the space since 1995, I consider myself to still be a newcomer.) For example, the rise of the multi-core processor means that tons of older research in parallel rule engines is of interest and relevant. For another example, the classic work on conflict resolution strategies doesn’t appear to be online and is in a long out-of-print 30-year-old book. (And at least the prices for “Pattern-Directed Inference Systems” are somewhat affordable – as of this writing, “Human Problem Solving” starts at $190 and goes up to $800 on Amazon.) A third example is that the Wikipedia article on the Rete algorithm only has references to papers that are not online for one reason or another. (I personally haven’t even seen the “A network match routine for production systems.” working paper.)
Thus, I would like to highlight a few useful resources:
We need to work together as a group to improve the online availability of our history.
No Comments » | AI, rules | Tagged: AI, archaeology, backward chaining, Forgy, forward chaining, multicore, parallel, parallelism, rule engine, rules | Permalink
Posted by Karl W. Reinsch
January 7, 2010
I just spotted another .NET implementation of Rete – NRuler. The site shows only 10 downloads, but it seems to have been live less than a month so far. Any of the 10 downloaders care to share their impressions? How does it compare with NxBRE or SRE?
(As an aside, I spotted NRuler because it is linked in the “See also” section of the Wikipedia article on Rete. At best, the link to NRuler should be an “External link” rather than a “See also” – and probably not even that. As an industry, we need to stop spamming this article with promotional product-specific links. Yes, I know that NRuler isn’t a commercial product, but I don’t see any reason for it to be linked there over any other piece of software.)
No Comments » | .NET, AI, rules | Tagged: .NET, AI, Microsoft, NRuler, NxBRE, rete, rule engine, rules, Simple Rule Engine, SRE | Permalink
Posted by Karl W. Reinsch
January 6, 2010
I recently stumbled across another rule engine for .NET that I hadn’t seen before: Simple Rule Engine (SRE).
Looks dormant, or possibly dead altogether. If any readers have tried it out, I welcome comments about your experiences with it.
No Comments » | .NET, AI, rules | Tagged: .NET, AI, Microsoft, rete, rule engine, rules, Simple Rule Engine, SRE | Permalink
Posted by Karl W. Reinsch
November 19, 2009
The results of the Mario Bros. AI competition have been made available. (Not really surprising that the top three entries used A*.)
Robin Baumgarten has won, and has made his source code available. AIGameDev.com also has an interview with Robin.
No Comments » | AI, games, video games | Tagged: A*, AI, games, machine learning, Mario, Nintendo, video games | Permalink
Posted by Karl W. Reinsch
August 6, 2009
Julian Togelius and Sergey Karakovskiy have organized a competition to create an agent (or AI) that plays the video game Super Mario Bros. – or, more accurately, Infinite Mario Bros. a tribute game featuring random level generation.
The advantage of using Infinite Mario Bros. is the random level generation – which can let the agent learn more generalized playing tactics rather than tactics that are tailored to a static set of levels as in Ms. Pac-Man or Pitfall.
I look forward to seeing the results of the competition, and hope to see source code published as well.
No Comments » | AI, games, video games | Tagged: AI, games, machine learning, Mario, Ms. Pac-Man, Nintendo, Pitfall, video games | Permalink
Posted by Karl W. Reinsch
June 29, 2009
The Netflix Prize has entered the 30-day notification period as a team has announced that they have achieved a 10.05% improvement over the original Cinematch algorithm.
Some further background on the contest can be found in a nice writeup in Wired from last year.
No Comments » | AI, datamining | Tagged: AI, algorithms, datamining, machine learning, Netflix | Permalink
Posted by Karl W. Reinsch
June 29, 2009
From Rutgers university comes a learning algorithm that they have applied to playing the Atari 2600 game “Pitfall!”.
An example video is on YouTube.
One of the research papers is apparently here (although the site isn’t being very responsive at the moment).
I’ll get around to posting on machine learning for Pac-Man/Ms. Pac-Man at some point as well.
(Spotted on Kotaku and GameSetWatch.)
No Comments » | AI, games, video games | Tagged: AI, Atari 2600, games, machine learning, Pitfall, video games | Permalink
Posted by Karl W. Reinsch
May 3, 2009
No Comments » | AI, Microsoft, rules | Tagged: AI, BizTalk, BizTalk BRE, expert systems, Microsoft, Microsoft BRE, rete, rules, Ship-It | Permalink
Posted by Karl W. Reinsch
March 25, 2009
I’ve been watching the multi-core video card space and looking at efforts to offload AI onto that hardware. In particular, I’m curious to see the shakeout of the various APIs. One candidate usage is, of course, video games.
Read the rest of this entry »
No Comments » | AI, games, video games | Tagged: AI, pathfinding, video games | Permalink
Posted by Karl W. Reinsch
February 16, 2009
And what do we have here? It seems that Nvidia and AMD are already on top of the idea of offloading AI onto GPUs.
Read the rest of this entry »
6 Comments | AI, video games | Tagged: AI, AMD, Cuda, GPGPU, multicore, NVidia, parallelism, video games | Permalink
Posted by Karl W. Reinsch