The effectiveness of integrating educational robotic activities into higher education Computer Science curricula: a case study in a developing country

In this paper, we present a case study to investigate the effects of educational robotics on a formal undergraduate Computer Science education in a developing country. The key contributions of this paper include a longitudinal study design, spanning the whole duration of one taught course, and its focus on continually assessing the effectiveness and the impact of robotic-based exercises. The study assessed the students' motivation, engagement and level of understanding in learning general computer programming. The survey results indicate that there are benefits which can be gained from such activities and educational robotics is a promising tool in developing engaging study curricula. We hope that our experience from this study together with the free materials and data available for download will be beneficial to other practitioners working with educational robotics in different parts of the world

The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain

The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a “router” network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates

A Visually Driven Hippocampal Place Cell Model

this paper we first describe an algorithm that operates on real images taken from various viewing locations and returns "blob" descriptions: regions of roughly uniform intensity having a rectangular or ovoid shape. We then construct simulated place cells using radial basis functions tuned to blob parameters, and train them by competitive learning to develop realistic place fields. The result is a model that takes real-world scenes as input and produces a distributed activity pattern over a set of place cells as output, from which the current viewing location can be estimated with good accuracy. The implication of this work is that the visual pathway to the rodent hippocampus could involve a relatively simple representation of the local view; rodents may not require object recognition to navigate visually

Logic Programs with Context-Dependent Preferences

The paper describes an extension of well-founded semantics for logic programs with two types of negation. In this extension information about preferences between rules can be expressed in the logical language and derived dynamically. This is achieved by using a reserved predicate symbol and a naming technique. Conicts among rules are resolved whenever possible on the basis of derived preference information. A legal reasoning example illustrates the usefulness of the approach

Handling Defaults and Their Exceptions in Controlled Natural Language

Defaults are statements in natural language that generalise over a particular kind of objects or over what a particular kind of objects does. Defaults are very useful in human communication since we often do not have complete information about the world, but we must be able to draw conclusions about what is normally the case. However, these conclusions are only tentative and sometimes we have to withdraw them and revise our theory if new information becomes available. In this paper, we propose the use of a controlled natural language as a high-level specification language for modelling commonsense reasoning problems. We investigate how defaults and exceptions can be incorporated into an existing controlled natural language and what kind of formal machinery is required to represent and reason with them in a non-monotonic way. Our controlled natural language looks completely natural at first glance since it consists of a well-defined subset of English but it is in fact a formal language that is computer-processable and can be translated unambiguously via discourse representation structures into an executable answer set program. Answer set programming is a relatively new logic-based knowledge representation formalism and is well-suited to solve commonsense reasoning problems.18 page(s