YKY said:
1. Probabilistic inference cannot be "grafted" onto crisp logic easily. The changes may be so great that much of the original work will be rendered useless. Agreed. However, I hope that by the time probabilistic inference is taught to Texai by mentors, it will be easy to supersede useless skills with correct ones. 2. You think we can do program synthesis with crisp logic only? This has profound implications if true... All of the work to date on program generation, macro processing, application configuration via parameters, compilation, assembly, and program optimization has used crisp knowledge representation (i.e. non-probabilistic data structures). Dynamic, feedback based optimizing compilers, such as the Java HotSpot VM, do keep track of program path statistics in order to decide when to inline methods for example. But on the whole, the traditional program development life cycle is free of probabilistic inference. I have a hypothesis that program design (to satisfy requirements), and in general engineering design, can be performed using crisp knowledge representation - with the provision that I will use cognitively-plausible spreading activation instead of, or to cache, time-consuming deductive backchaining. My current work will explore this hypothesis with regard to composing simple programs that compose skills from more primitive skills. I am adapting Gerhard Wickler's Capability Description Language to match capabilities (e.g. program composition capabilities) with tasks (e.g. clear a StringBuilder object). CDL conveniently uses a crisp FOL knowledge representation. Here is a Texai behavior language file that contains capability descriptions for primitive Java compositions. Each of these primitive capabilities is implemented by a Java object that can be persisted in the Texai KB as RDF statements. Like yourself, I find the profound implications of automatic programming fascinating. I can only hope that this fascination has guided me down the right path to AGI, rather than down a dead end. I've written a brief blog post on this and related AI-hard problems. Cheers. -Steve Stephen L. Reed Artificial Intelligence Researcher http://texai.org/blog http://texai.org 3008 Oak Crest Ave. Austin, Texas, USA 78704 512.791.7860 ----- Original Message ---- From: YKY (Yan King Yin) <[EMAIL PROTECTED]> To: agi@v2.listbox.com Sent: Tuesday, June 3, 2008 12:20:19 PM Subject: Re: [agi] OpenCog's logic compared to FOL? On 6/3/08, Stephen Reed <[EMAIL PROTECTED]> wrote: I believe that the crisp (i.e. certain or very near certain) KR for these domains will facilitate the use of FOL inference (e.g. subsumption) when I need it to supplement the current Texai spreading activation techniques for word sense disambiguation and relevance reasoning. I expect that OpenCog will focus on domains that require probabilistic reasoning, e.g. pattern recognition, which I am postponing until Texai is far enough along that expert mentors can teach it the skills for probabilistic reasoning. Your approach is sensible, indeed similar to mine -- I'm also experimenting with crisp logic only. But there are 2 problems: 1. Probabilistic inference cannot be "grafted" onto crisp logic easily. The changes may be so great that much of the original work will be rendered useless. 2. You think we can do program synthesis with crisp logic only? This has profound implications if true... YKY ________________________________ agi | Archives | Modify Your Subscription ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=103754539-40ed26 Powered by Listbox: http://www.listbox.com
