Building models of learning and expertise with CHREST

CHREST (Chunk Hierarchy and REtrieval STructures) is a complete computational architecture implementing processes of learning and perception. CHREST models have successfully simulated human data in a variety of domains, such as the acquisition of syntactic categories, expertise in programming and in chess, concept formation, implicit learning, and the acquisition of multiple representations in physics for problem solving. In this tutorial, we describe the learning, perception and attention mechanisms within CHREST as well as key empirical data captured by CHREST models. Apart from the theoretical material, this tutorial also introduces participants to an implementation of CHREST and its use in a variety of domains. Material and examples are provided so participants can adapt and extend the CHREST architecture

Usefulness of VRML building models in a direction finding context

This paper describes an experiment which aims to examine the effectiveness and efficiency of a Virtual Reality Modelling Language (VRML) building model compared with equivalent architectural plans, for direction finding purposes. The effectiveness and efficiency issues being primarily investigated were number of tasks completed overall and task completion times. The experiment involved a series of tasks where participants had to find a number of locations/objects in a building unknown to them at the outset of the experiment. Statistically significant results are presented for the benefit of the research community, law enforcement officers and fire fighters where it is clear that in this context, the VRML model led to better task completions than the equivalent architectural plans. Regarding the task completion times, no statistical significance was found. Given the current climate of security issues and terrorist threats, it is important that law enforcement officers have at their disposal the best information possible regarding the layout of a building, whilst keeping costs down. This also applies to fire fighters when rescuing victims. This experiment has shown that a VRML model leads to better task completions in direction finding

MCH Capacity-Building Models: A Summary

Table of Contents: Executive Summary Introduction Report Approach and Components Stakeholders in Maternal and Child Health in Nebraska Capacity-Building Approaches Individual Capacity-Building Models Community Capacity-Building Models Statewide Capacity-Building Models Organizational Models Observations Summary Reference

A Single Scalar Field Model of Dark Energy with Equation of State Crossing -1

In this paper we study the possibility of building models of dark energy with equation of state across -1 and propose explicitly a model with a single scalar field which gives rise to an equation of state larger than -1 in the past and less than -1 at the present time, consistent with the current observations.Comment: 4 pages, 1 figure, the version accepted by JCAP, presentation improved and references adde