On Tue, Jan 13, 2009 at 7:50 AM, YKY (Yan King Yin) <[email protected]> wrote: > On Tue, Jan 13, 2009 at 6:19 AM, Vladimir Nesov <[email protected]> wrote: > >> I'm more interested in understanding the relationship between >> inference system and environment (rules of the game) that it allows to >> reason about, > > Next thing I'll work on is the planning module. That's where the AGI > interacts with the environment. > >> ... about why and how a given approach to reasoning is >> expected to be powerful. > > I think if PZ logic can express a great variety of uncertain > phenomena, that's good enough. I expect it to be very efficient too. >
"Phenomena" are not uncertain, you may as well regard the problem as inference over deterministic and insanely detailed physics. To give a hint of why logic alone doesn't seem to address important questions: You can use concepts to capture sets of configurations, weighted with probability, but the main trick is in capturing the structure (=specific inference schemes). You can model something by a HMM, with one opaque hidden state, but even if it abstracts away most of the physical details, the state usually has its internal structure that you have to learn in order to cope with data sparsity. This structure can be represented by multiple individual concepts that look at the state of the system from multiple points of view, each concept describing an element of the structure of the system. It might be a good idea to select your concepts so that they arrange in something like a Bayesian network then, of variety that allows inferences you need (or any other inference scheme, for that matter). But it's a static view, where you focus on decomposition of a specific state. In reality, you'd want to reuse your concepts, recognizing them again and again in different situations, and reassemble the model from them. Each concept applies to many different situations, and its relationship to other concepts is context-dependent. This interaction between the structure of environment and custom reassembling of models to describe the focus of attention while reusing past knowledge seems to be the most interesting part at this point. -- Vladimir Nesov [email protected] http://causalityrelay.wordpress.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=126863270-d7b0b0 Powered by Listbox: http://www.listbox.com
