Abstract: Computer-Assisted Instruction systems (CAI) enable fully automated simulator-based training. Traditionally, a CAI system does not enable a true dialogue between the learner and the virtual instructor. Most frequently, the system acts like a human expert, and authoritatively provides feedback and ways to improve the task performance. In this conference paper, we describe an educational agent that enables a dialogue between the learner and the agent. The agent is called the companion agent. It acts like a virtual co-learner, for example by deliberating about new operational measures after a situation-change. The agent operates on the same authority level as the learner, and is therefore less threatening than a traditional virtual instructor. We believe companion agents are typically useful in modern, constructive learning situations where learners can take control of their own learning process. Potential applications of companion agents lie within the civil area (for example a civil tunnel operator during tunnel surveillance training) and the military area (for example embedded training in tactical surveillance).
This paper was selected as one of the Continuing Education Unit (CEU) papers for the 2007 Interservice/Industry Training, Simulation and Education Conference (I/ITSEC). The I/ITSEC board states that only those papers that demonstrate exceptional innovation, research, experimentation, and documentation in an area of new technology are selected for CEU credit.
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: This paper presents a distributed system facilitating robust decision-making under uncertainty in complex situations often found in strategic emergency management. The construction of decision-relevant scenarios, each being a consistent, coherent and plausible description of a situation and its future development, is used as a rationale for collecting, organizing, filtering and processing information for decision-making. The construction of scenarios is targeted at assessing decision alternatives avoiding time-consuming processing of irrelevant information. The scenarios are constructed in a distributed setting ensuring that each decision can be founded on a coherent and consistent set of assessments and assumptions provided by the best (human or artificial) experts available within limited time. Our theoretical framework is illustrated by means of an emergency management example.
Abstract: Bayesian network modeling by domain experts is still mainly a process of trial and error. The structure of the graph and the specification of the conditional probability tables (CPTs) are in practice often fiddled until a desired model behavior is obtained. We describe a development tool in which graph specification and CPT modeling are fully separated. Furthermore, the tuning of CPTs is handled automatically. The development tool consists of a database in which the graph description and the desired probabilistic behavior of the network are separately stored. From this database, the graph is constructed and the CPTs are numerically optimized in order to minimize the error between desired and actual behavior. The tool may be helpful in both development and maintenance of probabilistic expertsystems. A demo is provided. A numerical example illustrates the methodology.
Abstract: This demo presents an ICT system for collaborative situation assessment and strategic decision making that supports effective and efficient protection of the population and the environment against chemical hazards in industrial areas. Robust decision support taking into account multiple objectives entails the combination of Multi-Criteria Decision Analysis (MCDA) and Scenario-Based Reasoning (SBR). The ad-hoc formed workflow of (human and artificial) experts generates scenarios capturing uncertainties. Combining MCDA and SBR allows for structuring complex problems and accounting for uncertainties by the selection of a decision alternative that performs (sufficiently) well for various aims under a variety of different possible situation developments (i.e., scenarios).
Abstract: Decision making under uncertainty is fraught with
pitfalls for human thinking: biases prevail. The combination of
a scenario-based approach with multi-criteria decision analysis
assists in making value judgements, trade-offs and uncertainties
explicit. Scenarios, which are constructed in a distributedmanner
involving multiple experts from different domains, assist in over-coming e.g. the prominence effect and confirmation bias. Further-more, support is provided to handle the uncertainty associated
with each scenario without imposing unjustified assumptions
on each piece of information. We develop a relative reliability
concept,which differs from standard probability assessments as
it is sensitive to the context, such as the decision problem at hand,
the decision makersí requirements and the available information.
This approach maintains the flexibility of the distributed system
by allowing the experts to adapt the information they provide and
the likelihood assessments thereof to the situation. Our approach
is illustrated by an emergency management exampl
Abstract: This paper illustrates objectives of the DIADEM project which focuses on a
novel combination of advanced technologies which facilitate collaborative information
processing in environmental management applications. The emphasis is on the principles
and tools which facilitate creation of a single information space in advanced systems of
systems through a systematic integration of heterogeneous processes. In particular, we
illustrate the main principles of the DIADEM Process Integration framework, which
supports collaborative processing based on a combination of automated reasoning
processes and cognitive capabilities of multiple human experts, each contributing specific
expertise and processing resources.