YKY said: How about these scenarios: 1. "If a task is to be repeated 'many' times, use a loop. If only 'a few' times, write it out directly." -- this requires fuzziness 2. "The gain of using algorithm X on this problem is likely to be small." -- requires probability
Agreed. When Texai gets to this point I would incorporate an open source fuzzy logic library such as JFuzzyLogic. I believe I can interface the Texai KB to a fuzzy logic library without too much difficulty. Maybe you mean spreading activation is used to locate candidate facts / rules, over which actual deductions are attempted? That sounds very promising. One question is how to learn the association between nodes. To be clear, I would do the opposite. Offline backchaining, deductive inference could be performed to cache conclusions for common inference problems. The cache is implemented via spreading activation links between the antecedent terms of the rules and the consequent terms of the conclusions. Humans do not perform modus ponens deduction from first principles for commonsense problem solving. I believe that spreading activation can be employed to perform machine problem solving (e.g. executing a learned procedure) in a cognitively plausible fashion without real-time theorem proving. 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 5:29:07 PM Subject: Re: [agi] OpenCog's logic compared to FOL? On 6/4/08, Stephen Reed <[EMAIL PROTECTED]> wrote: 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. How about these scenarios: 1. "If a task is to be repeated 'many' times, use a loop. If only 'a few' times, write it out directly." -- this requires fuzziness 2. "The gain of using algorihtm X on this problem is likely to be small." -- requires probability 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. Maybe you mean spreading activation is used to locate candidate facts / rules, over which actual deductions are attempted? That sounds very promising. One question is how to learn the association between nodes. 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
