10 research outputs found
Solving Tree Problems with Category Theory
Artificial Intelligence (AI) has long pursued models, theories, and
techniques to imbue machines with human-like general intelligence. Yet even the
currently predominant data-driven approaches in AI seem to be lacking humans'
unique ability to solve wide ranges of problems. This situation begs the
question of the existence of principles that underlie general problem-solving
capabilities. We approach this question through the mathematical formulation of
analogies across different problems and solutions. We focus in particular on
problems that could be represented as tree-like structures. Most importantly,
we adopt a category-theoretic approach in formalising tree problems as
categories, and in proving the existence of equivalences across apparently
unrelated problem domains. We prove the existence of a functor between the
category of tree problems and the category of solutions. We also provide a
weaker version of the functor by quantifying equivalences of problem categories
using a metric on tree problems.Comment: 10 pages, 4 figures, International Conference on Artificial General
Intelligence (AGI) 201
Categorial Compositionality: A Category Theory Explanation for the Systematicity of Human Cognition
Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric) theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes). In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe—replaced by the relationships between the maps that transform them