[HN Gopher] Interpretable Model-Based Hierarchical RL Using Indu...
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Interpretable Model-Based Hierarchical RL Using Inductive Logic
Programming
Author : YeGoblynQueenne
Score : 48 points
Date : 2021-09-11 15:16 UTC (7 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| gavinray wrote:
| I don't work in the field, but I sort of passively follow it.
|
| A year ago I made this comment, in another ML thread:
|
| https://news.ycombinator.com/item?id=23315739 "I
| often wonder about whether neural networks might need to meet at
| a crossroads with other techniques." "Inductive
| Logic/Answer Set Programming or Constraints Programming seems
| like it could be a good match for this field. Because from my
| ignorant understanding, you have a more "concrete" representation
| of a model/problem in the form of symbolic logic or constraints
| and an entirely abstract "black box" solver with neural networks.
| I have no real clue, but it seems like they could be
| synergistic?"
|
| I can't interpret the paper -- is this roughly in this vein?
| infogulch wrote:
| I've been thinking along the same lines, it seems like logic +
| ML would complement each other well. Acquiring trustworthy
| labeled data is "THE" problem in ML, and figuring out which
| predicates to string together is "THE" problem in logic
| programming, seems like a perfect match.
|
| A logic program can produce a practically infinite number of
| perfectly consistent test cases for the ML model to learn from,
| and the ML model can predict which problem should be solved.
| I'd like to see a conversational interface that combines these
| two systems, ML generates logic statements and observes the
| results, repeat. That might help to keep it from going off the
| rails like a long GPT-3 session tends to do.
| amelius wrote:
| > Acquiring trustworthy labeled data is "THE" problem in ML,
| and figuring out which predicates to string together is "THE"
| problem in logic programming, seems like a perfect match.
|
| Can't this be generalized into using the ML to prune a search
| tree, and using the logic to generate the search tree? And
| didn't we already successfully try this, see e.g. AlphaGo?
| nextos wrote:
| This is already starting to happen, albeit quite slowly. I
| think it will gain a lot of momentum and it will lead to very
| interesting progress in AI.
|
| For example, deep functions + probabilistic models yield
| things such as deep markov models, which are interpretable
| and can represent really complex distributions such as music.
|
| Deep functions can also be used during sampling to generate
| sophisticated proposals in problems where standard algorithms
| struggle to navigate the posterior.
|
| There are also equivalent ideas being explored in RL, such as
| the OP.
| westurner wrote:
| AutoML is RL? The entire exercise of publishing and peer
| review is an exercise in cybernetics?
|
| https://en.wikipedia.org/wiki/Probabilistic_logic_network :
|
| > _The basic goal of PLN is to provide reasonably accurate
| probabilistic inference in a way that is compatible with both
| term logic and predicate logic, and scales up to operate in
| real time on large dynamic knowledge bases._
|
| > _The goal underlying the theoretical development of PLN has
| been the creation of practical software systems carrying out
| complex, useful inferences based on uncertain knowledge and
| drawing uncertain conclusions. PLN has been designed to allow
| basic probabilistic inference to interact with other kinds of
| inference such as intensional inference, fuzzy inference, and
| higher-order inference using quantifiers, variables, and
| combinators, and be a more convenient approach than Bayesian
| networks (or other conventional approaches) for the purpose
| of interfacing basic probabilistic inference with these other
| sorts of inference. In addition, the inference rules are
| formulated in such a way as to avoid the paradoxes of
| Dempster-Shafer theory._
|
| Has anybody already taught / reinforced an OpenCog [PLN,
| MOSES] AtomSpace hypergraph agent to do Linked Data prep and
| also _convex optimization_ with AutoML and better than grid
| search so gradients?
|
| Perhaps teaching users to bias analyses with e.g. Yellowbrick
| and the sklearn APIs would be a good curriculum traversal?
|
| opening/baselines "Logging and vizualizing learning curves
| and other training metrics"
| https://github.com/openai/baselines#logging-and-
| vizualizing-...
|
| https://en.wikipedia.org/wiki/AlphaZero
|
| There's probably an awesome-automl by now? Again, the sklearn
| interfaces.
|
| TIL that SymPy supports NumPy, PyTorch, and TensorFlow
| [Quantum; TFQ?]; and with a Computer Algebra System something
| for mutating the AST may not be necessary for symbolic
| expression trees without human-readable comments or symbol
| names? Lean mathlib: https://github.com/leanprover-
| community/mathlib , and then reasoning about concurrent /
| distributed systems (with side channels in actual physical
| component space) with e.g. TLA+.
|
| There are new UUID formats that are timestamp-sortable; for
| when blockchain cryptographic hashes aren't enough entropy.
| "New UUID Formats - IETF Draft"
| https://news.ycombinator.com/item?id=28088213
|
| ... You can host online ML algos through SingularityNet,
| which also does PayPal now for the RL.
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