On Thu, Jan 20, 2011 at 2:37 AM, Peter Bleackley <[log in to unmask]> wrote: > staving Logan Kearsley: >>> >>> As a retired computer programmer with an interest in artificial >>> intelligence, chat-bots, and machine translation, I can use a narrow, >>> and purely utilitarian definition of "better" by scoring proposed >>> changes on whether or not, and to what extent those changes would make >>> the task of computerized parsing and comprehension easier. Difficult >>> sentences like "Time flies like an arrow, but fruit flies like a >>> banana." must be made unambiguous and easy to parse. >> >> My own experience in this realm indicates that "easy for a computer to >> parse" and "easy for a human to parse" are only loosely correlated. >> Care must be taken to not make it too difficult for humans to use >> while still improving things for the computer (although, reading >> further, it seems like you're aware of that). >> > > This reminds me of Jeffrey Henning's Fith, an exolang based on FORTH. It's > an exolang whose grammar is based on a LIFO stack, and is impossible for > humans to parse in real time, since a LIFO stack simply isn't a natural way > for the human brain to work. LIFO stacks are pretty easy for computers to > parse, however. > > I do wonder it it would be any easier for alien kangaroos to mentally parse > a LIFO stack than it would be for humans. It doesn't seem very organic. I was inspired by that a long time ago. The basic idea can be fixed-up for easier human comprehension (even maintaining compatibility with the original rules, up to a certain extent) by tricks like limiting the stack depth or what classes of phrases are allowed to go beyond certain depths. Basing a new language on that idea gives you an RPN grammar, which can be arranged nicely for humans, but can also result in nested structures that get very confusing. It helps a lot if you sacrifice a little bit of word order for a little bit of explicit case marking, so you can move around some of those nested trees. My overall conclusions are that computers get by better with a smaller set of rules, but humans have a balance point somewhere where fewer rules result in at least as much difficulty as more. -l.