Xref: utzoo alt.cyb-sys:34 comp.ai.neural-nets:986 Path: utzoo!attcan!utgpu!jarvis.csri.toronto.edu!mailrus!wuarchive!gem.mps.ohio-state.edu!apple!sun-barr!rutgers!sunybcs!bingvaxu!cybsys From: cybsys@bingvaxu.cc.binghamton.edu (CYBSYS-L Moderator) Newsgroups: alt.cyb-sys,comp.ai.neural-nets Subject: Re: Data Complexity Message-ID: <2499@bingvaxu.cc.binghamton.edu> Date: 9 Oct 89 17:06:14 GMT References: <517@uvaee.ee.virginia.EDU> <1269@sdcc13.ucsd.EDU> Reply-To: cybsys@bingvaxu.cc.binghamton.edu.cc.binghamton.edu (CYBSYS-L Moderator) Organization: SUNY Binghamton, NY Lines: 21 [ Cross-posted from CYBSYS-L@BINGVMB ] Really-From: heirich%cs@ucsd.edu (Alan Heirich) I am also interested in the question of complexity measures raised by Ali Minai. I think it is an important issue for anyone concerned with self organizing systems -- i.e. how can you measure the extent to which a system is organized? Some candidates I've thought of are Shannon entropy, Kolmogorov entropy, "integrality" (a measure defined by I-don't-remember-who, discussed by Brooks in the recent book "Information, entropy and evolution", which I assume readers of this list are familiar with, or should be). I would like to hear about other possibilties. ------------------------- Alan Heirich Comp. Sci. & Eng., Cognitive Science C-014 University of California, San Diego 92093 heirich@cs.ucsd.edu aheirich@ucsd.bitnet