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Developing a smart environment for crisis management training

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Abstract

Despite the growth of advanced communication technologies, smart devices etc. the main approach to training strategic planners for crisis management (Gold Commanders) continues to be a paper-based, collective group dynamic exercise. The Pandora project has developed an advanced smart environment for the training of Gold Commanders which uses AI planning techniques to provide a crisis scenario modelled as an event network. This includes points of decision for trainees managed by automated rules from a knowledge base, behavioural modelling of the trainees, and ambient management of the environment to provide affective inputs to control and manage trainee stress. In this context, the system controls and reacts to trainee performance in relation to the events and decision points and can dynamically remodel and reconfigure the event network to respond appropriately to trainee decisions. Trainees can also be pressurised through compression of the timelines or ambient management of the multimedia information presented within the environment, causing them to make decisions under stress or with inadequate information. The environment can also represent any missing trainees within the scenario, which provides the potential to offer a completely autonomous facility for scenario design and test, and potentially a decision support facility, based on a build-up of empirical evidence from real world and training situations. In summary, the Pandora system integrates its computational intelligence, with the intelligence of the trainer and the trainees, to provide an emotionally engaging, augmented reality/virtual reality training environment for crisis managers.

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References

  • Bacon L, Windall G, MacKinnon L (2011) The development of a rich multimedia training environment for crisis management: using emotional affect to enhance learning. In: Proceedings of the 18th association for learning technology conference (ALT-C 2011), September 6–8, at the University of Leeds, UK. doi:10.3402/rlt.v19s1/7780. Paper 0151. Available at: http://repository.alt.ac.uk/id/eprint/2174. Full proceedings at http://repository.alt.ac.uk/id/eprint/2159. ISBN 978-91-977071-5-2

  • Bales R (1958) Task roles and social roles in problem solving groups. In: Macoby EE, Newcomb TM (eds) Readings in social psychology. Holt, Rinehart and Winston, New York

    Google Scholar 

  • Bandura A (1986) Social foundations of thought and action: a social cognitive theory. Prentice-Hall, Englewood Cliffs, NJ

  • Behrens RR (1998) Art, design and gestalt theory, vol 31, No. 4. The MIT Press, Leonardo, pp 299–303

  • Belbin RM (2010) Team roles at work, 2nd edn. Butterworth–Heinemann, Oxford

    Google Scholar 

  • Cesta A, Cortellessa G, De Benedictis R, Strickland K (2011) AI*IA 2011 Using planning for training crisis decision makers. AI*IA-11. In: 12th International conference of the artificial intelligence. Italian association. Palermo, Italy, 15–17 Sept 2011

  • Clark Russell D III (1974) Risk taking in groups: a social psychological analysis. J Risk Insur 41(1):75–92

    Article  Google Scholar 

  • Dahl TS, Mataric MJ, Sukhatme GS (2007) “Emergent robot differentiation for distributed multi-robot task allocation”, distributed autonomous robotic systems 6, Part V edn. Springer, Berlin, pp 201–210

    Google Scholar 

  • Engelbrecht AP (2005) Fundamentals of computational swarm intelligence. Wiley, New York

    Google Scholar 

  • Funder DC (2007) The personality puzzle, 4th edn. Norton, New York

    Google Scholar 

  • Heylighen F (1999) “Collective intelligence and its implementation on the Web: algorithms to develop a collective mental map”, computational and mathematical organization theory, vol 5, No 3, Kluwer, New York, pp 253–280

  • Liu H, Arafa Y, Boldyreff C, Dastbaz M (2011) Cost-effective virtual world development for serious games, IGIC 2011. In: Proceedings of the 3rd IEEE international games innovation, pp 48–51

  • MacKinnon L, Bacon E, Windall G (2010) “Tracking and influencing trainee emotions in a crisis-planning scenario”. In: W3C workshop on emotion markup language. Paris, 5th and 6th Oct 2010

  • Mcquiggan S, Mott B, Lester JC (2008) Modeling self-efficacy in intelligent tutoring systems: an inductive approach. User Model User Adap Inter 18:81–123

    Article  Google Scholar 

  • Northouse G (2007) Leadership theory and practice, 4th edn. Sage publication, Thousand Oak

    Google Scholar 

  • OpenSim (2012) http://opensimulator.org/wiki/Main_Page, Accessed Mar 2012

  • Salas E, Driskell J, Hughs S (1996) The study of stress and human performance. In: Driskell J, Salas E (eds) Stress and human performance. Lawrence Erlbaum Associates, New Jersey

    Google Scholar 

  • Salgado JF (1997) The five factor model of personality and job performance in the European Community. J Appl Psychol 82:30–43

    Article  Google Scholar 

  • Stevens CD, Ash RA (2001) Selecting employees for fit: personality and preferred managerial style. J Manag 12:500–517

    Google Scholar 

  • W3C (2011) Emotion Markup Language (EmotionML) 1.0 Working Draft 7 April 2011, http://www.w3.org/TR/emotionml/

  • De Schutter G, Theraulaz G, Deneubourg JL (2001) “Animal-robots collective intelligence”, annals of mathematics and artificial intelligence, vol 31, Kluwer, New York, pp 223–238

  • Zaccaro SJ, White L, Kilcullen R, Parker C, Williams D, O’Connor-Boes J (1997) Cognitive and temperament predictors of Army civilian leadership. Manag Res Inst, Bethesda

    Google Scholar 

  • Zinn JO (2008) Heading into unknown: everyday strategies for managing risk and uncertainty. Heal Risk Soc 10(5):439–450

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank colleagues from the other partners in the PANDORA project for their contributions to the work presented, those partners being CEFRIEL (Italy), XLAB (Slovenia), FUB (Italy), UEL (UK), ORT (France) and EPC (UK). We would also wish to thank the EU for funding this work under FP7-ICT-SEC-2007-1 Grant Number 225387.

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Authors and Affiliations

  1. School of Computing and Mathematical Sciences, University of Greenwich, Greenwich, London, UK

    L. Bacon & L. MacKinnon

  2. Consiglio Nazionale delle Ricerche (CNR-ISTC), Rome, Italy

    A. Cesta & G. Cortellessa

Authors
  1. L. Bacon
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  2. L. MacKinnon
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  3. A. Cesta
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  4. G. Cortellessa
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Correspondence to L. Bacon.

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Bacon, L., MacKinnon, L., Cesta, A. et al. Developing a smart environment for crisis management training. J Ambient Intell Human Comput 4, 581–590 (2013). https://doi.org/10.1007/s12652-012-0124-0

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  • DOI: https://doi.org/10.1007/s12652-012-0124-0

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