Path: utzoo!attcan!uunet!lll-winken!lll-tis!ames!mailrus!tut.cis.ohio-state.edu!bloom-beacon!ADS.COM!dan From: dan@ADS.COM (Dan Shapiro) Newsgroups: comp.ai.digest Subject: Animal Behavior and AI Message-ID: <19880824193258.2.NICK@HOWARD-JOHNSONS.LCS.MIT.EDU> Date: 24 Aug 88 19:32:00 GMT Sender: daemon@bloom-beacon.MIT.EDU Organization: The Internet Lines: 30 Approved: ailist@ai.ai.mit.edu To: ames!comp-ai-digest@ames.arc.nasa.gov Path: zodiac!dan From: Dan Shapiro Newsgroups: comp.ai.digest Subject: Animal Behavior and AI Date: Mon, 22 Aug 88 14:42 EDT References: <19880820041306.5.NICK@HOWARD-JOHNSONS.LCS.MIT.EDU> Sender: zodiac!ads.com!news@ames.arc.nasa.gov Reply-To: Dan Shapiro Organization: Advanced Decision Systems, Mt. View, CA (415) 960-7300 Lines: 18 Motion control isn't the only area where studying animals has merit. I have been toying with the idea of studying planning behavior in various creatures; a reality check would add to the current debate about "logical forethought" vs. "reactive execution" in the absence of plan structures. A wrinkle is that it would be very hard to get a positive fix on an animal's planning capabilities since all we can observe is their behavior (which could be motivated by a range of mechanisms). My thought is to study what we would call "errors" in animal behavior - behaviors that a more cognizant or capable planning engine would avoid. It seems to me that there must be a powerful difference between animal planning/action strategies and (almost all) current robotic approaches; creatures manage to do something reasonable (they survive) in a wide variety of situations while robots require very elaborate knowledge in order to act in narrow domains.