83,245 research outputs found
A theory of relation learning and cross-domain generalization
People readily generalize knowledge to novel domains and stimuli. We present
a theory, instantiated in a computational model, based on the idea that
cross-domain generalization in humans is a case of analogical inference over
structured (i.e., symbolic) relational representations. The model is an
extension of the LISA and DORA models of relational inference and learning. The
resulting model learns both the content and format (i.e., structure) of
relational representations from non-relational inputs without supervision, when
augmented with the capacity for reinforcement learning, leverages these
representations to learn individual domains, and then generalizes to new
domains on the first exposure (i.e., zero-shot learning) via analogical
inference. We demonstrate the capacity of the model to learn structured
relational representations from a variety of simple visual stimuli, and to
perform cross-domain generalization between video games (Breakout and Pong) and
between several psychological tasks. We demonstrate that the model's trajectory
closely mirrors the trajectory of children as they learn about relations,
accounting for phenomena from the literature on the development of children's
reasoning and analogy making. The model's ability to generalize between domains
demonstrates the flexibility afforded by representing domains in terms of their
underlying relational structure, rather than simply in terms of the statistical
relations between their inputs and outputs.Comment: Includes supplemental materia
Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models
Structure learning is a core problem in AI central to the fields of
neuro-symbolic AI and statistical relational learning. It consists in
automatically learning a logical theory from data. The basis for structure
learning is mining repeating patterns in the data, known as structural motifs.
Finding these patterns reduces the exponential search space and therefore
guides the learning of formulas. Despite the importance of motif learning, it
is still not well understood. We present the first principled approach for
mining structural motifs in lifted graphical models, languages that blend
first-order logic with probabilistic models, which uses a stochastic process to
measure the similarity of entities in the data. Our first contribution is an
algorithm, which depends on two intuitive hyperparameters: one controlling the
uncertainty in the entity similarity measure, and one controlling the softness
of the resulting rules. Our second contribution is a preprocessing step where
we perform hierarchical clustering on the data to reduce the search space to
the most relevant data. Our third contribution is to introduce an O(n ln n) (in
the size of the entities in the data) algorithm for clustering
structurally-related data. We evaluate our approach using standard benchmarks
and show that we outperform state-of-the-art structure learning approaches by
up to 6% in terms of accuracy and up to 80% in terms of runtime.Comment: Submitted to AAAI23. 9 pages. Appendix include
Reasoning about Independence in Probabilistic Models of Relational Data
We extend the theory of d-separation to cases in which data instances are not
independent and identically distributed. We show that applying the rules of
d-separation directly to the structure of probabilistic models of relational
data inaccurately infers conditional independence. We introduce relational
d-separation, a theory for deriving conditional independence facts from
relational models. We provide a new representation, the abstract ground graph,
that enables a sound, complete, and computationally efficient method for
answering d-separation queries about relational models, and we present
empirical results that demonstrate effectiveness.Comment: 61 pages, substantial revisions to formalisms, theory, and related
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Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
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