I see no argument in your text against my main argumentation, that an AGI should be able to learn chess from playing chess alone. This I call straw man replies.
My main point against embodiment is just the huge effort for embodiment. You could work for years with this approach and a certain AGI concept until you recognize that it doesn't work. If you apply your AGI concept in a small and even not necessarily AGI-complete domain you would come much faster to a benchmark whether your concept is even worth to make difficult studies with embodiment. Chess is a very good domain for this benchmark because it is very easy to program and it is very difficult to outperform human intelligence in this domain. - Matthias Von: David Hart [mailto:[EMAIL PROTECTED] Gesendet: Mittwoch, 22. Oktober 2008 09:43 An: agi@v2.listbox.com Betreff: Re: [agi] If your AGI can't learn to play chess it is no AGI Matthias, You've presented a straw man argument to criticize embodiment; As a counter-example, in the OCP AGI-development plan, embodiment is not primarily used to provide domains (via artificial environments) in which an AGI might work out abstract problems, directly or comparatively (not to discount the potential utility of this approach in many scenarios), but rather to provide an environment for the grounding of symbols (which include concepts important for doing mathematics), similar to the way in which humans (from infants through to adults) learn through play and also through guided education. 'Abstraction' is so named because it involves generalizing from the specifics of one or more domains (d1, d2), and is useful when it can be applied (with *any* degree of success) to other domains (d3, ...). Virtual embodied interactive learning utilizes virtual objects and their properties as a way of generating these specifics for artificial minds to use to build abstractions, to grok the abstractions of others, and ultimately to build a deep understanding of our reality (yes, 'deep' in this sense is used in a very human-mind-centric way). Of course, few people claim that machine learning with the help of virtually embodied environments is the ONLY way to approach building an AI capable of doing and mathematics (and communicating with humans about mathematics), but it is an approach that has *many* good things going for it, including proving tractable via measurable incremental improvements (even though it is admittedly still at a *very* early stage). -dave On Wed, Oct 22, 2008 at 4:20 PM, Dr. Matthias Heger <[EMAIL PROTECTED]> wrote: It seems to me that many people think that embodiment is very important for AGI. For instance some people seem to believe that you can't be a good mathematician if you haven't made some embodied experience. But this would have a rather strange consequence: If you give your AGI a difficult mathematical problem to solve, then it would answer: "Sorry, I still cannot solve your problem, but let me walk with my body through the virtual world. Hopefully, I will then understand your mathematical question end even more hopefully I will be able to solve it after some further embodied experience." AGI is the ability to solve different problems in different domains. But such an AGI would need to make experiences in domain d1 in order to solve problems of domain d2. Does this really make sense, if every information necessary to solve problems of d2 is in d2? I think an AGI which has to make experiences in d1 in order to solve a problem of domain d2 which contains everything to solve this problem is no AGI. How should such an AGI know what experiences in d1 are necessary to solve the problem of d2? In my opinion a real AGI must be able to solve a problem of a domain d without leaving this domain if in this domain there is everything to solve this problem. >From this we can define a simple benchmark which is not sufficient for AGI but which is *necessary* for a system to be an AGI system: Within the domain of chess there is everything to know about chess. So if it comes up to be a good chess player learning chess from playing chess must be sufficient. Thus, an AGI which is not able to enhance its abilities in chess from playing chess alone is no AGI. Therefore, my first steps in the roadmap towards AGI would be the following: 1. Make a concept for your architecture of your AGI 2. Implement the software for your AGI 3. Try if your AGI is able to become a good chess player from learning in the domain of chess alone. 4. If your AGI can't even learn to play good chess then it is no AGI and it would be a waste of time to make experiences with your system in more complex domains. -Matthias _____ agi | <https://www.listbox.com/member/archive/303/=now> Archives <https://www.listbox.com/member/archive/rss/303/> Fehler! Es wurde kein Dateiname angegeben.| <https://www.listbox.com/member/?&;> Modify Your Subscription <http://www.listbox.com> Fehler! Es wurde kein Dateiname angegeben. _____ agi | <https://www.listbox.com/member/archive/303/=now> Archives <https://www.listbox.com/member/archive/rss/303/> | <https://www.listbox.com/member/?&; 7> Modify Your Subscription <http://www.listbox.com> ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=117534816-b15a34 Powered by Listbox: http://www.listbox.com
