That was helpful. Thanks. 2008/12/1 Matt Mahoney <[EMAIL PROTECTED]>: > --- On Sun, 11/30/08, Philip Hunt <[EMAIL PROTECTED]> wrote: > >> Can someone explain AIXI to me? > > AIXI models an intelligent agent interacting with an environment as a pair of > interacting Turing machines. At each step, the agent outputs a symbol to the > environment, and the environment outputs a symbol and a numeric reward signal > to the agent. The goal of the agent is to maximize the accumulated reward. > > Hutter proved that the optimal solution is for the agent to guess, at each > step, that the environment is simulated by the shortest program that is > consistent with the interaction observed so far. > > Hutter also proved that the optimal solution is not computable because the > agent can't know which of its guesses are halting Turing machines. The best > it can do is pick numbers L and T, try all 2^L programs up to length L for T > steps each in order of increasing length, and guess the first one that is > consistent. If there are no matches, then it needs to choose larger L and T > and try again. That solution is called AIXI^TL. It's time complexity is O(T > 2^L). In general, it may require L up to the length of the observed > interaction (because there is a fast program that outputs the agent's > observations from a list of length L). > > In a separate paper ( http://www.vetta.org/documents/ui_benelearn.pdf ), Legg > and Hutter propose defining universal intelligence as the expected reward of > an AIXI agent in random environments. > > The value of AIXI is not that it solves the general intelligence problem, but > rather it explains why the problem is so hard. It also justifies a general > principle that is already used in science and in practical machine learning > algorithms: to choose the simplest hypothesis that fits the data. It formally > defines "simple" as the length of the shortest program that outputs a > description of the hypothesis. > > For example, to avoid overfitting in neural networks, you should use the > smallest number of connections and the least amount of training needed to fit > the training data, then stop. In this case, the complexity of your neural > network is the length of the shortest program that outputs the configuration > of your network and its weights. Even if you don't know what that program is, > and haven't chosen a programming language, you may reasonably expect that > fewer connections, smaller weights, and coarser weight quantization will > result in a shorter program. > > -- Matt Mahoney, [EMAIL PROTECTED] > > > > > ------------------------------------------- > 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/?&; > Powered by Listbox: http://www.listbox.com >
-- Philip Hunt, <[EMAIL PROTECTED]> Please avoid sending me Word or PowerPoint attachments. See http://www.gnu.org/philosophy/no-word-attachments.html ------------------------------------------- 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=120640061-aded06 Powered by Listbox: http://www.listbox.com
