Abstract: Given unexpected incidents on routes of guards that check security objects, like banks, one of the most challenging problems is still how to support improvisation by security personnel in taking decisions to prevent or resolve such incidents. Another as important associated problem is how a security company can naturally take advantage of its existing and novel knowledge about its organizational and ICT infrastructures, and the introduction of a decisionsupport system to help leverage of improvisation by humans. To tackle all this, on the one hand we present a dynamic coalition formation framework that allows the (re)configurations of agents that are associated with joint tasks in situational contexts to be evaluated by appropriate value functions. On the other hand, we present a dynamic scale-space paradigm that allows a security company to distill ranked lists of robust context-dependent reconfigurations at critical scales. We highlight the merits of ASK-ASSIST as a solution to the problem of supporting human improvisation.
Abstract: In crisis situations, decision-making capabilities rely on reports from all parties involved. For achieving the necessary capabilities of crisis technology, a communication-interface prototype representing concepts and ideas has been developed. To support language-independent communication and to reduce the ambiguity and multitude of semantic interpretation of human observers reports, the messages are constructed using a spatial arrangement of visual symbols. We developed a dedicated grammar to interpret and convert the visual language messages to (natural language) text and speech. The communication interface also provides an icon prediction to have faster interaction in next icon selections. The system processes the incoming messages to build a world model by the employment of ontology. A blackboard structure in a Mobile Ad-Hoc Network is used to share and distribute information. We deployed our visual language interface in a serious game environment of a disaster and rescue simulator. The current implementation of this environment is capable of simulating real disaster situations using information from human user observers reports.
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 decisionsupport system by
orchestrating both automated systems and human experts into workflows tailored to each specific problem.
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: Emergency situations occur unpredictably and cause individuals and organizations to shift their focus and attention immediately to dealing with the situation. When disasters become large in scale all the limitations resulting from a lack of integration and collaboration among all the involved organizations begin to expose themselves and further compound the negative consequences of the event. Often in large scale disasters the people who must work together have no history of doing so, they have not developed a trust or understanding of one another’s abilities, and the totality of resources they each bring to bear were never before exercised. As a result, the challenges for individual or group decisionsupport systems (DSS) in emergency situations are diverse and immense. In this chapter, we present recent advances in this area and highlight important remaining challenges.
Abstract: Emergency managers need to assess, combine and process large volumes of information with varying degrees of
(un)certainty. To keep track of the uncertainties and to facilitate gaining an understanding of the situation, the
information is combined into scenarios: stories about the situation and its development. As the situation evolves,
typically more information becomes available and already acknowledged information is changed or revised.
Meanwhile, decision-makers need to keep track of the scenarios including an assessment whether the infor-mation constituting the scenario is still valid and relevant for their purposes. Standard techniques to support sce-nario updating usually involve complete scenario re-construction. This is far too time-consuming in emergency
management. Our approach uses a graph theoretical scenario formalisation to enable efficient scenario updating.
MCDA techniques are employed to decide whether information changes are sufficiently important to warrant
scenario updating. A brief analysis of the use-case demonstrates a large gain in efficiency.
Abstract: French coastguard missions have become increasingly varied implying new challenges such as the reduction of the decision cycle and the expansion of available information. Thus, it involves new needs for enhanced decisionsupport. An efficient situation awareness system has to quickly detect and identify suspicious boats. The efficiency of such a system relies on a reliable sensor fusion since a coastguard uses sensors to achieve his mission. We present an innovative approach based on multi-agent negotiation to fuse classifiers, benefiting from the efficiency of existing classification tools and from the flexibility and reliability of a multi-agent system to exploit distributed data across dispersed sources. We developed a first prototype using a basic negotiation protocol in order to validate the feasibility and the interest of our approach. The results obtained are promising and encourage us to continue on this way.
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 decisionsupport: 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: Multi-criteria decision analysis (MCDA) is a technique for decisionsupport which aims at providing transparent and coherent support for complex decision situations taking into account subjective preferences of the decision makers. However, MCDA does not foresee an analysis of multiple plausible future developments of a given situation. In contrast, scenario-based reasoning (SBR) is frequently used to assess future developments on the longer term. The ability to discuss multiple plausible future developments provides a rationale for strategic plans and actions. Nevertheless, SBR lacks an in-depth performance evaluation of the considered actions.
This paper explores the integration of both techniques that combines their respective strengths as well as their application in environmental crisis management. The proposed methodology is illustrated by an environmental incident example. Future work is to conduct validations on the basis of real-world scenarios by public Dutch and Danish chemical incident crisis management authorities.
Abstract: The increasing complexity of our world demands new perspectives on the role of technology in human decision making. We need new technology to cope with the increasingly complex and information-rich nature of our modern society. This is particularly true for critical environments such as crisis management and traffic management, where humans need to engage in close collaborations with artificial systems to observe and understand the situation and respond in a sensible way. The book Interactive Collaborative Information Systems addresses techniques that support humans in situations in which complex information handling is required and that facilitate distributed decision-making. The theme integrates research from information technology, artificial intelligence and human sciences to obtain a multidisciplinary foundation from which innovative actor-agent systems for critical environments can emerge. It emphasizes the importance of building actor-agent communities: close collaborations between human and artificial actors that highlight their complementary capabilities in situations where task distribution is flexible and adaptive. This book focuses on the employment of innovative agent technology, advanced machine learning techniques, and cognition-based interface technology for the use in collaborative decisionsupport systems.
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 decisionsupport 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 makers’ 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: Humans primarily assess situations and plan for actions by (implicit) scenario-based analysis. Decision making in crisis management situations in The Netherlands unfortunately does not explicitly feature scenario-based analysis; not within teams nor between teams. In this article we formulate conditions for successful application of scenario-based analysis, based on our experiences in crisis management and crisis management exercises. The conditions are formulated and briefly assessed in a number of cases. An important implication for information systems support is identified and future research is announced.
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 introduces the MultiAgent Decision Process software toolbox, an open source C++ library for decision-theoretic planning under uncertainty in multiagent systems. It provides support for several multiagent models, such as POSGs, Dec-POMDPs and MMDPs. The toolbox aims to reduce development time for planning algorithms and to provide a benchmarking platform by providing a number of commonly used problem descriptions. It features a parser for a text-based ﬁle format for discrete Dec-POMDPs, shared functionality for planning algorithms, as well as the implementation of several Dec-POMDP planners. We describe design goals and architecture of the toolbox, and provide an overview of its functionality, illustrated by some usage examples. Finally we report on current and future work.
Abstract: Collaboration environments impose high demands on humans and
artificial systems. Especially during critical tasks team members, including
humans, artificial systems and other (sub-) teams, require support to guarantee
their continued effectiveness. Effectiveness of individuals and teams is an
important ingredient for organizational effectiveness, managerial decision
quality, as well as for maintaining organizational awareness. In this position
paper we introduce our conceptual view on realizing sustained team
effectiveness, in which both the measurement of effectiveness and team
management play an important role. A unified, interdisciplinary approach
facilitates measuring effectiveness in more complex organizations.
Abstract: Collaboration environments impose high demands on humans and artificial systems. Especially during critical tasks team members, including humans, artificial systems and other (sub-) teams, require support to guarantee their continued effectiveness. Effectiveness of individuals and teams is an important ingredient for organizational effectiveness, managerial decision quality, as well as for maintaining organizational awareness. In this position paper we introduce our conceptual view on realizing sustained team effectiveness, in which both the measurement of effectiveness and team management play an important role. A unified, interdisciplinary approach facilitates measuring effectiveness in more complex organizations.