Instead of responding to each comment, I'd make the following answers altogether:
1. This paper assumes a background of algorithm analysis. People without that won't correctly understand what I mean. 2. A CPS system is "non-algorithmic" with respect to some problems, while still be "algorithmic" with respect to some other problems. For NARS, the former is the case for all user-level "problems" (that the user provides in Narsese), and the latter is the case in micro-level (single step) or macro-level (lifelong experience). On the contrary, An APS system is "algorithmic" in all these three levels. As system designers, I write algorithms to make NARS run, though I don't write any algorithm to handle the problems the system meets in its own life cycle. This difference has been explained in http://nars.wang.googlepages.com/wang.computation.pdf . 3. No, NARS hasn't solved any problem that no human can (for what the current implementation can do, visit http://code.google.com/p/open-nars/). The point the paper want to make is that the "problems" an AI system can "solve" are not bounded by computability theory and computational complexity theory, though it is still too early to tell how far it can go in this direction. Pei On Thu, Sep 18, 2008 at 4:05 PM, Pei Wang <[EMAIL PROTECTED]> wrote: > TITLE: Case-by-case Problem Solving (draft) > > AUTHOR: Pei Wang > > ABSTRACT: Case-by-case Problem Solving is an approach in which the > system solves the current occurrence of a problem instance by taking > the available knowledge into consideration, under the restriction of > available resources. It is different from the traditional Algorithmic > Problem Solving in which the system applies a given algorithm to each > problem instance. Case-by-case Problem Solving is suitable for > situations where the system has no applicable algorithm for a problem. > This approach gives the system flexibility, originality, and > scalability, at the cost of predictability. This paper introduces the > basic notion of case-by-case problem solving, as well as its most > recent implementation in NARS, an AGI project. > > URL: http://nars.wang.googlepages.com/wang.CaseByCase.pdf > ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=114414975-3c8e69 Powered by Listbox: http://www.listbox.com
