----- Original Message ---- From: Richard Loosemore <[EMAIL PROTECTED]> To: [email protected] Sent: Friday, April 11, 2008 3:06:21 PM Subject: Re: [agi] Blog essay on the complex systems problem
Richard Loosemore wrote: [snip] I would not say that your hierarchical control structure is doomed to failure because I do not yet know how close it is to what we understand of the human cognitive system. The reason for saying that is that, in the end, the conclusion of my argument is that we must stay quite close to the human system, and adopt a methodology that supports a certain kind of "agnostic" exploration of different types of system. In that context it be that your HCS is quite close to the system that works in human cognition, and in that case there would be nothing wrong with your choice. Whew.. The only thing I would say is to watch out for dependencies between your HCS and other aspects of your system. If the HCS requires a strictly serial evaluation of goals that are explicitly represented using the same knowledge representation scheme as is used for regular declarative knowledge, for example, I would counsel caution, because I believe that this design runs into trouble. Hmm, I'm not sure about whether Texai will perform strictly serial evaluation of goals, as an HCS naturally lends itself to a hierarchical task network in which higher level tasks can be planned symbolically, and in which lower level tasks are simply performed reactively (e.g. subsumption architecture). There are many opportunities for parallelism in an HCS. With the provision that Texai goal-achieving tasks will have associated utilities derived from Bayesian inference, they will indeed be explicitly represented using the same knowledge representation scheme as is used for regular declarative knowledge. The Texai KR scheme is based upon OpenCyc and will be elaborated to represent skills as procedures. As for the organizational perspective, I am not quite sure which point you were addressing with that. Perhaps my point is clarified if you can imagine that a multitude of unorganized human beings is a complex system, as you define it. However, when organized, these same humans can perform in a scalable, understandable, justifiable, and predictable manner. The Texai architecture not only aspires to be cognitively-plausible with respect to a single human mind, but to be organizationally-plausible with respect to a vast number of Texai instances acting in concert. And your question about the driverless cars architecture... you seem to be suggesting that this might be a "a partitioning and scaling solution to AGI complexity". That choice of words has got me worried about a possible misunderstanding, because you might have been implying that the complex systems problem I have described was all about partitioning the AGI problem to reduce its "complicatedness" .... and that interpretation would be not where I was going with it at all! Sorry, I considered your graph illustration of the problem, and I attempted to provide evidence that my solution has been field tested in an robotics application on the path to AGI. -Steve Stephen L. Reed Artificial Intelligence Researcher http://texai.org/blog http://texai.org 3008 Oak Crest Ave. Austin, Texas, USA 78704 512.791.7860 __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
