A Conceptual Model of Layered Adjustable Autonomy

  • Salama A. Mostafa
  • Mohd Sharifuddin Ahmad
  • Muthukkaruppan Annamalai
  • Azhana Ahmad
  • Saraswathy Shamini Gunasekaran
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 206)

Abstract

Autonomy and autonomous agents are currently the most researched topics in autonomous systems. Issues like autonomy adjustment, autonomy level, and the required degree of autonomy to be performed are investigated. Abstracting an autonomy model poses the problem of identifying specific aspects that merit an autonomous system. In this paper, we propose another model of autonomy that conceptualizes autonomy as a spectrum, which is constructed in a layered structure of a multi-agent environment called Layered Adjustable Autonomy (LAA). The autonomy spectrum of the LAA is divided into adjustable-leveled layers. Each of which has distinct attributes and properties that assist an agent in managing the influences of the environment during its decision-making process. The LAA structure is designed to endorse an agent’s qualification to make a decision by setting the degree of autonomy to the agent’s choice of decision-making. An Autonomy Analysis Module (AAM) is also proposed to control and delegate the agent’s actions at specific autonomy levels. Hence, the AAM determines the threshold of the agent autonomy level to act in its qualified layer. Ultimately, the proposed LAA model will be implemented on an air drone for the purpose of testing and refinement.

Keywords

Software agent Multi-agent system (MAS) adjustable autonomy autonomous systems Layered Adjustable Autonomy (LAA) decision-making 
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References

  1. 1.
    Cesta, A., D’Aloisi, D., Collia, M.: Adjusting Autonomy of Agent Systems. AAAI Technical Report SS-99-06 (1999)Google Scholar
  2. 2.
    Myers, K.L., Morley, D.N.: Human Directability of Agents. In: K-CAP 2001. ACM 1-58113-380 (2001)Google Scholar
  3. 3.
    Ghallab, M., Nau, D.S., Traverso, P.: Automated planning: Theory and practice. Morgan Kaufmann, San Mateo (2004)MATHGoogle Scholar
  4. 4.
    Nau, D.S.: Current trends in automated planning. AI Magazine 28(4), 43–58 (2007)Google Scholar
  5. 5.
    Bradshaw, J.M., Feltovich, P.J., Jung, H., Kulkarni, S., Taysom, W., Uszok, A.: Dimensions of Adjustable Autonomy and Mixed-Initiative Interaction. In: Nickles, M., Rovatsos, M., Weiss, G. (eds.) AUTONOMY 2003. LNCS (LNAI), vol. 2969, pp. 17–39. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Scerri, P., Reed, N.: Designing agents for systems with adjustable autonomy. In: The IJCAI 2001 Workshop on Autonomy, Delegation, and Control: Interacting with Autonomous Agents (2001)Google Scholar
  7. 7.
    Brainov, S., Hexmoor, H.: Quantifying Relative Autonomy in Multiagent Interaction. In: Hexmoor, H., Castelfranchi, C., Falcone, R. (eds.) Agent Autonomy, pp. 55–74. Kluwer, Dordrecht (2002)Google Scholar
  8. 8.
    Van der Vecht, B., Dignum, F., Meyer, J.-J.C., Neef, M.: A dynamic coordination mechanism using adjustable autonomy. In: Proc. COIN@MALLOW 2007, Durham, UK (2007)Google Scholar
  9. 9.
    Rogers, A., Ramchurn, S.D., Jennings, N.R.: Delivering the smart grid: Challenges for autonomous agents and multi-agent systems research. In: Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2012), Toronto, CA, July 22-26, pp. 2166–2172 (2012)Google Scholar
  10. 10.
    Lili, Y., Rubo, Z., Hengwen, G.: Situation reasoning for an adjustable autonomy system. International Journal of Intelligent Computing and Cybernetics 5(2), 226–238 (2012)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Dumond, D., Ayers, J., Schurr, N., Carlin, A., Burke, D., Rousseau, J.: Coordinating with Humans by Adjustable-Autonomy for Multirobot Pursuit (CHAMP). Unmanned Systems Technology XIV. In: Karlsen, R.E., Gage, D.W., Shoemaker, C.M., Gerhart, G.R. (eds.) Proceedings of the SPIE, The Smithsonian/NASA Astrophysics Data System, vol. 8387, pp. 838703–838703-15 (2012)Google Scholar
  12. 12.
    Bradshaw, J.M., Sierhuis, M., Acquisti, A., Feltovich, P., Hoffman, R., Jeffers, R., Prescott, D., Suri, N., Uszok, A., Van Hoof, R.: Adjustable Autonomy and Human-Agent Teamwork in Practice: An Interim Report on Space Applications. In: Hexmoor, H., Falcone, R., Castelfranchi, C. (eds.) Agent Autonomy, pp. 243–280. Kluwer (2003)Google Scholar
  13. 13.
    Sierhuis, M., Bradshaw, J.M., Acquisti, A., Van Hoof, R., Jeffers, R., Uszok, A.: Human-Agent Teamwork and Adjustable Autonomy in Practice. In: Proceeding of the 7th International Symposium on Artificial Intelligence, Robotics and Automation in Space: i-SAIRAS, NARA, Japan, May 19-23 (2003)Google Scholar
  14. 14.
    Dorais, G., Bonasso, R.P., Kortenkamp, D., Pell, B., Schrekenghost, D.: Adjustable Autonomy for Human-centered Autonomous Systems on Mars. In: Proceedings of the AAAI Spring Symposium on Agents with Adjustable Autonomy, AAAI Technical Report SS-99-06. AAAI Press, Menlo Park (1999)Google Scholar
  15. 15.
    Cohen, R., Fleming, M.: Adjusting the Autonomy in Mixed-initiative Systems by Reasoning about Interaction. Multiagent Systems, Artificial Societies, and Simulated Organizations 7, 137–158 (2003)Google Scholar
  16. 16.
    Schurr, N., Marecki, J., Tambe, M.: Improving Adjustable Autonomy Strategies for Time-Critical Domains. In: The 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009). International Foundation for Autonomous Agents and Multiagent Systems, Budapest, Hungary, pp. 353–360 (May 2009)Google Scholar
  17. 17.
    Brooks, R.A.: A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation 2(1), 14–23 (1986)CrossRefGoogle Scholar
  18. 18.
    Ball, M., Callaghan, V.: Explorations of Autonomy: An Investigation of Adjustable Autonomy in Intelligent Environments. In: The Eighth International Conference on Intelligent Environments, pp. 114–121. IEEE Press, Guanajuato (2012)Google Scholar
  19. 19.
    Mohammed, K.A., Ahmad, M.S., Mostafa, S.A., Sharifuddin, F.M.A.M.: A Nodal Approach to Modeling Human-Agents Collaboration. International Journal of Computer Applications, Foundation of Computer Science 43(12), 33–40 (2012)Google Scholar
  20. 20.
    Bradshaw, J.M., Jung, H., Kulkarni, S., Johnson, M., Feltovich, P., Allen, J., Bunch, L., Chambers, N., Galescu, L., Jeffers, R., Suri, N., Taysom, W., Uszok, A.: Kaa: Policy-based Explorations of a Richer Model for Adjustable Autonomy. In: AAMAS 2005, Utrecht, Netherlands, July 25-29 (2005)Google Scholar
  21. 21.
    Wooldridge, M.: An Introduction to Multiagent System, 2nd edn. A John Wiley and Sons, Ltd. (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Salama A. Mostafa
    • 1
  • Mohd Sharifuddin Ahmad
    • 2
  • Muthukkaruppan Annamalai
    • 3
  • Azhana Ahmad
    • 2
  • Saraswathy Shamini Gunasekaran
    • 1
  1. 1.College of Graduate StudiesUniversiti Tenaga NasionalSelangor Darul EhsanMalaysia
  2. 2.College of Information TechnologyUniversiti Tenaga NasionalSelangor Darul EhsanMalaysia
  3. 3.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARASelangor Darul EhsanMalaysia

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