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A model of FSM-based planner and dialogue supporting system for emergency call services

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Abstract

The objective of an emergency call is to provide the operator with as much pertinent information about the emergency as possible, so that the best course of action can be taken toward its resolution. In this paper, we propose a dialogue planner to support operators who take emergency calls. The proposed system helps the operators to respond appropriately to a caller’s report by planning potential follow-up dialogue. The decision-making process used by the planner to select from among the proposed actions is implemented as a finite-state machine. The planner assigns a weight to each path between the states, evaluates the current dialogue model, and then adjusts the weights accordingly. Therefore, the weights assigned to potential actions vary dynamically as the dialogue progresses. The proposed model guides the operators’ decisions throughout each conversation by informing them regarding the action that has the highest weighting.

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References

  1. Lee K, Kim JK, Park MW, Kim L (2016) Situation based dynamic planning for dialog support with hierarchical knowledge. In: 2016 International Conference on Platform Technology and Service (PlatCon). https://doi.org/10.1109/platcon.2016.7456825

  2. Blair JA (1998) The limits of the dialogue model of argument. Argumentation 12:325–339

    Article  Google Scholar 

  3. Skar M, Sydnes M, Sydnes AK (2016) Integrating unorganized volunteers in emergency response management: a case study. Int J Emerg Serv 5:52–65

    Article  Google Scholar 

  4. Gabriel R (1976) Emergency call service. J R Coll Gen Pract 26(162):74–75

    Google Scholar 

  5. Lee K, Kim JK, Park MW, Kim L, Hsiao KF (2017) A situation-based dialogue classification model for emergency calls. In: 2017 International Conference on Platform Technology and Service (PlatCon), pp 1–4. https://doi.org/10.1109/platcon.2017.7883679

  6. Cimatti A, Giunchiglia E, Giunchiglia F, Traverso P (1997) Planning via model checking: a decision procedure for AR. In: Recent advances in AI planning. Springer, Berlin, pp 130–142. https://doi.org/10.1007/3-540-63912-8_81

  7. Kim J-D, Son J, Baik D-K (2012) CA5W1HOnto: ontological context-aware model based on 5W1H. Int J Distrib Sens Netw 8:247346

    Article  Google Scholar 

  8. Oh Y, Han J, Woo W (2010) A context management architecture for large-scale smart environments. IEEE Commun Mag 48:118–126

    Article  Google Scholar 

  9. Abowd GD, Wang H-M, Monk AF (1995) A formal technique for automated dialogue development. In: Proceedings of the 1st Conference on Designing Interactive Systems: Processes, Practices, Methods, & Techniques. ACM, pp 219–226. https://doi.org/10.1145/225434.225459

  10. Kreutel J, Matheson C (2003) Incremental information state updates in an obligation-driven dialogue model. Log J IGPL 11:485–511

    Article  MATH  Google Scholar 

  11. Bilange E (1991) A task independent oral dialogue model. In: Proceedings of the Fifth Conference on European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, pp 83–88. https://doi.org/10.3115/977180.977195

  12. Lambert L, Carberry S (1991) A tripartite plan-based model of dialogue. In: Proceedings of the 29th annual meeting on association for computational linguistics. Association for Computational Linguistics, pp 47–54. https://doi.org/10.3115/981344.981351

  13. Ahmad J, Sajjad M, Rho S, Kwon S, Lee M, Baik S (2018) Determining speaker attributes from stress-affected speech in emergency situations with hybrid SVM-DNN architecture. Multimed Tools Appl 77(4):4883–4907. https://doi.org/10.1007/s11042-016-4041-7

    Article  Google Scholar 

  14. Situation Meaning in the Cambridge English Dictionary. [Online]. http://dictionary.cambridge.org/dictionary/english/situation. Accessed 28 Feb 2018

  15. Devlin K (2006) Situation theory and situation semantics. In: Gabbay DM, Woods J (eds) Handbook of the history of logic, vol 7. North-Holland, Amsterdam, pp 601–664

    Google Scholar 

  16. Brand D, Zafiropulo P (1983) On communicating finite-state machines. J ACM 30:323–342

    Article  MathSciNet  MATH  Google Scholar 

  17. Tožička J, Jakůbuv J, Komenda A (2014) Generating multi-agent plans by distributed intersection of finite state machines. In: Proceedings of the Twenty-First European Conference on Artificial Intelligence. IOS Press, pp 1111–1112. https://doi.org/10.3233/978-1-61499-419-0-1111

  18. Cleeremans A, Servan-Schreiber D, McClelland JL (1989) Finite state automata and simple recurrent networks. Neural Comput 1:372–381

    Article  Google Scholar 

  19. Mohri M (1997) Finite-state transducers in language and speech processing. Comput Linguist 23:269–311

    MathSciNet  Google Scholar 

  20. Mohri M (1996) On some applications of finite-state automata theory to natural language processing. Nat Lang Eng 2:61–80

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Soonchunhyang University Research Fund and also supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean Government, MSIP (NRF-2015M3A9D7067219).

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Authors and Affiliations

  1. Convergence Institute of Medical Information Communication Technology and Management, Soonchunhyang University, Asan, South Korea

    Keonsoo Lee & Yunyoung Nam

  2. Division of Convergence Computer and Media, Mokwon University, Daejeon, South Korea

    Yang Sun Lee

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  1. Keonsoo Lee
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  2. Yang Sun Lee
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  3. Yunyoung Nam
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Correspondence to Yunyoung Nam.

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Lee, K., Lee, Y.S. & Nam, Y. A model of FSM-based planner and dialogue supporting system for emergency call services. J Supercomput 74, 4603–4612 (2018). https://doi.org/10.1007/s11227-018-2432-4

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