Ben,
here are some of my random thoughts on this.
Indeed, loose reasoning over generalized concepts should be very important
for AGI, and proof sketching seems an interesting analogy here. However,
there are others. E.g. in Heuristic Search, there were attempts to
generalize states and transitions between them, and to search in this
greatly reduced search space first. Unfortunately, I don't know any general
and interesting solution here. In deep reinforcement learning, there also
was a paper on learning both a space of generalized states and a policy for
it. I don't believe that such deep learning models will scale up to complex
symbolic domains, but 'theorem proving' approach might also be too
restrictive...
I have been thinking about this topic for a while recently, and I believe
that inference control should be tightly connected with conceptualization.
We rarely can find patterns in inference trees per se, but humans usually
construct new concepts, in terms of which they can describe
(domain-specific) inference rules. E.g. in the Go game, players use quite
abstract notions ('wall', etc.) and reasoning over them (building a wall
here will protect the territory and spread the influence). Such rules and
concepts can be mined not in the inference trees, but in historical data of
agent-environment interactions...
So,
- Most inference rules are domain-specific rules, and they involve concepts
constructed specifically to be used in these rules (one can go further and
say that most of our concepts are inference control concepts, but it sounds
too radical)
- There are just a few general inference rules (e.g. entities, which are
similar w.r.t. some properties, might be similar w.r.t. other properties).
These rules involve general concepts (e.g. similarity), which can be either
pre-defined, or which can also be constructed together with these rules for
these rules to work (e.g. similar entities are entities for which this
inference rule works). Such rules based on predefined abstract concepts and
relations can be found by Pattern Miner, but this is of limited interest.
- Inference/reasoning is an abstracted simulation/prediction. There should
be no huge difference in constructing higher-level concepts from experience
and from inference trees.
- Generalization is an extremely non-trivial task. And what I see is that
OpenCog is very refined in the part of reasoning, but it uses very
simplistic Pattern Miner for generalization. Obviously, we cannot use
anything heavier at the scale of the whole Atomspace, but for isolated
domains, this should necessarily be done. Well, there is also MOSES in
OpenCog, but it is also somewhat specialized, and not deeply integrated...
Well... 'Proof sketching' for inference control is the step in the right
direction, but we should focus much more on a stronger generalization...-- Alexey 2018-01-07 13:52 GMT+03:00 Ben Goertzel <[email protected]>: > Nil, Zar, Alexey, Eddie, Mike, anyone else interested, > > Attached file "inference-sketch-notes.pdf" outlines some speculative > thinking i've been doing regarding using "proof sketching" as a means > of PLN inference control... > > (the other attached file contains, toward the end, an example bio-AI > inference that I use as an example in the document...) > > Nil, I am throwing these ideas out here now in part because they > present a potentially important use-case for the integration of > rule-engine inference into the pattern miner, as you're in the midst > of working on... > > -- Ben > > > -- > Ben Goertzel, PhD > http://goertzel.org > > "In the province of the mind, what one believes to be true is true or > becomes true, within certain limits to be found experientially and > experimentally. These limits are further beliefs to be transcended. In > the mind, there are no limits.... In the province of connected minds, > what the network believes to be true, either is true or becomes true > within certain limits to be found experientially and experimentally. > These limits are further beliefs to be transcended. In the network's > mind there are no limits." -- John Lilly > > -- > You received this message because you are subscribed to the Google Groups > "opencog" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To post to this group, send email to [email protected]. > Visit this group at https://groups.google.com/group/opencog. > To view this discussion on the web visit https://groups.google.com/d/ > msgid/opencog/CACYTDBejdBtiWWTKpdAq3M3RbomTCccSESfqQ-eBMHhWH9DR2w%40mail. > gmail.com. > For more options, visit https://groups.google.com/d/optout. > -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/CABpRrhwe%2BGKRq601s5y0T8-N4%3DZySx7WK5F3qsE_7DARfA-2KA%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
