Ed Porter said:
It should be noted that Shruiti uses a mix of forward changing and backward
chaining, with an architecture for controlling when and how each is used.
...
My understanding that forward reasoning is reasoning from conditions to
consequences, and backward reasoning is the opposite. But I think what is a
condition and what is a consequence is not always clear, since one can use
if A then B rules to apply to situations where A occurs before B, B occurs
before A, and A and B occur at the same time. Thus I think the notion of
what is forward and backward chaining might be somewhat arbitrary, and could
be better clarified if it were based on temporal relationships. I see no
reason that Shruiti's "?" activation should not run be spread across all
those temporal relationships, and be distinguished from Shruiti's "+" and
"-" probabilistic activation by not having a probability, but just a
temporary attentional characteristic. Additional inference control mechanism
could then be added to control which directions in time to reason with in
different circumstances, if activation pruning was necessary.
Furthermore, Shruiti, does not use multi-level compositional hierarchies for
many of its patterns, and it only uses generalizational hierarchies for slot
fillers, not for patterns. Thus, it does not many of the general reasoning
capabilities that are necessary for NL understanding.... Much of the spreading
activation in a more general purpose AGI would be up and down compositional
and generaliztional hiearachies, which is not necessarily forward or
backward chaining, but which is important in NL understanding. So I agree
that simple forward and backward chaining are not enough to solve general
inferences problems of any considerable complexity.
-----------------------------------
Can you describe some of the kinds of systems that you think would be necessary
for complex inference problems. Do you feel that all AGI problems (other than
those technical problems that would be common to a variety of complicated
programs that use large data bases) are essentially inference problems? Is
your use of the term inference here intended to be inclusive of the various
kinds of problems that would have to be dealt with or are you referring to a
class of problems which are inferential in the more restricted sense of the
term? (I feel that the two senses of the term are both legitimate, I am just a
little curious about what it was that you were saying.)
I only glanced at a couple of papers about SHRUTI, and I may be looking at a
different paper than you were talking about, but looking at the website it
looks like you were talking about a connectionist model. Do you think a
connectionist model (probabilistic or not) is necessary for AGI. In other
words, I think a lot of us agree that some kind of complex (or complicated)
system of interrelated data is necessary for AGI and this does correspond to a
network of some kind, but these are not necessarily connectionist.
What were you thinking of when you talked about multi-level compositional
hierarchies that you suggested were necessary for general reasoning?
If I understood what you were saying, you do not think that activation
synchrony is enough to create insightful binding given the complexities that
are necessary for higher level (or more sophisticated) reasoning. On the other
hand you did seem to suggest that temporal synchrony spread across a rhythmic
flux ofrelational knowledge of might be useful for detecting some significant
aspects during learning. What do you think?
I guess what I am getting at is I would like you to make some speculations
about the kinds of systems that could work with complicated reasoning problems.
How would you go about solving the binding problem that you have been talking
about? (I haven't read the paper that I think you were referring to and I only
glanced at one paper on SHRUTI but I am pretty sure that I got enough of what
was being discussed to talk about it.)
Jim Bromer
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agi
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