Abstract: This paper appeared at the ISCRAM 2010  and presents an intelligent system 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: 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: In complex strategic decision-making situations the need for well-structured support arises. To evaluate decision alternatives, information about the situation and its development must be determined, managed and processed by the best available experts. For various types of information different reasoning principles have been developed: deterministic, probabilistic, fuzzy and techniques for reasoning under ignorance (i.e., the likelihood of an event cannot be quantified). We propose a new approach based on Decision Maps supporting decision makers under fundamental uncertainty by generating descriptions of different possible situation developments (scenarios) in a distributed manner. The scenarios are evaluated using Multi-Criteria Decision Analysis techniques.
Abstract: This paper describes the sample implementation of a distributed goaloriented reasoning engine for multi-agent systems. The paper summarizes part of the design and programming issues that we addressed for providing the initial prototype with customization and self-configuration facilities.