Prolog is not fast, it is painfully slow for complex inferences due to using backtracking as a control mechanism
The time-complexity issue that matters for inference engines is inference-control ... i.e. dampening the combinatorial explosion (which backtracking does not do) Time-complexity issues within a single inference step can always be handled via mathematical or code optimization, whereas optimizing inference control is a deep, deep AI problem... So, actually, the main criterion for the AGI-friendliness of an inference scheme is whether it lends itself to flexible, adaptive control via -- taking long-term, cross-problem inference history into account -- learning appropriately from noninferential cognitive mechanisms (e.g. attention allocation...) -- Ben G On Wed, Sep 17, 2008 at 3:00 PM, YKY (Yan King Yin) < [EMAIL PROTECTED]> wrote: > 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/?&; > Powered by Listbox: http://www.listbox.com > -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC Director of Research, SIAI [EMAIL PROTECTED] "Nothing will ever be attempted if all possible objections must be first overcome " - Dr Samuel Johnson ------------------------------------------- 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
