14,008 research outputs found
Learning Logic Programs by Discovering Higher-Order Abstractions
Discovering novel abstractions is important for human-level AI. We introduce
an approach to discover higher-order abstractions, such as map, filter, and
fold. We focus on inductive logic programming, which induces logic programs
from examples and background knowledge. We introduce the higher-order
refactoring problem, where the goal is to compress a logic program by
introducing higher-order abstractions. We implement our approach in STEVIE,
which formulates the higher-order refactoring problem as a constraint
optimisation problem. Our experimental results on multiple domains, including
program synthesis and visual reasoning, show that, compared to no refactoring,
STEVIE can improve predictive accuracies by 27% and reduce learning times by
47%. We also show that STEVIE can discover abstractions that transfer to
different domain
SPIDA: Abstracting and generalizing layout design cases
Abstraction and generalization of layout design cases generate new knowledge that is more widely applicable to use than specific design cases. The abstraction and generalization of design cases into hierarchical levels of abstractions provide the designer with the flexibility to apply any level of abstract and generalized knowledge for a new layout design problem. Existing case-based layout learning (CBLL) systems abstract and generalize cases into single levels of abstractions, but not into a hierarchy. In this paper, we propose a new approach, termed customized viewpoint - spatial (CV-S), which supports the generalization and abstraction of spatial layouts into hierarchies along with a supporting system, SPIDA (SPatial Intelligent Design Assistant)
- …