Abstract: This paper appeared at the ISCRAM 2010  and presents an intelligentsystem facilitating better in-
formed decision making under severe uncertainty, as often found in emergency management. The
construction of decision-relevant scenarios, being plausible descriptions of a situation and its future
development, is used as a rationale for collecting, organizing, filtering and processing information for
decision making. The development of scenarios is geared to assessing decision alternatives, thus avoiding
time-consuming analysis and processing of irrelevant information.
The scenarios are constructed in a distributed setting allowing for a flexible adaptation of reasoning (prin-
ciples and processes) to the problem at hand and the information available. Each decision can be founded
on a coherent set of scenarios, which was constructed using the best expertise available within a limited
timeframe. Our theoretical framework is demonstrated in a distributed decision support system by
orchestrating both automated systems and human experts into workflows tailored to each specific problem.
Abstract: Probabilistic graphical models, and in particular Bayesian networks, are nowa-
days well established as a modeling tool for domains with uncertainty. The capability of dealing with uncertainty is essential for any intelligentsystem. With BayesLib, we provide a JAVA library for creating, editing and querying Bayesian networks in software applications. Comparing to our previous Bayesian-net libraries, one important difference is that it is now implemented in Java. Therefore, it can be run on any platform with an installed Java Runtime Environment (jre) without recompilation. It has an internal xml parser to parse xml formatted files. This XML format is based on Microsoft Research’s XBN (for "Bayesian network in XML") standard format, and therefore facilitates communication with other systems. Thus, with this software, the Bayesian net can now be easily embedded in an Enterprise or web application stacks, and can be used in a multi user environment.
Abstract: In this paper, two different methods for information fusion are compared with respect to communication cost. These are the lambda-pi and the junction-tree approach as the probability computing methods in Bayesian networks. The analysis is done within the scope of large distributed networks of computing nodes. The result of this comparison enables us to make astatement about the most appropriate method for reasoning in distributed Bayesian networks. Each node in the network is considered an intelligent agent in a multi-agentsystem.
Abstract: The proliferation of small mobile devices and wireless networks has resulted in an increasing demand to support the applications found in wired environments on mobile devices. In real time replication systems, such as collaborative systems, this trend gives some new problems to address. The properties of wireless networks are low bandwidth and high latency, which change dynamically over time. The risk of the network getting congested is therefore high with the result that the user will not receive the important information in time. Consequently there is a need to develop algorithms and methods for adaptive work environments and adaptive data distribution, to minimise the traffic load. An architecture based on multi- and mobile-agents is proposed as a solution. Personalized behaviour is included in a flexible and extensible system. A prototype of the architecture has been implemented in a crisis environment and was used for an evaluation. It is assumed that each individual in the field is equipped with a PDA that can communicate with other PDA's in the surrounding and remote servers. Users can report about their environment using a personalized iconic language. Each user is supervised by a personal agent. In case of emergency users are routed outside a dangerous area using a personalised dynamic routing system, called PIRA "Personal Intelligent Routing Assistant". The system and results of testing will be presented in this paper.
Abstract: In the near future, intelligent agents on mobile devices will push to as well as request location-dependent information from users at convenient and
inconvenient times. In this paper, we consider the negative effects of mobile agent interruption and present strategies to reduce these effects drawn from
social psychology and task-interruption literature. We propose the implementation of social behaviours to minimize the negative effects of (task) interruptions caused by mobile agents and report the results of two studies that evaluate two social behaviours agents can adopt. The results from these studies
indicate that a mobile agent adopting social system behaviour can lead to a less disruptive user experience.
Abstract: Conducting empirical research involves a balancing act between scientific rigor and real-life pragmatics. The Delft Co-operation on IntelligentSystems (D-CIS) laboratory researches systems-of-systems consisting of the human and artificial systems involved in collaborative decision-making under chaotic circumstances. An important objective is the usefulness of our results in our major application domain: crisis management. The D-CIS lab was involved in setting up a crisis management exercise experiment and the according measurements regarding an improvement in internal communication at Gemeente (Municipality) Borsele. In this paper, the empirical research regarding this experiment, the methodology and its results are briefly outlined. The main lessons learned concern the interrelationship between the scenario, experiment and measurements, the problem of acquiring usable data and the challenges of conducting grounded research.
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.