On Thu, Sep 18, 2008 at 1:46 AM, Abram Demski <[EMAIL PROTECTED]> wrote:
Speaking of my BPZ-logic... > 2. Good at quick-and-dirty reasoning when needed Right now I'm focusing on quick-and-dirty *only*. I wish to make the logic's speed approach that of Prolog (which is a fast inference algorithm for binary logic). > --a. Makes unwarranted independence assumptions Yes, I think independence should always be assumed "unless otherwise stated" -- which means there exists a Bayesian network link between X and Y. > --b. Collapses probability distributions down to the most probable > item when necessary for fast reasoning Do you mean collapsing to binary values? Yes, that is done in BPZ-logic. > --c. Uses the maximum entropy distribution when it doesn't have time > to calculate the true distribution Not done yet. I'm not familiar with max-ent. Will study that later. > --d. Learns simple conditional models (like 1st-order markov models) > for use later when full models are too complicated to quickly use I focus on learning 1st-order Bayesian networks. I think we should start with learning 1st-order Bayesian / Markov. I will explore mixing Markov and Bayesian when I have time... > 3. Capable of "repairing" initial conclusions based on the bad models > through further reasoning > --a. Should have a good way of representing the special sort of > uncertainty that results from the methods above Yes, this can be done via meta-reasoning, which I'm currently working on. > --b. Should have a "repair" algorithm based on that higher-order uncertainty Once it is represented at the meta-level, you may do that. But higher-order uncertain reasoning is not high on my priority list... YKY ------------------------------------------- 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=114414975-3c8e69 Powered by Listbox: http://www.listbox.com
