Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!utgpu!water!watmath!clyde!cbosgd!ucbvax!BRILLIG.UMD.EDU!hendler From: hendler@BRILLIG.UMD.EDU.UUCP Newsgroups: comp.ai.digest Subject: Re: AIList Digest V5 #171 Message-ID: <8707062225.AA18518@brillig.umd.edu> Date: Mon, 6-Jul-87 18:25:51 EDT Article-I.D.: brillig.8707062225.AA18518 Posted: Mon Jul 6 18:25:51 1987 Date-Received: Sat, 11-Jul-87 13:44:48 EDT Sender: daemon@ucbvax.BERKELEY.EDU Distribution: world Organization: The ARPA Internet Lines: 20 Approved: ailist@stripe.sri.com While I have some quibbles with Don N.'s long statement on AI viz (or vs.) science, I think he gets close to what I have felt a key point for a long time -- that the move towards formalism in AI, while important in the change of AI from a pre-science (alchemy was Drew McDermott's term) to a science, is not enough. For a field to make the transition an experimental methodology is needed. In AI we have the potential to decide what counts as experimentation (with implementation being an important consideration) but have not really made any serious strides in that direction. When I publish work on planning and claim ``my system makes better choices than'' I cannot verify this other than by showing some examples that my system handles that 's can't. But of course, there is no way of establishing that couldn't do examples mine can't and etc. Instead we can end up forming camps of beliefs (the standard proof methodology in AI) and arguing -- sometimes for the better, sometimes for the worse. While I have no solution for this, I think it is an important issue for consideration, and I thank Don for provoking this discussion. -Jim Hendler