Implicit and explicit learning in ACT-R

A useful way to explain the notions of implicit and explicit learning in ACT-R is to define implicit learning as learning by ACT-R's learning mechanisms, and explicit learning as the results of learning goals. This idea complies with the usual notion of implicit learning as unconscious and always active and explicit learning as intentional and conscious. Two models will be discussed to illustrate this point. First a model of a classical implicit memory task, the SUGARFACTORY scenario by Berry & Broadbent (1984) will be discussed, to show how ACT-R can model implicit learning. The second model is of the so-called Fincham task (Anderson & Fincham, 1994), and exhibits both implicit and explicit learning

Spontaneous Analogy by Piggybacking on a Perceptual System

Most computational models of analogy assume they are given a delineated source domain and often a specified target domain. These systems do not address how analogs can be isolated from large domains and spontaneously retrieved from long-term memory, a process we call spontaneous analogy. We present a system that represents relational structures as feature bags. Using this representation, our system leverages perceptual algorithms to automatically create an ontology of relational structures and to efficiently retrieve analogs for new relational structures from long-term memory. We provide a demonstration of our approach that takes a set of unsegmented stories, constructs an ontology of analogical schemas (corresponding to plot devices), and uses this ontology to efficiently find analogs within new stories, yielding significant time-savings over linear analog retrieval at a small accuracy cost.Comment: Proceedings of the 35th Meeting of the Cognitive Science Society, 201

Automated Retrieval of Non-Engineering Domain Solutions to Engineering Problems

Organised by: Cranfield UniversityBiological inspiration for engineering design has occurred through a variety of techniques such as creation and use of databases, keyword searches of biological information in natural-language format, prior knowledge of biology, and chance observations of nature. This research focuses on utilizing the reconciled Functional Basis function and flow terms to identify suitable biological inspiration for function based design. The organized search provides two levels of results: (1) associated with verb function only and (2) narrowed results associated with verb-noun (function-flow). A set of heuristics has been complied to promote efficient searching using this technique. An example for creating smart flooring is also presented and discussed.Mori Seiki – The Machine Tool Compan

Distributed Information Retrieval using Keyword Auctions

This report motivates the need for large-scale distributed approaches to information retrieval, and proposes solutions based on keyword auctions

Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus

Similarity of Semantic Relations

There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM