[HN Gopher] Interpretable Model-Based Hierarchical RL Using Indu... ___________________________________________________________________ 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. ___________________________________________________________________ (page generated 2021-09-11 23:01 UTC)