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<http://www.aiai.ed.ac.uk/oplan/cdl/index.html>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<http://texai.svn.sourceforge.net/viewvc/texai/BehaviorLanguage/data/method-definitions.bl?view=markup>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: 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
