Abstract: Probabilistic graphical models, and in particular Bayesian networks, are nowadays well established as a modeling tool for domains with
uncertainty. In the SHELL outreach project, we have build a Bayesian network model for petrophysical decision support: the system estimates mineral composition based on borehole estimates. The system uses advanced hybrid Monte Carlo methods for inference. Unfortunately, we cannot disclose the system for Shell. Therefore, to demonstrate the method we have built a demonstrator for similar kind of inference in a toy-domain. What is the chemical composition of wine, given taste observations?
Note that this is a toy model for demonstration purposes. The model does not pretend to be realistic in any way.
Abstract: Vigilance concerns the basic human capacity for information processing and is therefore essential to any form of human cognition. Both physical and mental effort are thought to affect vigilance. Mental effort is known for its vigilance declining effects, but the effects of physical effort are less clear. This study investigated whether these two forms of effort affect the EEG (Electro-EncephaloGram; measure of brain activity) and subjective alertness differently. Participants performed a physical task and were subsequently presented with a mental task, or vice versa. Mental effort decreased subjective alertness and increased theta power (i.e. waves with low frequency) in the EEG. Both results suggest a vigilance decline. Physical effort, however, increased subjective alertness and alpha and beta1 power in the EEG. These findings point towards an increase in vigilance. Beta2 power was reduced after physical effort, which may reflect a decrease in active cognitive processing. No transfer effects were found between the effort conditions, suggesting that the effects of mental and physical effort are distinct. It is concluded that mental effort decreases vigilance, whereas physical effort increases vigilance without improving subsequent task performance.
Abstract: Advances in network technologies enable distributed systems, operating in complex physical environments, to co-ordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public safety. In these systems humans and machines will, in close interaction, be adaptive to a changing environment. Various architecture models are proposed for such Networked Adaptive Interactive Hybrid Systems (NAIHS) from different research areas like (networked) sensor fusion, command and control, artificial intelligence, robotics and human machine interaction. In this paper an architecture model is proposed that seeks to combine their merits. The NAIHS model focuses on the ‘hybrid mind’ that is layered in several dimensions
defining specific functional components and their
interactions. Subsequently, the interaction between the human and artificial part of the system is discussed.
Abstract: Gas distribution models can provide comprehensive information about a large
number of gas concentration measurements, highlighting, for example, areas of unusual
gas accumulation. They can also help to locate gas sources and to plan where
future measurements should be carried out. Current physical modeling methods,
however, are computationally expensive and not applicable for real world scenarios
with real-time and high resolution demands. This chapter reviews kernel methods
that statistically model gas distribution. Gas measurements are treated as random
variables, and the gas distribution is predicted at unseen locations either using a
kernel density estimation or a kernel regression approach. The resulting statistical apmodels
do not make strong assumptions about the functional form of the gas distribution,
such as the number or locations of gas sources, for example. The major
focus of this chapter is on two-dimensional models that provide estimates for the
means and predictive variances of the distribution. Furthermore, three extensions
to the presented kernel density estimation algorithm are described, which allow to
include wind information, to extend the model to three dimensions, and to reflect
time-dependent changes of the random process that generates the gas distribution
measurements. All methods are discussed based on experimental validation using
real sensor data.
Abstract: In this paper we discuss how the design of an Intelligent Companion constitutes a challenge and a test-bed for computer-based technologies aimed at improving the user's cognitive abilities. We conceive an Intelligent Companion to be an autonomous cognitive system (ACS) that should be capable of naturally interacting and communicating in real-world environments. It should do so by embodying (reinforcement) learning of physically grounded conceptualizations of multimodal perception, decision making, planning and actuation, with the aim of supporting human cognition in both an intelligent and intelligible way.
Abstract: Research suggests opposing alertness effects of mental effort and physical effort: mental effort seems to decrease and physical effort appears to increase subjective alertness. There are indications, however, that physical exercise also leads to feelings of lowered alertness. The well-known multidimensional Thayer alertness scale does not seem to assess physical alertness properly. New items were added to the original scale; these were expected to form a physical factor of subjective alertness. In part 1 of this study, participants filled in the revised Thayer scale before and after a control condition and conditions of physical and mental exercise. Physical exercise only increased feelings of physical fatigue, not of alertness. Mental effort increased feelings of sleepiness. In part 2, a Factor analysis was performed on a larger data set in order to validate the use of a separate physical factor. Indeed, a separate physical factor was found. Besides this physical factor, the analysis revealed the factors “sleepiness”, “calmness”, and “tension”, which have originally been described by Thayer. In conclusion, physical alertness is different from mental alertness. Therefore, an explicit physical factor is required in subjective alertness scales.
Abstract: Urban Search and Rescue is a growing area of robotic research. The RoboCup Federation has recognized this, and has created the new Virtual Robots competition to complement its existing physical robot and agent competitions. In order to successfully compete in this competition, teams need to field multi-robot solutions that cooperatively explore and map an environment while searching for victims. This paper presents the results of the first annual RoboCup Rescue Virtual competition. It provides details on the metrics used to judge the contestants as well as summaries of the algorithms used by the top four teams. This allows readers to compare and contrast these effective approaches. Furthermore, the simulation engine itself is examined and real-world validation results on the engine and algorithms are offered.