From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)

This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness

Expressive Stream Reasoning with Laser

An increasing number of use cases require a timely extraction of non-trivial knowledge from semantically annotated data streams, especially on the Web and for the Internet of Things (IoT). Often, this extraction requires expressive reasoning, which is challenging to compute on large streams. We propose Laser, a new reasoner that supports a pragmatic, non-trivial fragment of the logic LARS which extends Answer Set Programming (ASP) for streams. At its core, Laser implements a novel evaluation procedure which annotates formulae to avoid the re-computation of duplicates at multiple time points. This procedure, combined with a judicious implementation of the LARS operators, is responsible for significantly better runtimes than the ones of other state-of-the-art systems like C-SPARQL and CQELS, or an implementation of LARS which runs on the ASP solver Clingo. This enables the application of expressive logic-based reasoning to large streams and opens the door to a wider range of stream reasoning use cases.Comment: 19 pages, 5 figures. Extended version of accepted paper at ISWC 201

Creating awareness of kinaesthetic learning using the Experience API: current practices, emerging challenges, possible solutions

We describe our use of the Experience API in preparing blue-collar workers for three frequently arising work contexts, including, for example, the requirement to perform maintenance tasks exactly as specified, consistently, quickly, and without error. We provide some theoretical underpinning for modifying and updating the API to remain useful in near-future training scenarios, such as having a shorter time allowed for kinaesthetic learning experiences than in traditional apprenticeships or training. We propose ways to involve a wide range of stakeholders in appraising the API and ensuring that any enhancements to it, or add-ons, are useful, feasible and compatible with current TEL practices and tools, such as learning-design modelling languages