Skip to main content
Log in

A verifiable multi-agent framework for dependable and adaptable avionics

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

The aerospace industry is continuously looking for improvements in operational efficiency and performance of systems. In its quest to do so, the industry is turning to Intelligent Adaptive Systems as a possible solution in many areas. However, the nature of the domain imposes expectations of safety, correctness and guarantees of behaviour from such systems. Meeting these expectations simultaneously, finally leading to certified products, poses many challenging problems. A research gap is perceived when the cycle of requirements, system design, verification and validation is examined, paving the need for correctness and guarantees of specifications in the early stages of a complex adaptive avionics system. We present a framework that is targeted for a broad class of avionics systems, engineered for short- and long-term system behaviours, resilient, real-time decision making, establishing trust on the way to certification and being amenable to analysis using formal methods. We have used this framework with two case studies (Flight Management System and Unmanned Aircraft System) and provide an application of this framework with one case study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

Notes

  1. A widely used system design tool that uses Model-Driven Development philosophy for Agent-Based Modelling and Simulations enhancing design quality, reducing development effort and meeting qualitative aspects.

References

  1. Intelligent Systems Technical Committee 2016 Roadmap for intelligent systems in aerospace. AIAA, 1st edition

  2. Cheng B H et al 2009 Springer-Verlag, Berlin, Heidelberg, pp. 1–26

  3. Kashi Rajanikanth Nagaraj, D'Souza Meenakshi and Kishore Raman Koyalkar 2017 Incorporating formal methods and measures obtained through analysis, simulation testing for dependable self-adaptive software in avionics systems. In: Proceedings of the 10th ACM India Conference, Bhopal, India, ACM COMPUTE 2017

  4. Kashi Rajanikanth Nagaraj, D'Souza Meenakshi and Baghel S Kumar, Kulkarni Nitin 2016 Incorporating adaptivity using learning in avionics self adaptive software: A case study. In: International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016, Jaipur, India, Sept 21-24

  5. Kashi Rajanikanth Nagaraj, D'Souza Meenakshi and Baghel S Kumar, Kulkarni Nitin 2016 Formal verification of avionics self adaptive software: A case study. In: Proceedings of the 9th India Software Engineering Conference, Goa, India, ACM, pp. 163–169

  6. D'Souza Meenakshi and Kashi Rajanikanth Nagaraj 2019 Avionics self-adaptive software: Towards formal verification and validation. In: Proceedings of 15th International Conference, ICDCIT 2019, Bhubaneswar, India, volume 11319 of Lecture Notes in Computer Science, Springer, pp. 3–23

  7. Krupitzer C et al 2015 A survey on engineering approaches for self-adaptive systems. Pervasive and Mobile Computing 17(PB): 184–206

    Article  Google Scholar 

  8. Georgeff M P, Pell B, Pollack M E, Tambe M and Wooldridge M 1999 The belief-desire-intention model of agency. In: Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages, ATAL ’98, Springer-Verlag, UK, pp. 1–10

  9. Kashi R N and D’Souza M 2018 Vermillion: A verifiable multiagent framework for dependable and adaptable avionics. Technical report, IIIT - Bangalore, India

  10. SAE-Aerospace. ARP4754A guidelines for development of civil aircraft and systems. Technical report

  11. Huth M and Ryan M 2004 Logic in Computer Science: Modelling and Reasoning About Systems. Cambridge University Press, NY, USA

    Book  Google Scholar 

  12. Salehie M and Tahvildari L 2009 Self-adaptive software: Landscape and research challenges. ACM Transactions on Autonomous and Adaptive Systems 4(2): 14:1–14:42

    Article  Google Scholar 

  13. Kashi Rajanikanth Nagaraj and D'Souza Meenakshi 2019 Mitigating byzantine failures in multi-agent based dependable and adaptable avionics software. In: Proceedings of Third IEEE International Conference on Electrical, Computer and Communication Technologies (IEEE ICECCT 2019), pp. 849–857

  14. Dodd R B 2006 Defence Science, and Technology Organisation (Australia). An analysis of task scheduling for a generic avionics mission computer [electronic resource] / R.B. Dodd. DSTO Fishermens Bend, Vic

  15. Woodcock J and Davies J 1996 Using Z: Specification, Refinement, and Proof. Prentice-Hall, Inc., USA

    MATH  Google Scholar 

  16. RTCA-SC-205 2013 DO-333:Formal Methods Supplement to DO-178C and DO-278A

  17. RTCA-SC205 2011 DO-178C: Software Considerations in Airborne Systems and Equipment Certification

  18. Community Z Tools Project 2013 Standalone czt ide version 1.6.0.201301310424

  19. D’Inverno M, Luck M, Georgeff M, Kinny D and Wooldridge M 2004 The dmars architecture: A specification of the distributed multi-agent reasoning system. Autonomous Agents and Multi-Agent Systems 9(1): 5–53

    Article  Google Scholar 

  20. Georgeff M P and Ingrand F F 1989 Monitoring and control of spacecraft systems using procedural reasoning. In Proceedings of the Space Operations Automation and Robotics Workshop

  21. Ljungberg M and Lucas A 1992 The oasis air traffic management system. In: Proceedings of 2nd Pacific RIM Conference on AI, Seoul, South Korea

  22. Singh D, Sardina S, Padgham L and James G 2011 Integrating learning into a BDI agent for environments with changing dynamics. In: Toby Walsh Craig Knoblock and Sierra Carles, editors, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), AAAI Press, Barcelona, Spain, pp. 2525–2530

    Google Scholar 

  23. Wilkinson C, Lynch J, Bharadwaj R, Woodham K 2016 Verification of adaptive systems. Technical report, Federal Aviation Administration, National Technical Information Services (NTIS), USA

  24. SAE-Aerospace 1996 SAE ARP4761 guidelines and methods for conducting the safety assessment process on civil airborne systems and equipment. Technical report, SAE Aerospace

  25. RTCA-SC205 1982 DO-178B: Software Considerations in Airborne Systems and Equipment Certification

  26. Ball T, Podelski A and Rajamani S K 2001 Boolean and cartesian abstraction for model checking c programs. In: Margaria Tiziana and Yi Wang, editors, Tools and Algorithms for the Construction and Analysis of Systems, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 268–283

    Chapter  Google Scholar 

  27. Clarke E, Grumberg O, Jha S, Lu Y and Veith H 2003 Counterexample-guided abstraction refinement for symbolic model checking. Journal of the ACM 50(5): 752–794

    Article  MathSciNet  Google Scholar 

  28. Cimatti A, Clarke E M, Giunchiglia F and Roveri M 1999 Nusmv: A new symbolic model verifier. In: Proc. of the 11th Int. Conference on Computer Aided Verification, pp. 495–499

  29. Baier C and Katoen J-P 2008 Principles of Model Checking. Cambridge: The MIT Press

    MATH  Google Scholar 

  30. Cummings M L, Bruni S, Mercier S, Mitchell P J 2007 Automation architecture for single operator, multiple uav command and control. Int. C2 J. 1(2): 1–24

    Google Scholar 

  31. Uri Wilensky 2000 Netlogo multi-agent programmable modeling environment

  32. ICAO 2005 Annex 2 to the Convention on International Civil Aviation, Rules of the Air

  33. Hoekstra J M 2001 Designing for Safety:the Free Flight Air Traffic Management Concept NLR TP-2001-313. PhD thesis, Delft University and National Aerospace Laboratory NLR, Netherlands

  34. AAI. Rnav-i (gnss or dme/dme/iru) sids and stars, 2009.

  35. Sutton R S and Barto A G 1998 Introduction to Reinforcement Learning. 1st edition. Cambridge, USA: MIT Press

    MATH  Google Scholar 

  36. FBK-irst, CMU, Univ. of Genova, and Univ. of Trento. NuSMV: a new symbolic model checker, 2015

  37. Schleiss P, Zeller M, Weiss G and Eilers D 2014 Safeadapt - safe adaptive software for fully electric vehicles. In: Proc. of 3rd Conference on Future Automotive Technology (CoFAT)

  38. Dutertre B and Stavridou V 1997 Formal requirements analysis of an avionics control system. IEEE Trans. Softw. Eng. 23(5): 267–278

    Article  Google Scholar 

  39. United States Department Of Transportation. Faa requirements engineering management [rem] handbook, 2009

  40. Schmitt P, Tonin I, Wonnemann C, Jenn E, Leriche S and Hunt J J 2006 A case study of specification and verification using jml in an avionics application. In: Proc. of the 4th Int.l Workshop on Java Technologies for Real-time and Embedded Systems, pp. 107–116

  41. Lepri D, Ábrahám E and Ölveczky P C 2013 A timed CTL model checker for real-time maude. In: CALCO, volume 8089 of Lecture Notes in Computer Science, Springer, pp. 334–339

  42. Donzé A, Maler O, Bartocci E, Nickovic D, Grosu R and Smolka S 2012 On Temporal Logic and Signal Processing. In: Chakraborty S and Mukund M, editors, Automated Technology for Verification and Analysis. ATVA 2012, volume 7561 of Lecture Notes in Computer Science (LNCS), Springer, pp. 92–106

  43. Hallsteinsen S et al 2012 A development framework and methodology for self-adapting applications in ubiquitous computing environments. J. Syst. Softw. 85(12): 2840–2859

    Article  Google Scholar 

  44. Canino J M et al 2012 A multi-agent approach for designing next generation of air traffic systems

  45. Hunter J, Raimondi F, Rungta N and Stocker R 2013 A synergistic and extensible framework for multi-agent system verification. In: Proceedings of AAMAS ’13, pp. 869–876

  46. Evertsz R, Thangarajah J, Yadav N and Ly T 2015 A framework for modelling tactical decision-making in autonomous systems. J. Syst. Softw. 110(C): 222–238

    Article  Google Scholar 

  47. Xia Q, Wang L and Li X 2014 Flight conflict detection algorithm for uav and mav under the whole airspace. J. Inf. Comput. Sci. 11(6): 2069

    Article  Google Scholar 

  48. Baron S and Feehrer C 1985 An analysis of the application of ai to the development of intelligent aids for flight crew tasks. Technical report, NASA Langley Research Center, Hampton, VA., USA

  49. Abeloos A L M, Mulder M and Paassen M M V 2000 The applicability of an adaptive human-machine interface in the cockpit. In: Proc. 19th European Annual Conf. on Human Decision Making and Manual Control

  50. Spirkovska L and Lodha S K 2004 Context-aware intelligent assistant approach to improving pilot’s situational awareness. Technical report, NASA Ames Research Centre, United States

  51. Tsiotras P and Johnson E 2012 Advanced methods for intelligent flight guidance and planning in support of pilot decision making. Technical report, Georgia Institute of Technology

  52. Baomar H and Bentley P J 2016 An intelligent autopilot system that learns flight emergency procedures by imitating human pilots. In: IEEE SSCI, pp. 1–9

  53. Denney R 1996 A comparison of the model-based & algebraic styles of specification as a basis for test specification. SIGSOFT Softw. Eng. Notes 21(5): 60–64

    Article  Google Scholar 

  54. Dennis L A and Farwer B 2008 Gwendolen: A bdi language for verifiable agents. In: Löwe Benedikt, editor, Logic and the Simulation of Interaction and Reasoning, Aberdeen, AISB. AISB’08 Workshop

  55. Bordini R H, Fisher M, Wooldridge M and Visser W 2004 Model checking rational agents. IEEE Intell. Syst. 19(5): 46–52

    Article  Google Scholar 

  56. Raimondi F 2013 Case study description: Avionic scenario. Dagstuhl Rep. 3: 180–184

    Google Scholar 

  57. Iftikhar M U and Weyns D 2012 Formal verification of self-adaptive behaviors in decentralized systems with uppaal: An initial study

  58. Iftikhar M U and Weyns D 2012 A case study on formal verification of self-adaptive behaviors in a decentralized system. In: FOCLASA, volume 91 of EPTCS, pp. 45–62

  59. Bochot T, Virelizier P, Waeselynck H and Wiels V 2009 Model checking flight control systems: the airbus experience. In: ICSE 2009. 31st International Conference on Software Engineering, Companion Volume, IEEE, pp. 18–27

  60. Cofer D and Miller S P 2014 Formal methods case studies for do-333. Technical report, NASA, Langley Research Center, Hampton, Virginia 23681-2199

  61. Webster M, Cameron N, Fisher M and Jump M 2014 Generating certification evidence for autonomous unmanned aircraft using model checking and simulation. J. Aerosp. Inf. Syst. 11(5): 258–279

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rajanikanth Kashi Nagaraj.

Appendix

Appendix

The code artefacts are stored in Bitbucket Repository. The interested reader may write to the first author cited under the title of this paper to get access to the same (for Login and Password).

Link for the web browser (Google Chrome)

https://bitbucket.org/vrmiiitb/vrmrepository/src/master/

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kashi Nagaraj, R., D’Souza, M. A verifiable multi-agent framework for dependable and adaptable avionics. Sādhanā 46, 27 (2021). https://doi.org/10.1007/s12046-020-01538-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-020-01538-4

Keywords

Navigation