Abstract: The tunnel operator monitors and regulates the flow of traffic inside a tunnel, and takes actions in case an incident occurs. TNO has developed a training simulator that enables the operator to train incident situations. We are currently improving the simulator by developing intelligent agents that support a qualified operator, who trains himself, without the presence of a human instructor. This paper provides an overview of research activities in this project. In particular, it describes two types of educational agents, the authoritative instructor agent and the non-authoritative companion agent.
Abstract: When we talk about improving Information Systems for Crisis Response and Management, we must consider at least two sorts of problems to solve: the processing of human provided information (perception and knowledge) in combination with other sensor information and the human processing of sensor based information (for situation awareness and decision making). As an illustration, two examples of research work that is done at the D-CIS lab in Delft is presented here. The first example is about Distributed Perception Networks in disaster management and the second example is about cognitive performance factors in decision making (in crisis situations).
Abstract: The MOSAIC project aims at enhanced situation awareness and reduced information overload to public safety officers (police, fire brigade, medical transport) in a complex safety incident.
In this first MOSAIC indicative experiment, a realistic safety incident, a ship collision with many persons and poisonous gas involved, was simulated by messages on this incident. These messages correspond to usual messages sent to the local police commander at the location of incident.
In the experiment, the original message set (Set-0) was reduced in consecutive ways.
In Set-1 messages on victims and events related to the safety incident were combined.
In Set-3 only information pertaining to operational police tasks remained (all information on the informative police task was removed).
The normal operational background task of the Control and Command Room (CCR) was simulated by a computer game.
The effects of the information reduction on situation awareness and information overload were measured by a questionnaire with questions on the situation, an open evaluation, and background task performance.
Eighteen police officers participated in the experiment. The results showed that removal of messages containing information that was sent before reduces the information overload that is experienced. Aggregating numbers of victims and certain types of events is a most effective way to decrease the number of messages while improving situation awareness. Aggregating geographical data by plotting was, unexpectedly not effective; training beforehand might have improved this.
To conclude, reduction of the number of messages proved to be effective in improving information processing of police officers.
Abstract: Improving our knowledge of and capabilities to handle disasters and crises is not simply a matter of more information processing and more reliable communication and computation. It needs the exchange of information between many different scientific and technology disciplines and a much better understanding of engineering complex C4I systems-of-systems. This discussion paper will address the need for and purpose of an international community and how to obtain focus and transfer of scientific results.
Abstract: We propose a method for improving Belief Propagation (BP) that takes into account the influence of loops in the graphical model. The method is a variation on and generalization of the method recently introduced by Montanari and Rizzo . It consists of two steps: (i) standard BP is used to calculate cavity distributions for each variable (i.e. probability distributions on the Markov blanket of a variable for a modified graphical model, in which the factors involving that variable have been removed); (ii) all cavity distributions are combined by a messagepassing algorithm to obtain consistent single node marginals. The method is exact if the graphical model contains a single loop. The complexity of the method is exponential in the size of the Markov blankets. The results are very accurate in general: the error is often several orders of magnitude smaller than that of standard BP, as illustrated by numerical experiments.
Abstract: We propose a method for improving approximate inference methods that corrects for the influence of loops in the graphical model. The method is applicable to arbitrary factor graphs, provided that the size of the Markov blankets is not too large. It is an alternative implementation of an idea introduced recently by Montanari and Rizzo (2005). In its simplest form, which amounts to the assumption that no loops are present, the method reduces to the minimal Cluster Variation Method approximation (which uses maximal factors as outer clusters). On the other hand, using estimates of the effect of loops (obtained by some approximate inference algorithm) and applying the Loop Correcting (LC) method usually gives significantly better results than applying the approximate inference algorithm directly without loop corrections. Indeed, we often observe that the loop corrected error is approximately the square of the error of the approximate inference method used to estimate the effect of loops. We compare different variants of the Loop Correcting method with other approximate inference methods on a variety of graphical models, including "real world" networks, and conclude that the LC approach generally obtains the most accurate results.
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: 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: This paper describes the optimization of traffic light controllers using a model-based reinforcement learning approach. Traffic lights are optimized using mostly local, low-level information, but some high-level information concerning the general traffic situation at neighboring traffic junctions is take into account, enhancing situation awareness and improving decision making. We show, using experiments performed with a traffic simulator, that this approach outperforms existing methods.