Entity

Time filter

Source Type


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. Source


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. Source


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. Source


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. Source


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). Source

Discover hidden collaborations