On 1/24/07, YKY (Yan King Yin) <[EMAIL PROTECTED]> wrote:
Suppose I have a set of *deductive* facts/rules in FOPL. You can actually use this data in your AGI to support other forms of inference such as induction and abduction. In this sense the facts/rules collection does not dictate the form of inference engine we use.
No, you cannot do that without twisting some definitions. You are right that now many people define "induction" and "abduction" in the language of FOPL, but what they actually do is to omit important aspects in the process, such as uncertainty. To me that is cheating. I addressed this issue in http://nars.wang.googlepages.com/wang.syllogism.ps . In http://www.springer.com/west/home/computer/artificial?SGWID=4-147-22-173659733-0 I explained in detail (especially in Ch. 9 and 10) why the language of FOPL is improper for AI.
I am still reading your book, but I found numerous good ideas in it. I know that you treat deduction, induction, and abduction in a unified way. That is a very elegant theory but it may have problems. For example, if: (1) I read a lot of books (2) I hate my mom your system may infer by induction that "reading a lot of books -> hating ones mom". In some instances doing this is meaningful, but in general your system may be flooded with a lot of these speculative statements, drawing time from the day-to-day deductive operations.
Yes, a conclusion like that will be derived, if the system has nothing better to do. Such a conclusion looks stupid only after the system knows much more about the related concepts. The system won't be flooded with a lot of these speculative statements, as far as it doesn't try to derive every possible conclusion, or to treat every conclusion as equally important.
I tend to think of induction as something less essential than deduction.
In general, I agree --- that is why inductive/abductive conclusions are in general less confident than deductive ones in NARS. However, for a concrete problem, the crucial step may be provided initially by a hypothesis with low confidence.
That's why my top priority is to build an inference engine for deduction. Inductive learning will be added later in the form of data mining, which is very computation-intensive.
I'm afraid it is not going to work --- many people have tried to extend FOPL to cover a wider range, and run into all kinds of problems. To restart from scratch is actually easier than to maintain consistency among many ad hoc patches and hacks. To me, one of the biggest mistake of mainstream AI is to treat "learning" as independent to "working", and can be added in later. To see AI in this way and to put learning into the foundation will produce very different systems. In NARS, "learning" and "reasoning", as well as some other "cognitive facilities", are different aspects of the same underlying process, and cannot handled separately. Pei
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