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Intelligent Decision Technology Support in Practice pp 141–168Cite as

\(\mathrm {AC}^{3}\)M: The Agent Coordination and Cooperation Cognitive Model

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 42))

Abstract

Coordination is the management of the interdependencies of activities, whereas cooperation is where two parties form a voluntary relationship to share resources for a goal. With the introduction of Intelligent Multi-Agent System (I-MAS), Coordination and Cooperation (\(\mathrm {CO}\mathrm {-}\mathrm {O}^{2}\)) is required to ensure agents act coherently. This ensures that agent tasks and actions are completed in a timely manner. Current \(\mathrm {CO}{-}\mathrm {O}^{2}\) models are ubiquitous and rigid. I-MAS have matured allowing agents to exhibit personification and cognitive processes. This now renders tradition agent coordination and cooperation models useless. The Agent Coordination and Cooperation Cognitive Model (\(\mathrm {AC}^3\)M), is a first generation of hybrid \(\mathrm {CO}{-}\mathrm {O}^{2}\) models which exploits an agent’s cognitive and mental processes to establish a link between \(\mathrm {CO}{-}\mathrm {O}^{2}\). The methodology and design of \(\mathrm {AC}^3\)M focuses on how the organisational structure, environment and mental modelling of an intelligent agent can affect \(\mathrm {CO}{-}\mathrm {O}^{2}\). The implementation of \(\mathrm {AC}^3\)M features three frameworks, which enhances the link between \(\mathrm {CO}{-}\mathrm {O}^{2}\). Each of \(\mathrm {AC}^3\)M’s framework also assist in establishing the importance of perception, situation awareness and decision-making in \(\mathrm {CO}{-}\mathrm {O}^{2}\) methodologies. The application of \(\mathrm {AC}^3\)M is particularly successful in team automation within dynamic environments. Teaming in an unknown or dynamic environment heavily relies on how effectively \(\mathrm {CO}{-}\mathrm {O}^{2}\) is established.

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References

  1. Guilford, J.: The Nature of Human Intelligence. McGraw-Hill (1967)

    Google Scholar 

  2. Simon, H.: Studying human intelligence by creating artificial. Am. Sci. 69(3), 300–309 (1981)

    Google Scholar 

  3. Bass, B.: Cognitive, social and emotional intelligence of transformational leaders. In: Riggio, R., Murphy, S., Priozzolo, F. (eds.) Multiple Intelligences and Leadership, pp. 105–119. Lawrence Erlbaum Associates (2002)

    Google Scholar 

  4. Salovey, P., Deteiler-Bedell, B., Deteiler-Bedell, J., Mayer, J.: Emotional intelligence. In: Lewis, M., Haviland-Jones, J.M., Feldman Barrett, L. (eds.) Handbook of Emotions, pp. 533–548. Guilford Press (2008)

    Google Scholar 

  5. Thorndike, E.: Animal intelligence: an experimental study of the associate processes in animals. Am. Psychol. 53(10), 1125–1127 (1998)

    Article  Google Scholar 

  6. Kirn, S.: Organisational intelligence and distributed ai. Technical report, Institute of Business Informatics, University of Munster (1995)

    Google Scholar 

  7. van Aart, C., Wielinga, B., Schreiber, G.: Organizational building blocks for design of distributed intelligent system. Int. J. Hum. Comput. Stud. 61(5), 567–599 (2004)

    Article  Google Scholar 

  8. Perrow, C.: An organizational analysis of organizational theory. Contemp. Sociol. 29(3), 469–476 (2000)

    Article  Google Scholar 

  9. Carley, K., Gasser, L.: Computational organization theory. In: Wiess, G. (eds.) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, pp. 299–330. The MIT Press (2000)

    Google Scholar 

  10. Carley, K.: Computational and mathematical organization theory: perspective and directions. Comput. Math. Organ. Theor. 1(1), 39–56 (1995)

    Article  Google Scholar 

  11. Prietula, M., Carley, K.: Computational organization theory: autonomous agents and emergent behavior. J. Organ. Comput. 4(1), 41–83 (1994)

    Google Scholar 

  12. Eduardo, L., Herna, C.: On distributed artificial intelligence. Knowl. Eng. Rev. 3, 21–57 (1988)

    Article  Google Scholar 

  13. Moulin, B., Chaibdraa, B.: An overview of distributed artificial intelligence. In O’Hare, G., Jennings, N. (eds.) Foundations of Distributed Artificial Intelligence, pp. 3–55. John Wiley (1996)

    Google Scholar 

  14. Doran, J.: Distributed Al and its Applications. In: Advanced Topics in Artificial Intelligence, pp. 368–372. Springer (1992)

    Google Scholar 

  15. Gasser, L.: Distributed artificial intelligence. AI Expert 4(7), 26–33 (1989)

    Google Scholar 

  16. Bond, A., Gasser, L.: Readings in Distributed Artificial Intelligence. Morgan Kaufmann (1988)

    Google Scholar 

  17. Consoli, A., Tweedale, J., Jain, L.: Aligning cognitive models using ac\(^{3}\)m. In: ICETET ’08: First International Conference on Emerging Trends in Engineering and Technology, pp. 882 – 886. IEEE Computer Society (2008)

    Google Scholar 

  18. Nwana, H.: Software agents: an overview. Knowl. Eng. Rev. 11(3) (1996)

    Google Scholar 

  19. Wooldridge, M.: Intelligent agents: the key concepts. In: Maik, V., Tpankova, O., Krautwurmova, H., Luck, M. (eds.) Multi-Agent Systems and Applications II, vol. 2322 of Lecture Notes in Computer Science, pp. 3–43. Springer (2002)

    Google Scholar 

  20. Wooldridge, M.: An Introduction to Multi-Agent Systems. Wiley, West Sussex, England (2002)

    Google Scholar 

  21. Wooldridge, M., Jennings, N.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)

    Article  Google Scholar 

  22. Consoli, A.: Enhancing team automation in dynamic environments, Ph.D. thesis. School of Electrical and Information Engineering, University of South Australia (2009)

    Google Scholar 

  23. Bergenti, F., Huhns, M.: On the use of agents as components of software systems. In: Bergenti, F., Gleizes, M.P., Zambonelli, F. (eds.) Methodologies and Software Engineering for Agent Systems, pp. 19–31. Kluwer (2004)

    Google Scholar 

  24. Bratman, M.: Intention, Plans and Practical Reasoning. Harvard University (1987)

    Google Scholar 

  25. Bratman, M., Israel, D., Pollack, M.: Plans and resource-bounded practical reasoning. Comput. Intell. 4(4), 349–355 (1988)

    Article  Google Scholar 

  26. Bratman, M.E.: Intention and personal policies. Philos. Perspect. 3, 443–469 (1989)

    Article  Google Scholar 

  27. Omicini, A.: From objects to agent societies: abstractions and methodologies for the engineering of open distributed systems. In: Corradi, A., Omicini, A., Poggi, A. (eds.) WOA 2000: 1st Joint Workshop “From Objects to Agents”: Evolutive Trends of Software Systems, pp. 29–34. Pitagora Editrice Bologna, Parma, Italy, 29–30 (May 2000)

    Google Scholar 

  28. Henderson-Sellers, B., Giorgini, P.: Agent-oriented methodlogies: an introduction. In: Henderson-Sellers, B., Giorgini, P. (eds.) Agent-Oriented Technologies, pp. 1–19. Idea Group (2005)

    Google Scholar 

  29. Iglesias, C.A., Garijo, M., Centeno-González, J.: A survey of agent-oriented methodologies. In: ATAL’98: 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages, pp. 317–330. Springer, London, UK (1999)

    Google Scholar 

  30. Singh, M.: Agent-based abstractions for software development. In: Bergenti, F., Gleizes, M.P., Zambonelli, F. (eds.) Methdologies and Software Engineering for Agent Systems, pp. 5–18. Kluwer (2004)

    Google Scholar 

  31. Consoli, A., Tweedale, J., Jain, L.: An architecture for agent coordination and cooperation. In: Apolloni, B., Howlett, R.J., Jain, L.C. (eds.) KES’2007: 11th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, pp. 934–940. Springer, Vietre sul Mare, Italy, 12–14 Sept 2007

    Google Scholar 

  32. Consoli, A., Tweedale, J., Jain, L.: Defining a decision-support framework in ac\(^{3}\)m. In: Nakamatsu, K., Phillips-Wren, G., Jain, L., Howlett, R. (eds.) IDT’09: First KES International Symposium in Intelligent Decision Technologies, pp. 203–212 (2009)

    Google Scholar 

  33. Bocchi, L., Ciancarini, P.: A perspective on multiagent coordination models. In: Huget M.-P. (ed.) Communications in Multiagent Systems. Volume 2650 of Lecture Notes in Computer Science, pp. 146–163. Springer (2003)

    Google Scholar 

  34. Busi, N., Ciancarini, P., Gorrieri, R., Zavattaro, G.: Coordination model: a guided tour. In: Omicini, A., Zambonelli, F., Klusch, M., Tolksdorf, R. (eds.) Coordination of Internet Agents: Models, Technologies and Applications. Lecture Notes in Computer Science, pp. 6–23. Springer (2000)

    Google Scholar 

  35. Denti, E., Omicini, A., Ricci, A.: Coordination tools for the development of agent-based systems. In: Trappl, R. (ed.) EMCSR 2002: 16th European Meeting on Cybernetics and Systems Research, pp. 671–676. Austrian Society of Cybernetic Studies, Vienna, Austria (2002)

    Google Scholar 

  36. Consoli, A., Tweedale, J., Jain, L.: The link between agent coordination and cooperation. In: Shi, Z., Shimohara, K., Feng, D. (eds.) ICIIP2006—3rd International Conference on Intelligent Information Processing, vol. 228, pp. 11–19. Adelaide, South Australia, Australia, Springer (2006)

    Google Scholar 

  37. Consoli, A., Tweedale, J., Jain, L.: An overview of agent coordination and cooperation. In: Gabrys, B., Hewlett, R., Jain, L. (eds.) KES2006: 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (2006)

    Google Scholar 

  38. Nii, H.: The blackboard model of problem solving. AI Mag. 7(2), 38–53 (1986)

    Google Scholar 

  39. Smith, R.: The contract net protocol: high-level communication and control in a distributed problem solver. IEEE Trans. Comput. 29(12), 1104–1113 (1980)

    Article  Google Scholar 

  40. Durfee, E., Lesser, V., Corkill, D.: Trends in cooperative distributed problem solving. IEEE Trans. Knowl. Data Eng. 1(1), 63–83 (1989)

    Article  Google Scholar 

  41. Wooldridge, M., Jennings, N.: The cooperative problem-solving process. J. Logic Comput. 9(4), 563–592 (1999)

    Article  MathSciNet  Google Scholar 

  42. Ramesh, V., Glass, R., Vessey, I.: Research in computer science: an empirical study. J. Syst. Softw. 70, 165–176 (2004)

    Article  Google Scholar 

  43. Zambonelli, F., Jennings, N., Wooldridge, M.: Developing multiagent systems: the gaia methodology. ACM Trans. Softw. Eng. Methodol. 12(3), 317–370 (2003)

    Article  Google Scholar 

  44. Zambonelli, F., Jennings, N., Wooldridge, M.: Multi-agent systems as computational organizations: the gaia methodology. In: Henderson-Sellers, B., Giorgini, P. (eds.) Agent-Oriented Methodologies, pp. 136–171. Idea Group (2005)

    Google Scholar 

  45. Parasuraman, R., Riley, V.: Humans and automation: use, misuse, disuse, abuse. Source: Human Factors: J. Hum. Factors Ergon. Soc. 39(2), 230–253 (1997)

    Google Scholar 

  46. Sheridan, T., Parasuraman, R.: Human-automation interaction. Rev. Hum. Factors Ergon. 1, 89–129 (2005)

    Article  Google Scholar 

  47. Cuevas, H., Fiore, S., Caldwell, B., Strater, L.: Augmenting team cognition in human-automation teams performing in complex operational environments. Aviat. Space Environ. Med. 78, 63–70 (2007)

    Google Scholar 

  48. Nisser, T.: Human factors challenges in unmanned aerial vehicles (UAVs): A literature review. Technical Report TFHS:05:1, Lund University School of Aviation (2006)

    Google Scholar 

  49. Parasuraman, R., Sheridan, T., Wickens, C.: A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 30(3), 286–297 (2000)

    Article  Google Scholar 

  50. Wickens, C.: Engineering Psychology and Human Performance. Harper Collins (1992)

    Google Scholar 

  51. Endsley, M., Bolstad, C., Jones, D., Riley, J.: Situation awareness oriented design: from user’s cognitive requirements to creating effective supporting technologies. In: Human Factors and Ergonomics Society Annual Meeting: Cognitive Engineering and Decision Making, pp. 268–272 (2003)

    Google Scholar 

  52. Galster, S.: Human automation integration. In: Uninhabited Military Vehicles: Human Factors Issues in Augmenting the Force, pp. 1–108. NATO Research and Technology Organisation (2006)

    Google Scholar 

  53. Schulte, A.: Artificial cognition and cooperative automation. In: Uninhabited Military Vehicles: Human Factors Issues in Augmenting the Force. NATO Research and Technology Organisation (2006)

    Google Scholar 

  54. McCarley, J., Wickens, C.: Human factors implications of UAVs in the national airspace. Technical Report AHFD-05-05/FAA-05-01, Institute of Aviation, University of Illinois (2005)

    Google Scholar 

  55. Tweedale, J.: Fuzzy control loop in an autonomous landing system for unmanned air vehicles. In: WCCI 2012: 2012 IEEE World Congress on Computational Intelligence (2012)

    Google Scholar 

  56. Tweedale, J.: Using mutli-agent systems to improve the level of autonomy for operators controlling unmanned vehicles. In: KES’12: Advances in Knowledge-Based and Intelligent Information and Engineering Systems, pp. 1666–1675. IOS Press, (Sept 2012)

    Google Scholar 

  57. Brooks, R.A.: A robust layered control system for a mobile robot. IEEE J Robot. Autom. 2(1), 14–23 (1986)

    Article  Google Scholar 

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Authors and Affiliations

  1. Defence Science and Technology Organisation, Edinburgh, 5111, Australia

    Angela Consoli

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  1. Angela Consoli
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Correspondence to Angela Consoli .

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Editors and Affiliations

  1. School of Engineering, KES Centre, University of South Australia , Adelaide, South Australia, Australia

    Jeffrey W. Tweedale

  2. FCT Campus, UNINOVA, Caparica, Portugal

    Rui Neves-Silva

  3. School of Engineering, University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  4. Sellinger School of Business and Management, Loyola University Maryland, Baltimore, Maryland, USA

    Gloria Phillips-Wren

  5. Graduate School of IPS, Waseda University, Fukuoka, Japan

    Junzo Watada

  6. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

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Consoli, A. (2016). \(\mathrm {AC}^{3}\)M: The Agent Coordination and Cooperation Cognitive Model. In: Tweedale, J., Neves-Silva, R., Jain, L., Phillips-Wren, G., Watada, J., Howlett, R. (eds) Intelligent Decision Technology Support in Practice. Smart Innovation, Systems and Technologies, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-21209-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-21209-8_9

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