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  • 1.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Adaptation using iterated estimations2002In: Advances in Case-Based Reasoning: Proceedings of the 6th European Conference, ECCBR 2002, Aberdeen, Scotland, UK, September 4–7, 2002 / [ed] Susan Craw, Alun Preece, Springer Berlin/Heidelberg, 2002, p. 88-102Conference paper (Refereed)
  • 2.
    Falkman, Göran
    et al.
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Gustafsson, Marie
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). Department of Computer Science and Engineering, Chalmers, University of Technology/University of Gothenburg, Göteborg, Sweden.
    Torgersson, Olof
    Department of Computer Science and Engineering, Chalmers, University of Technology/University of Gothenburg, Göteborg, Sweden.
    Jontell, Mats
    Institute of Odontology, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden.
    The Origin, Representation, and Use of Collaboration Patterns in a Medical Community of Practice2008In: Emerging Technologies and Information Systems for the Knowledge Society: Proceedings of the First World Summit on the Knowledge Society, WSKS 2008, Athens, Greece, September 24–26, 2008 / [ed] Miltiadis D. Lytras, John M. Carroll, Ernesto Damiani, Robert D. Tennyson, Springer Berlin/Heidelberg, 2008, p. 403-412Conference paper (Refereed)
    Abstract [en]

    Founded on the Semantic Web technologies OWL and RDF, SOMWeb is an online community of practice that is used for knowledge sharing and dissemination within an oral medicine community in Sweden. It is shown how patterns for communication and collaboration within SOMWeb can be identified and represented in OWL, in terms of knowledge components, such as ontologies describing domain knowledge, user models, and organization models. It is described how patterns could be put into use and inform the design of future versions of SOMWeb.

  • 3.
    Falkman, Göran
    et al.
    University of Skövde, Department of Computer Science. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Torgersson, Olof
    Department of Computing Science, Chalmers University of Technology and Göteborg University.
    Enhancing usefulness of declarative programming frameworks through complete integration2002In: Proceedings of the 12th International Workshop on Logic Programming Environments (WLPE’02), Copenhagen, Denmark, July 31, 2002, CoRR , 2002, p. 111-122Conference paper (Refereed)
  • 4.
    Gustafsson Friberger, Marie
    et al.
    Department of Computer Science, Malmö University.
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Collaboration processes, outcomes, challenges and enablers of distributed clinical communities of practice2011In: Behavior and Information Technology, ISSN 0144-929X, E-ISSN 1362-3001, Vol. 32, no 6, p. 519-531Article in journal (Refereed)
    Abstract [en]

    Modern healthcare's need for knowledge sharing and bridging the research–practice gap requires new forms of collaboration, in which clinicians of varying clinical and research expertise work together over geographical and organisational borders. To support such distributed communities of practice (CoPs), an understanding of their collaboration processes, outcomes, challenges and enablers is needed. The article examines these issues through a case study of a long-running CoP, the Swedish Oral Medicine Network (SOMNet). SOMNet's main form of collaboration is monthly telephone conference meetings centred on case consultations. Cases are submitted by the clinicians via a Web-based system. The methods used were interviews, observations, and a questionnaire. The work adds to previous research by studying a distributed CoP explicitly focused on supporting the transfer of scientific results from researchers to practitioners. We found that the regular meetings give a rhythm to the community. The centrality of cases means an immediate benefit for the submitter while the community is provided an authentic context for learning. SOMNet yields opportunities for help and learning for diverse expertise levels; the type of benefits is affected by the participant's degree of oral medicine knowledge and collaboration involvement. There are challenges in accommodating varying levels of expertise and encouraging those less experienced to participate. Enablers of the collaboration include the participation of experts, meeting facilitators and well-adapted ITs.

  • 5.
    Gustafsson, Marie
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). Computer Science and Engineering, Chalmers University of Technology, Göteborg, Sweden.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Experiences in Modeling Clinical Examinations in Oral Medicine Using OWL2007In: Proceedings of the OWLED 2007 Workshop on OWL: Experiences and Directions, CEUR-WS.org , 2007, p. 1-10Conference paper (Refereed)
    Abstract [en]

    This article describes the modeling of clinical examinations in oral medicine using OWL. Based on experiences from our previous work and knowledge model, requirements for an ontology for examinations in oral medicine are identified. OWL can be used to address most, but not all, of the requirements. We found a lack of guidance for several design choices and for development of OWL ontologies at different levels of sophistication. However, using OWL gives us the ability to come back and refine the knowledge model after initial deployment.

  • 6.
    Gustafsson, Marie
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Representing Clinical Knowledge in Oral Medicine Using Ontologies2005In: Connecting Medical Informatics and Bio-Informatics: Proceedings of MIE2005 - The XIXth International Congress of the European Federation for Medical Informatics / [ed] Rolf Engelbrecht, Antoine Geissbuhler, Christian Lovis, George Mihalas, Amsterdam: IOS Press, 2005, p. 743-748Conference paper (Refereed)
    Abstract [en]

    Medical ontologies like GALEN, the FMA or SNOMED represent a kind of “100% certain” medical knowledge which is not inherent to all medical sub-domains. Clinical radiology uses computerized imaging techniques to make the human body visible and interprets the imaging findings in a clinical context delivering a textual report. For clinical radiology few standardized vocabularies are available. We examined the definitions given in the glossary of terms for thoracic radiology published by the Fleischner Society. We further classified these terms with regard to their definitions in terms of (a) describing visible structures on the image itself, (b) referring to ontological entities of the body (anatomical or pathological), and (c) terms imposing knowledge on structures visible on the image, epistemologically representing ontological entities of the body. Each ontological/epistemological definition was rated on a scale of vague/weak-sound/strong and put in context with the evaluation comments for the use of the terms given in the glossary itself. The result of this distinction shows that clinical radiology uses many terms referring to ontological entities valid for representation in a medical ontology. However, many epistemological terms exist in the terminology which impose epistemological knowledge on ontological entities. The analysis of the evaluation comments reveals that terms classified as sound (ontologically) and strong (epistemologically) are evaluated higher than terms bearing vague or weak definitions. On the basis of this, we argue that the distinction between ontological and epistemological definitions is necessary in order to construct epistemologically-sensitive application ontologies for medical sub-domains, like clinical radiology, where knowledge is fragmented in terms of description, inferred from a description, concluded on the basis of imaging, or other additional information with varying degrees of certainty.

  • 7.
    Gustafsson, Marie
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). Chalmers University of Technology, Göteborg, Sweden.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Study of Use and Needs of an Online Community of Practice in Oral Medicine2008In: eHealth beyond the horizon – get IT there: Poster abstracts of the 21st International Congress of the European Federation for Medical Informatics (MIE 2008), Göteborg, Sweden, May 25–28 2008, 2008Conference paper (Refereed)
  • 8.
    Gustafsson, Marie
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). Computer Science and Engineering, Chalmers University of Technology, Göteborg, Sweden.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Lindahl, Fredrik
    Computer Science and Engineering, Chalmers University of Technology, Göteborg, Sweden.
    Torgersson, Olof
    Computer Science and Engineering, Chalmers University of Technology, Göteborg, Sweden.
    Enabling an online community for sharing oral medicine cases using semantic web technologies2006In: The Semantic Web - ISWC 2006: 5th International Semantic Web Conference, ISWC 2006, Athens, GA, USA, November 5-9, 2006. Proceedings / [ed] Isabel Cruz, Stefan Decker, Dean Allemang, Chris Preist, Daniel Schwabe, Peter Mika, Mike Uschold, Lora M. Aroyo, Berlin, Heidelberg: Springer Berlin/Heidelberg, 2006, p. 820-832Conference paper (Refereed)
    Abstract [en]

    This paper describes how Semantic Web technologies have been used in an online community for knowledge sharing between clinicians in oral medicine in Sweden. The main purpose of this community is to serve as repository of interesting and difficult cases, and as a support for monthly teleconferences. All information regarding users, meetings, news, and cases is stored in RDF. The community was built

  • 9.
    Helldin, Tove
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    Human-Centred Automation and the Development of Fighter Aircraft Support systems2011In: Proceedings of the Swedish Human Factors Network (HFN) Conference, Linköping, Sweden, November 24-25, 2011, Human Factors Network (HFN) , 2011, p. 21 sidor-Conference paper (Refereed)
  • 10.
    Helldin, Tove
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Human-Centred Automation of Threat Evaluation in Future Fighter Aircraft2011In: Informatik 2011 / [ed] Hans-Ulrich Heiß, Peter Pepper, Holger Schlingloff, Jörg Schneider, Bonn: Gesellschaft für Informatik , 2011, p. 502-513Conference paper (Refereed)
    Abstract [en]

    It has long been considered crucial to develop decision support systems that aid fighter pilots achieve their goals. Such systems often require automation of tasks formerly performed manually by the pilots, in situations characterized by huge amounts of (possibly uncertain and incomplete) sensor data and contextual information, time-pressure and dynamically changing tasks. Thus, careful investigations must be performed so as to develop such systems that provide accurate support for their users. This paper reports on the findings concerning research within the field of human-centred automation as well as presents empirical results concerning the applicability of automation guidelines when designing information fusion based support systems in the fighter aircraft domain.

  • 11.
    Helldin, Tove
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Alfredson, Jens
    Saab AB, Aeronautics, SE-581 88 Linköping, Sweden.
    Holmberg, Johan
    Saab AB, Aeronautics, SE-581 88 Linköping, Sweden.
    The Applicability of Human-Centred Automation Guidelines in the Fighter Aircraft Domain2011In: Proceedings of the 29th European Conference on Cognitive Ergonomics (ECCE´11) / [ed] Anke Dittmar, Peter Forbrig, Association for Computing Machinery (ACM), 2011, p. 67-74Conference paper (Refereed)
    Abstract [en]

    Motivation – To guide the development of human-centred automation within the fighter aircraft domain.

    Research approach – Identified human-centred automation guidelines have been analysed in relation to existing fighter aircraft automated functions together with system developers at Saab Aeronautics.

    Findings/Design – The results show that the human-centred automation guidelines have been considered during the development process. From these results, implications for the design of guidelines and for the design of automated systems in the aircraft domain are drawn.

    Research limitations/Implications – Deeper analysis of how automated functions can support pilots in future fighter aircraft is needed since the proposed guidelines are too general for the military fighter aircraft domain. Thus, future work involves an evaluation of the guidelines together with fighter aircraft system developers and/or military strategists. Such analysis must be carried out with specific automated functions in mind.

    Originality/Value – By comparing with existing automated functions, the research makes contributions to HCA guidelines to be used in the fighter aircraft domain. Suggestions of human-centred automation improvements within the fighter aircraft domain are presented. The analysis has also identified differences between the proposed guidelines and parts of the studied implementation examples.

    Take away message – The HCA guidelines must be adapted according to the specific tasks that the automated functions are intended to assist the operators with. To adapt the automation according to the level of experience of the operators as well as to expand the cooperative automation functions between aircraft in a team have been identified as future directions for automation improvements within the fighter aircraft domain.

  • 12.
    Helldin, Tove
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Riveiro, Maria
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Dahlbom, Anders
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Lebram, Mikael
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Transparency of military threat evaluation through visualizing uncertainty and system rationale2013In: Engineering Psychology and Cognitive Ergonomics: Applications and Services / [ed] Don Harris, Springer Berlin/Heidelberg, 2013, no PART 2, p. 263-272Conference paper (Refereed)
    Abstract [en]

    Threat evaluation (TE) is concerned with determining the intent, capability and opportunity of detected targets. To their aid, military operators use support systems that analyse incoming data and make inferences based on the active evaluation framework. Several interface and interaction guidelines have been proposed for the implementation of TE systems; however there is a lack of research regarding how to make these systems transparent to their operators. This paper presents the results from interviews conducted with TE operators focusing on the need for and possibilities of improving the transparency of TE systems through the visualization of uncertainty and the presentation of the system rationale. © 2013 Springer-Verlag Berlin Heidelberg.

  • 13.
    Helldin, Tove
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Riveiro, Maria
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Davidsson, Staffan
    Volvo Car Corporation, Gothenburg, Sweden.
    Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving2013In: Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI’13), New York: Association for Computing Machinery (ACM), 2013, p. 210-217Conference paper (Refereed)
    Abstract [en]

    To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.

  • 14.
    Johansson, Fredrik
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    A Bayesian network approach to threat evaluation with application to an air defense scenario2008In: Proceedings of the 11th International Conference on Information Fusion, FUSION 2008, Cologne, 30 June 2008–3 July 2008, IEEE Computer Society, 2008, p. 1352-1358Conference paper (Refereed)
    Abstract [en]

    In this paper, a precise description of the threat evaluation process is presented. This is followed by a review describing which parameters that have been suggested for threat evaluation in an air surveillance context throughout the literature, together with an overview of different algorithms for threat evaluation. Grounded in the findings from the literature review, a threat evaluation system have been developed. The system is based on a Bayesian network approach, making it possible to handle imperfect observations. The structure of the Bayesian network is described in detail. Finally, an analysis of the system’s performance as applied to a synthetic scenario is presented.

  • 15.
    Johansson, Fredrik
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    A Comparison between Two Approaches to Threat Evaluation in an Air Defense Scenario2008In: Modeling Decisions for Artificial Intelligence: 5th International Conference, MDAI 2008 Sabadell, Spain, October 30-31, 2008. Proceedings, Springer Berlin/Heidelberg, 2008, p. 110-121Conference paper (Refereed)
    Abstract [en]

     Threat evaluation is a high-level information fusion problem of high importance within the military domain. This task is the foundation for weapons allocation, where assignment of blue force (own) weapon systems to red force (enemy) targets is performed. In this paper, we compare two fundamentally different approaches to threat evaluation: Bayesian networks and fuzzy inference rules. We conclude that there are pros and cons with both types of approaches, and that a hybrid of the two approaches seems both promising and viable for future research.

     

  • 16.
    Johansson, Fredrik
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    A survivability-based testbed for comparing threat evaluation algorithms2008In: Proceedings of the 2nd Skövde Workshop on Information Fusion Topics (SWIFT 2008) / [ed] H. Boström, R. Johansson, Joeri van Laere, Skövde: University of Skövde , 2008, p. 22-24Conference paper (Refereed)
    Abstract [en]

    Threat evaluation is the process in which threat values are assigned to detected targets, based upon the inferred capabilities and intents of the targets to inflict damage to blue force defended assets. This is a high-level information fusion process of high importance, since the calculated threat values are used as input when blue force weapon systems are allocated to the incoming targets, a process often referred to as weapon allocation. Threat values can be calculated from a number of different parameters, such as the position of the closest point of approach (CPA) with respect to blue force defended assets, time required to reach the CPA, the target’s velocity, and its type. A number of algorithms for calculating threat values have been suggested throughout literature, however, criteria to evaluate the performance of such algorithms seem to be lacking. In this paper, we discuss different ways to assess the performance of threat evaluation algorithms. In specific, we describe how threat evaluation algorithms can be compared to each other, using a survivability criterion. Survivability is measured by running the threat evaluation algorithms on simulated scenarios and using the resulting threat values as input to a weapon allocation module. Depending on how well the threat evaluation is performed, the ability of the blue force weapon systems to eliminate the incoming targets will vary (and thereby also the survivability of the defended assets).

     

  • 17.
    Johansson, Fredrik
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    An empirical investigation of the static weapon-target allocation problem2009In: Proceedings of the 3rd Skövde Workshop on Information Fusion Topics (SWIFT 2009) / [ed] Ronnie Johansson, Joeri van Laere and Jonas Mellin, University of Skövde , 2009, p. 63-67Conference paper (Refereed)
    Abstract [en]

    The allocation of weapons to targets (such as missiles and hostile aircrafts) is a well-known resource allocation problem within the field of operations research. It has been proven that this problem, in general, is NP-complete. For this reason, optimal solutions to the static weapon-target allocation (WTA) problem can not be obtained in real-time for large-scale problems. We try to find the limit for how large problems that can be solved optimally in real-time by exhaustive search algorithms through running empirical experiments. We also propose a heuristic genetic algorithm for solving larger-scale problems.

  • 18.
    Johansson, Fredrik
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Detection of vessel anomalies: A Bayesian network approach2007In: Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2007), IEEE Computer Society, 2007, p. 395-400Conference paper (Refereed)
    Abstract [en]

    In this paper we describe a data mining approach for detection of anomalous vessel behaviour. The suggested approach is based on Bayesian networks which have two important advantages compared to opaque machine learning techniques such as neural networks: (1) possibility to easily include expert knowledge into the model, and (2) possibility for humans to understand and interpret the learned model. Our approach is implemented and tested on synthetic data, where initial results show that it can be used for detection of single-object anomalies such as speeding.

  • 19.
    Johansson, Fredrik
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Implementation and integration of a Bayesian Network for prediction of tactical intention into a ground target simulator2006In: Proceedings of the 9th IEEE International Conference on Information Fusion (FUSION 2006), Florence, Italy, July 10–13, 2006, IEEE Computer Society, 2006, p. 1-7Conference paper (Refereed)
    Abstract [en]

    Prediction of the enemy's intention is a main issue of threat analysis, and, hence, will be an important part of the C2-systems of tomorrow. A technique that can be useful for this kind of predictions is Bayesian networks (BNs). We have developed a BN for prediction of the enemy's tactical intention, and the implemented BN has been integrated into a ground target simulation framework. The general problem of how to find appropriate prior distributions for BNs has been addressed by developing a tool for data collection, which may make it easier to come up with appropriate prior distributions, by learning conditional probability tables from collected cases, i.e. parameter learning

  • 20.
    Johansson, Fredrik
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Performance Evaluation of TEWA Systems for Improved Decision Support2009In: Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2009) / [ed] Vicenç Torra, Yasuo Narukawa, Masahiro Inuiguchi, Springer Berlin/Heidelberg, 2009, p. 205-216Conference paper (Refereed)
    Abstract [en]

    In air defense situations, decision makers have to protect defended assets through assigning available firing units to threatening targets in real-time. To their help they have decision support systems known as threat evaluation and weapon allocation (TEWA) systems. The problem of performance evaluation of such systems is of great importance, due to their critical role. Despite this, research on this problem is close to non-existing. We are discussing the use of survivability and resource usage cost as comparative performance metrics, which can be used for comparing the effectiveness of different system configurations, by using simulations. These metrics have been implemented into a testbed, in which we have performed some comparative experiments. Our results show that changes of individual parts of the threat evaluation and weapon allocation system configuration can have a large effect on the effectiveness of the system as a whole, and illustrate how the metrics and the testbed can be used.

     

  • 21.
    Johansson, Fredrik
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Real-time Allocation of Firing Units To Hostile Targets2011In: Journal of Advances in Information Fusion, ISSN 1557-6418, Vol. 6, no 2, p. 187-199Article in journal (Refereed)
    Abstract [en]

    The protection of defended assets such as military bases and population centers against hostile targets (e.g., aircrafts, missiles, and rockets) is a highly relevant problem in the military conflicts of today and tomorrow. In order to neutralize threats of this kind, they have to be detected and engaged before causing any damage to the defended assets. We review algorithms for solving the resource allocation problem in real-time, and empirically investigate their performance using the open source testbed SWARD. The reults show that many of the tested algorithms produce high quality solutions for small-scale problems. A novel vaiant of particle swarm optimization seeded with an enhanced greedy algorithm is described and is shown to perform best for large instances of the real-time allocation problem.

  • 22.
    Jontell, Mats
    et al.
    Göteborgs universitet.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Gustafsson, Marie
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Torgersson, Olof
    Chalmers.
    Elektroniskt verktyg för klinik, utbildning och forskning2008In: Tandläkartidningen, ISSN 0039-6982, Vol. 100, no 12, p. 78-81Article in journal (Refereed)
    Abstract [sv]

    Att praktisera evidensbaserad odontologi innebär att integrera expertisen hos individuella kliniker med bästa vetenskapliga evidens från externa kunskapskällor.

  • 23.
    Khan, Fahad Shabhaz
    et al.
    Chalmers University of Technology.
    Anwer, Rao Muhammad
    Chalmers University of Technology.
    Torgersson, Olof
    Chalmers University of Technology.
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    Data Mining in Oral Medicine Using Decision Trees2008In: Proceedings of the 5th International Conference on Computer, Electrical, and Systems Science, and Engineering (CESSE 2008), Cairo, Egypt, February 6–8, 2008, World Academy of Science Engineering and Technology - WASET , 2008, p. 225-230Conference paper (Refereed)
    Abstract [en]

    Data mining has been used very frequently to extract hidden information from large databases. This paper suggests the use of decision trees for continuously extracting the clinical reasoning in the form of medical expert’s actions that is inherent in large number of EMRs (Electronic Medical records). In this way the extracted data could be used to teach students of oral medicine a number of orderly processes for dealing with patients who represent with different problems within the practice context over time.

  • 24.
    Niklasson, Lars
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    Johansson, Fredrik
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Dahlbom, Anders
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Brax, Christoffer
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Kronhamn, Thomas
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Smedberg, Martin
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Warston, Håkan
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Gustavsson, Per M.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). Saab Microwave Systems, Skövde, Sweden.
    A Unified Situation Analysis Model for Human and Machine Situation Awareness2007In: INFORMATIK 2007: Informatik trifft Logistik: Band 2: Beiträge der 37. Jahrestagung der Gesellschaft für Informatik e.V. (GI) 24. - 27. September 2007 in Bremen / [ed] Otthein Herzog, Karl-Heinz Rödiger, Marc Ronthaler, Rainer Koschke, Bonn: Gesellschaft für Informatik , 2007, p. 105-109Conference paper (Refereed)
  • 25.
    Niklasson, Lars
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics.
    Johansson, Fredrik
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Dahlbom, Anders
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Brax, Christoffer
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Kronhamn, Thomas
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Smedberg, Martin
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Warston, Håkan
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Gustavsson, Per M.
    Product Development, Saab Microwave Systems, Gothenburg, Sweden.
    Extending the scope of Situation Analysis2008In: Proceedings of the 11th International Conference on Information Fusion (FUSION 2008), Cologne, Germany, June 30–July 3, 2008, IEEE Press, 2008, p. 454-461Conference paper (Refereed)
    Abstract [en]

    The use of technology to assist human decision making has been around for quite some time now. In the literature, models of both technological and human aspects of this support can be identified. However, we argue that there is a need for a unified model which synthesizes and extends existing models. In this paper, we give two perspectives on situation analysis: a technological perspective and a human perspective. These two perspectives are merged into a unified situation analysis model for semi-automatic, automatic and manual decision support (SAM)2. The unified model can be applied to decision support systems with any degree of automation. Moreover, an extension of the proposed model is developed which can be used for discussing important concepts such as common operational picture and common situation awareness.

  • 26.
    Ohlander, Ulrika
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Saab Aeronautics, Saab AB, Linköping, Sweden.
    Alfredson, Jens
    Saab Aeronautics, Saab AB, Linköping, Sweden.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Understanding Team Effectiveness in a Tactical Air Unit2015In: Engineering Psychology and Cognitive Ergonomics: 12th International Conference, EPCE 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, Proceedings / [ed] Don Harris, Springer, 2015, Vol. 9174, p. 472-479Conference paper (Refereed)
    Abstract [en]

    Effective team work is regarded as a key factor for success in missions performed by fighter aircraft in a Tactical Air Unit (TAU). Many factors contrib-ute to how a team will succeed in their mission. From the existing literature on teamwork, Salas, Sims and Burke [1], suggested five main factors and three sup-porting mechanisms for effective team work. These were proposed as the “Big Five” of teamwork. This article investigates if the model offered by Salas et al. is applicable to a TAU of fighter aircraft. Semi-structured interviews were carried out with six fighter pilots. The results of these interviews imply that the model has relevance for the teamwork in a TAU. Moreover, this paper discusses impli-cations for the design of future decision-support systems that support team effec-tiveness. 

  • 27.
    Riveiro, Maria
    et al.
    University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL). University of Skövde, School of Humanities and Informatics.
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Empirical evaluation of visualizations of normal behavioral models for supporting maritime anomaly detection2011In: Abstracts of GeoViz: Linking Geovisualization with Spatial Analysis and Modeling, March 10–11, 2011, Hamburg, Germany, 2011, p. 1-2Conference paper (Refereed)
  • 28.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Interactive Visualization of Normal Behavioral Models and Expert Rules for Maritime Anomaly Detection2009In: Computer graphics, imaging & visualisation: New advances and trends / [ed] Ebad Banissi, Muhammad Sarfraz, Jiawan Zhang, Anna Ursyn, Wong Chow Jeng, Mark W. McK. Bannatyne, Jian J. Zhang, Lim Hwee San, and Mao Lin Huang, IEEE Computer Society, 2009, p. 459-466Conference paper (Refereed)
    Abstract [en]

    Maritime surveillance systems analyze vast amounts of heterogeneous sensor data from a large number of objects. In order to support the operator while monitoring such systems, the identification of anomalous vessels or situations that might need further investigation may reduce the operator’s cognitive load. While it is worth acknowledgingthat many existing mining applications support identification of anomalous behavior, autonomous anomaly detection systems are rarely used in the real world, since the detection of anomalous behavior is normally not a welldefined problem and therefore, human expert knowledge is needed. This calls for the development of interaction components that can support the user in the detection process.

    In order to support the comprehension of the knowledge embedded in the system, we propose an interactive way of visualizing expert rules and normal behavioral models built from the data. The overall goal is to facilitate the validation and update of these models and signatures, supporting the insertion of human expert knowledge while improving confidence and trust in the system.

  • 29.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    The role of visualization and interaction in maritime anomaly detection2011In: Visualization and Data Analysis 2011: Proceedings of SPIE / IS & T Electronic Imaging / [ed] Pak Chung Wong, Jinah Park, Ming C. Hao, Chaomei Chen, Katy Börner, David L. Kao, Jonathan C. Roberts, SPIE - International Society for Optical Engineering, 2011, p. Article number 78680M, 1-12Conference paper (Refereed)
    Abstract [en]

    The surveillance of large sea, air or land areas normally involves the analysis of large volumes of heterogeneous data from multiple sources. Timely detection and identification of anomalous behavior or any threat activity is an important objective for enabling homeland security. While it is worth acknowledging that many existing mining applications support identification of anomalous behavior, autonomous anomaly detection systems for area surveillance are rarely used in the real world. We argue that such capabilities and applications present two critical challenges: (1) they need to provide adequate user support and (2) they need to involve the user in the underlying detection process.

    In order to encourage the use of anomaly detection capabilities in surveillance systems, this paper analyzes the challenges that existing anomaly detection and behavioral analysis approaches present regarding their use and maintenance by users. We analyze input parameters, detection process, model representation and outcomes. We discuss the role of visualization and interaction in the anomaly detection process. Practical examples from our current research within the maritime domain illustrate key aspects presented.

  • 30.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Improving maritime anomaly detection and situation awareness through interactive visualization2008In: Proceedings of the 11th International Conference on Information Fusion (FUSION 2008), IEEE Computer Society, 2008, p. 47-54Conference paper (Refereed)
    Abstract [en]

    Surveillance of large land, air or sea areas with a multitude of sensor and sensor types typically generates huge amounts of data. Human operators trying to establish individual or collective maritime situation awareness are often overloaded by this information. In order to help them cope with this information overload, we have developed a combined methodology of data visualization, interaction and mining techniques that allows filtering out anomalous vessels, by building a model over normal behavior from which the user can detect deviations. The methodology includes a set of interactive visual representations that support the insertion of the user’s knowledge and experience in the creation, validation and continuous update of the normal model. Additionally, this paper presents a software prototype that implements the suggested methodology.

     

  • 31.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Falkman, Göran
    University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Visual Analytics for the Detection of Anomalous Maritime Behavior2008In: Proceedings of 12th International Conference on Information Visualisation IV08 / [ed] Ebad Banissi, Liz Stuart, Mikael Jern, Gennady Andrienko, Francis T. Marchese, Nasrullah Memon, Reda Alhajj, Theodor G. Wyeld, Remo Aslak Burkhard, Georges Grinstein, Dennis Groth, Anna Ursyn, Carsten Maple, Anthony Faiola, and Brock Craft, IEEE Computer Society, 2008, p. 273-279Conference paper (Refereed)
    Abstract [en]

    The surveillance of large sea areas often generates huge amounts of multidimensional data. Exploring, analyzing and finding anomalous behavior within this data is a complex task. Confident decisions upon the abnormality of a particular vessel behavior require a certain level of situation awareness that may be difficult to achieve when the operator is overloaded by the available information. Based on a visual analytics process model, we present a novel system that supports the acquisition of situation awareness and the involvement of the user in the anomaly detection process using two layers of interactive visualizations. The system uses an interactive data mining module that supports the insertion of the user's knowledge and experience in the creation, validation and continuous update of the normal model of the environment.

  • 32.
    Riveiro, Maria
    et al.
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Ziemke, Tom
    University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
    Warston, Håkan
    Saab Microwave Systems AB (Sweden).
    VISAD: an interactive and visual analytical tool for the detection of behavioural anomalieis in maritime traffic data2009In: Visual Analytics for Homeland Defense and Security: Proceedings of SPIE Defense, Security, and Sensing 2009 / [ed] William J. Tolone, William Ribarsky, SPIE - International Society for Optical Engineering, 2009, p. Article ID 734607-Conference paper (Refereed)
    Abstract [en]

    Monitoring the surveillance of large sea areas normally involves the analysis of huge quantities of heterogeneous data from multiple sources (radars, cameras, automatic identification systems, reports, etc.). The rapid identification of anomalous behavior or any threat activity in the data is an important objective for enabling homeland security. While it is worth acknowledging that many existing mining applications support identification of anomalous behavior, autonomous anomaly detection systems are rarely used in the real world. There are two main reasons: (1) the detection of anomalous behavior is normally not a well-defined and structured problem and therefore, automatic data mining approaches do not work well and (2) the difficulties that these systems have regarding the representation and employment of the prior knowledge that the users bring to their tasks. In order to overcome these limitations, we believe that human involvement in the entire discovery process is crucial.

    Using a visual analytics process model as a framework, we present VISAD: an interactive, visual knowledge discovery tool for supporting the detection and identification of anomalous behavior in maritime traffic data. VISAD supports the insertion of human expert knowledge in (1) the preparation of the system, (2) the establishment of the normal picture and (3) in the actual detection of rare events. For each of these three modules, VISAD implements different layers of data mining, visualization and interaction techniques. Thus, the detection procedure becomes transparent to the user, which increases his/her confidence and trust in the system and overall, in the whole discovery process.

  • 33.
    Torgersson, Olof
    et al.
    Department of Computing Science, Chalmers University of Technology, Sweden.
    Falkman, Göran
    University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).
    Using text generation to access clinical data in a variety of contexts2002In: Health Data in the Information Society: Proceedings of MIE2002, IOS Press, 2002, p. 460-465Conference paper (Refereed)
1 - 33 of 33
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