> To know > whether they could have missed each other you really do have to visualise > the clinic and the possible crowds and the individual figures - and "figure > out" whether they could be physically apart enough not to see each other. > Reasoning here depends on the brain's imaginative capacity to move figures > around the world's scenes/stages and check whether they fit together or not. > Checking whether logical symbols match each other isn't going to help you, > and is a fundamentally secondary operation.
No, what you describe is just one possible strategy for solving the problem. OpenCog also has some code for this sort of simulation modeling, but it's not always needed... If asked whether two folks in a doctor's clinic at around the same time are likely to bump into each other, I can certainly answer the question without visualizing the clinic. If someone asked me that question "Jane and Sally were at a certain doctor's clinic at around the same time; do you think they bumped into each other?", I wouldn't necessarily ask the questioner about the specific geometry of the clinic, I might just ask something general like "How big is the clinic? How big is the waiting area? How many people tend to be there at once?" Based on this general information, I could then reason logically about the odds that Jane and Sally bumped into each other. The chain of reasoning might go something like (A and B) implies C where A) Two people in a big crowded space are unlikely to notice each other B) The doctor's office is a big crowded space, according to what I've just been told C) Jane and Sally probably didn't notice each other when they were in the doctor's office ... This is **uncertain logical reasoning** applied to commonsense knowledge. At some point in the history of the mind doing this reasoning, the proposition (A) was probably learned from experience [though it's possible to learn such things via language instead].... However, just because I learned (A) via visual, embodied experience at some point in my past, doesn't prevent me from using (A) in the future in chains of logical reasoning where I have no idea what the big crowded space in question looks like. This is part of the power of logical reasoning: it lets us draw conclusions about cases where we **lack** the concrete sensory information or episodic memory to use more direct methods. There's no contradiction between visual observation, "mind's eye" simulation, and logical reasoning. These mental processes all need to work together. There's also no contradiction between logical reasoning, as a description of what minds do sometimes, and neural network modeling as a way of describing brains. There are clear connections between logical inference rules and neural net dynamics (e.g. Hebbian learning between neuronal groups and uncertain term logic deduction). Neural nets can implement logical inference along with other cognitive methods, though in OpenCog we have not currently chosen neural nets as our implementation tool. It seems you may be unaware of the unconscious uncertain logical reasoning your mind continually does. But this deficit in your own introspective habits or capability, while unfortunate for you, shouldn't be taken as a constraint for others' AGI development work.... -- Ben G ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
