Thales Research and Technology Netherlands

Delft, Netherlands

Thales Research and Technology Netherlands

Delft, Netherlands

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Koen H.,South African Council for Scientific and Industrial Research | Koen H.,University of Pretoria | De Villiers J.P.,South African Council for Scientific and Industrial Research | De Villiers J.P.,University of Pretoria | And 4 more authors.
FUSION 2014 - 17th International Conference on Information Fusion | Year: 2014

Rhino poaching in South Africa is leading to a catastrophic reduction in the rhino population. In this paper a Bayesian network causal model is proposed to model the underlying (causal) relationships that lead to rhino poaching events. The model may be used to fuse a collection of heterogeneous information sources. If a game reserve is partitioned into several geographical areas or cells, the model may perform inference for each of these cells separately, and give a relative predictive distribution of poaching events over the game reserve. After an overview of the current problem definition and a brief overview of similar modelling approaches, the Bayesian network model is presented. The developed Bayesian network based model is an initial attempt at proposing a sensible modelling approach for this problem. Some of the complexities of the approach are discussed, before considering how the model may be validated at a later stage. © 2014 International Society of Information Fusion.


Comes T.,Karlsruhe Institute of Technology | Conrado C.,Thales Research and Technology Netherlands | Hiete M.,Karlsruhe Institute of Technology | Kamermans M.,Thales Research and Technology Netherlands | And 3 more authors.
Belgian/Netherlands Artificial Intelligence Conference | Year: 2010

This paper appeared at the ISCRAM 2010 [3] and presents an intelligent system facilitating better informed 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 (principles 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.


Comes T.,Karlsruhe Institute of Technology | Conrado C.,Thales Research and Technology Netherlands | Dalmas T.,Space Application Services | Wijngaards N.,Thales Research and Technology Netherlands | Schultmann F.,Karlsruhe Institute of Technology
Belgian/Netherlands Artificial Intelligence Conference | Year: 2011

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).


Comes T.,Karlsruhe Institute of Technology | Hiete M.,Karlsruhe Institute of Technology | Wijngaards N.,Thales Research and Technology Netherlands | Schultmann F.,Karlsruhe Institute of Technology
Proceedings - 2nd International Conference on Intelligent Networking and Collaborative Systems, INCOS 2010 | Year: 2010

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. © 2010 IEEE.


Pavlin G.,Thales Research and Technology Netherlands | Groen F.,University of Amsterdam | De Oude P.,University of Amsterdam | Kamermans M.,Thales Research and Technology Netherlands
Studies in Computational Intelligence | Year: 2010

This chapter introduces a system for early detection of gaseous substances and coarse source localization by using heterogeneous sensor measurements and human reports. The system is based on Distributed Perception Networks, a Multi-agent system framework implementing distributed Bayesian reasoning. Causal probabilistic models are exploited in several complementary ways. They support uniform and efficient integration of very heterogeneous information sources, such as different static and mobile sensors as well as human reports. In principle, modular Bayesian networks allow creation of complex probabilistic observation models which adapt to changing constellations of information sources at runtime. On the other hand, Bayesian networks are used also for coarse modeling of transitions in the gas propagation processes. By combining dynamic models of gas propagation processes with the observation models, we obtain adaptive Bayesian systems which correspond to Hidden Markov Models. The resulting systems facilitate seamless combination of prior domain knowledge and heterogeneous observations. © 2010 Springer-Verlag Berlin Heidelberg.


Pavlin G.,University of Amsterdam | Pavlin G.,Thales Research and Technology Netherlands | de Oude P.,University of Amsterdam | Maris M.,University of Amsterdam | And 2 more authors.
Information Fusion | Year: 2010

This paper introduces design principles for modular Bayesian fusion systems which can (i) cope with large quantities of heterogeneous information and (ii) can adapt to changing constellations of information sources on the fly. The presented approach exploits the locality of relations in causal probabilistic processes, which facilitates decentralized modeling and information fusion. Observed events resulting from stochastic causal processes can be modeled with the help of Bayesian networks, compact and mathematically rigorous probabilistic models. With the help of the theory of Bayesian networks and factor graphs we derive design and organization rules for modular fusion systems which implement exact belief propagation without centralized configuration and fusion control. These rules are applied in distributed perception networks (DPN), a multi-agent systems approach to distributed Bayesian information fusion. While each DPN agent has limited fusion capabilities, multiple DPN agents can autonomously collaborate to form complex modular fusion systems. Such self-organizing systems of agents can adapt to the available information sources at runtime and can infer critical hidden events through interpretation of complex patterns consisting of many heterogeneous observations. © 2009 Elsevier B.V. All rights reserved.


De Oude P.,University of Amsterdam | Groen F.C.A.,University of Amsterdam | Pavlin G.,University of Amsterdam | Pavlin G.,Thales Research and Technology Netherlands
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

This paper discusses an approach to distributed Bayesian modeling and inference, which is relevant for an important class of contemporary real world situation assessment applications. By explicitly considering the locality of causal relations, the presented approach (i) supports coherent distributed inference based on large amounts of very heterogeneous information, (ii) supports a systematic validation of distributed models and (iii) can be robust with respect to the modeling deviations of parameters. The challenges of distributed situation assessment applications and their solutions are explained with the help of a real world example from the gas monitoring domain. © 2011 Springer-Verlag.


Badica C.,University of Craiova | Conrado C.,Thales Research and Technology Netherlands | Mignet F.,Thales Research and Technology Netherlands | De Oude P.,Thales Research and Technology Netherlands | Pavlin G.,Thales Research and Technology Netherlands
Studies in Computational Intelligence | Year: 2013

This paper introduces challenges in contemporary situation assessment using collaborative inference and discusses solutions that are based on workflows between distributed processing nodes. The paper exposes the necessary conditions that workflows have to satisfy in order to support accurate situation assessment and provides a systematic approach to verification of the workflows. In particular, we emphasize the link between the complexity of the domain and the complexity of the workflows in terms of data and control coupling. With the help of graphical representations, we characterize the complexity of the domains and identify critical relations that have to be captured by collaborating processes in a workflow supporting correct situation assessment.

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