22 research outputs found
Dr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept Statistics
The symbolic AI community is increasingly trying to embrace machine learning
in neuro-symbolic architectures, yet is still struggling due to cultural
barriers. To break the barrier, this rather opinionated personal memo attempts
to explain and rectify the conventions in Statistics, Machine Learning, and
Deep Learning from the viewpoint of outsiders. It provides a step-by-step
protocol for designing a machine learning system that satisfies a minimum
theoretical guarantee necessary for being taken seriously by the symbolic AI
community, i.e., it discusses "in what condition we can stop worrying and
accept statistical machine learning." Unlike most textbooks which are written
for students trying to specialize in Stat/ML/DL and willing to accept jargons,
this memo is written for experienced symbolic researchers that hear a lot of
buzz but are still uncertain and skeptical. Information on Stat/ML/DL is
currently too scattered or too noisy to invest in. This memo prioritizes
compactness, citations to old papers (many in early 20th century), and concepts
that resonate well with symbolic paradigms in order to offer time savings. It
prioritizes general mathematical modeling and does not discuss any specific
function approximator, such as neural networks (NNs), SVMs, decision trees,
etc. Finally, it is open to corrections. Consider this memo as something
similar to a blog post taking the form of a paper on Arxiv.Comment: 12 pages of main contents, 29 pages in total. It could also serve as
an accompanying material for Latplan paper. (arXiv:2107.00110) v2: rewrote
the general ELBO derivation without Prolog. v3: significantly extended the
Bayesian reasoning section in the appendix, with several proofs for conjugate
priors. v4+: errata fi
Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)
We achieved a new milestone in the difficult task of enabling agents to learn
about their environment autonomously. Our neuro-symbolic architecture is
trained end-to-end to produce a succinct and effective discrete state
transition model from images alone. Our target representation (the Planning
Domain Definition Language) is already in a form that off-the-shelf solvers can
consume, and opens the door to the rich array of modern heuristic search
capabilities. We demonstrate how the sophisticated innate prior we place on the
learning process significantly reduces the complexity of the learned
representation, and reveals a connection to the graph-theoretic notion of
"cube-like graphs", thus opening the door to a deeper understanding of the
ideal properties for learned symbolic representations. We show that the
powerful domain-independent heuristics allow our system to solve visual
15-Puzzle instances which are beyond the reach of blind search, without
resorting to the Reinforcement Learning approach that requires a huge amount of
training on the domain-dependent reward information.Comment: Accepted in IJCAI 2020 main track (accept ratio 12.6%). The prequel
of this paper, "The Search for STRIPS", can be found here: arXiv:1912.05492 .
(update, 2020/08/11) We expanded the related work sectio
Deep Neuroevolution of Recurrent and Discrete World Models
Neural architectures inspired by our own human cognitive system, such as the
recently introduced world models, have been shown to outperform traditional
deep reinforcement learning (RL) methods in a variety of different domains.
Instead of the relatively simple architectures employed in most RL experiments,
world models rely on multiple different neural components that are responsible
for visual information processing, memory, and decision-making. However, so far
the components of these models have to be trained separately and through a
variety of specialized training methods. This paper demonstrates the surprising
finding that models with the same precise parts can be instead efficiently
trained end-to-end through a genetic algorithm (GA), reaching a comparable
performance to the original world model by solving a challenging car racing
task. An analysis of the evolved visual and memory system indicates that they
include a similar effective representation to the system trained through
gradient descent. Additionally, in contrast to gradient descent methods that
struggle with discrete variables, GAs also work directly with such
representations, opening up opportunities for classical planning in latent
space. This paper adds additional evidence on the effectiveness of deep
neuroevolution for tasks that require the intricate orchestration of multiple
components in complex heterogeneous architectures