259,259 research outputs found
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)
Balancing generalization and lexical conservatism : an artificial language study with child learners
Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Such learning has been considered problematic for theories of acquisition: if learners generalize abstract patterns to new words, how do they learn lexically-based exceptions? One approach claims that learners use distributional statistics to make inferences about when generalization is appropriate, a hypothesis which has recently received support from Artificial Language Learning experiments with adult learners (Wonnacott, Newport, & Tanenhaus, 2008). Since adult and child language learning may be different (Hudson Kam & Newport, 2005), it is essential to extend these results to child learners. In the current work, four groups of children (6 years) were each exposed to one of four semi-artificial languages. The results demonstrate that children are sensitive to linguistic distributions at and above the level of particular lexical items, and that these statistics influence the balance between generalization and lexical conservatism. The data are in line with an approach which models generalization as rational inference and in particular with the predictions of the domain general hierarchical Bayesian model developed in Kemp, Perfors & Tenenbaum, 2006. This suggests that such models have relevance for theories of language acquisition
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Weight Priors for Learning Identity Relations
Learning abstract and systematic relations has been an open issue in neural network learning for over 30 years. It has been shown recently that neural networks do not learn relations based on identity and are unable to generalize well to unseen data. The Relation Based Pattern (RBP) approach has been proposed as a solution for this problem. In this work, we extend RBP by realizing it as a Bayesian prior on network weights to model the identity relations. This weight prior leads to a modified regularization term in otherwise standard network learning. In our experiments, we show that the Bayesian weight priors lead to perfect generalization when learning identity based relations and do not impede general neural network learning. We believe that the approach of creating an inductive bias with weight priors can be extended easily to other forms of relations and will be beneficial for many other learning tasks
Revisiting peak shift on an artificial dimension: Effects of stimulus variability on generalization
This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this record.One of Mackintoshâs many contributions to the comparative psychology of
associative learning was in developing the distinction between the mental processes
responsible for learning about features and learning about relations. His research on
discrimination learning and generalization served to highlight differences and
commonalities in learning mechanisms across species and paradigms. In one such
example, Wills and Mackintosh (1998) trained both pigeons and humans to discriminate
between two categories of complex patterns comprising overlapping sets of abstract
visual features. They demonstrated that pigeons and humans produced similar âpeakshiftedâ
generalization gradients when the proportion of shared features was systemically
varied across a set of transfer stimuli, providing support for an elemental feature-based
analysis of discrimination and generalization. Here we report a series of experiments
inspired by this work, investigating the processes involved in post-discrimination
generalization in human category learning. We investigate how post-discrimination
generalization is affected by variability in the spatial arrangement and probability of
occurrence of the visual features, and develop an associative learning model that builds
on Mackintoshâs theoretical approach to elemental associative learning
Logic-Based Analogical Reasoning and Learning
Analogy-making is at the core of human intelligence and creativity with
applications to such diverse tasks as commonsense reasoning, learning, language
acquisition, and story telling. This paper contributes to the foundations of
artificial general intelligence by developing an abstract algebraic framework
for logic-based analogical reasoning and learning in the setting of logic
programming. The main idea is to define analogy in terms of modularity and to
derive abstract forms of concrete programs from a `known' source domain which
can then be instantiated in an `unknown' target domain to obtain analogous
programs. To this end, we introduce algebraic operations for syntactic program
composition and concatenation and illustrate, by giving numerous examples, that
programs have nice decompositions. Moreover, we show how composition gives rise
to a qualitative notion of syntactic program similarity. We then argue that
reasoning and learning by analogy is the task of solving analogical proportions
between logic programs. Interestingly, our work suggests a close relationship
between modularity, generalization, and analogy which we believe should be
explored further in the future. In a broader sense, this paper is a first step
towards an algebraic and mainly syntactic theory of logic-based analogical
reasoning and learning in knowledge representation and reasoning systems, with
potential applications to fundamental AI-problems like commonsense reasoning
and computational learning and creativity
Constraining generalisation in artificial language learning : children are rational too
Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Examples occur throughout language. For example, English native speakers know that certain noun-adjective combinations are impermissible (e.g. strong winds, high winds, strong breezes, *high breezes). Another example is the restrictions imposed by verb subcategorization, (e.g. I gave/sent/threw the ball to him; I gave/sent/threw him the ball; donated/carried/pushed the ball to him; * I donated/carried/pushed him the ball). Such lexical
exceptions have been considered problematic for acquisition: if learners generalize abstract patterns
to new words, how do they learn that certain specific combinations are restricted? (Baker, 1979).
Certain researchers have proposed domain-specific procedures (e.g. Pinker, 1989 resolves verb subcategorization in terms of subtle semantic distinctions). An alternative approach is that learners are
sensitive to distributional statistics and use this information to make inferences about when
generalization is appropriate (Braine, 1971).
A series of Artificial Language Learning experiments have demonstrated that adult learners can utilize
statistical information in a rational manner when determining constraints on verb argument-structure
generalization (Wonnacott, Newport & Tanenhaus, 2008). The current work extends these findings to
children in a different linguistic domain (learning relationships between nouns and particles). We also
demonstrate computationally that these results are consistent with the predictions of domain-general
hierarchical Bayesian model (cf. Kemp, Perfors & Tenebaum, 2007)
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