Skip to main content
SpringerLink
Log in

Achieving dynamic, multi-commander, multi-mission planning and execution

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The Multi-Agent Distributed Goal Satisfaction (MADGS) system facilitates distributed mission planning and execution in complex dynamic environments with a focus on distributed goal planning and satisfaction and mixed-initiative interactions with the human user. By understanding the fundamental technical challenges faced by our commanders on and off the battlefield, we can help ease the burden of decision-making. MADGS lays the foundations for retrieving, analyzing, synthesizing, and disseminating information to commanders. In this paper, we present an overview of the MADGS architecture and discuss the key components that formed our initial prototype and testbed.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Barbuceanu M, Fox MS (1996) Coordinating multiple agents in the supply chain. In: Proceedings of the Fifth Workshops on Enabling Technology for Collaborative Enterprises(WET ICE’96), IEEE Computer Society Press, pp 134–141

  2. Brown S, Cox MT (1999) Planning for information visualization in mixed-initiative systems. In: Cox MT (ed) Proceedings of the 1999 AAAI-99 Workshop on Mixed-Initiative Intelligence, AAAI Press, Menlo Park, CA, pp 2–10

    Google Scholar 

  3. Bryson J, Decker K, DeLoach SA, Huhns M, Wooldridge M (2001) Agent development tools. In: Intelligent Agents VII–Proceedings of the 7th International Workshop on Agent Theories, Architectures, and Languages (ATAL’2000), 2000 Springer Lecture Notes in AI, Springer Verlag, Berlin

    Google Scholar 

  4. Caldwell B (1995) Managing your inventory. Information WEEK 554:88

    Google Scholar 

  5. Carbonell JG, Blythe J, Etzioni O, Gil Y, Joseph R, Kahn D, Knoblock C, Minton S, Perez A, Reilly S, Veloso MM, Wang X (1992) Prodigy4.0: The manual and tutorial. Technical Report CMU-CS-92-150, Computer Science Department, Carnegie Mellon University

  6. Cox MT (2000) A conflict of metaphors: modeling the planning process. In: Proceedings of 2000 Summer Computer Simulation Conference, 2000. The Society for Computer Simulation, San Diego, CA, pp 666–671

    Google Scholar 

  7. Cox MT (2000) Interfaces for mixed-initiative planning. In: IUI’2000 Workshop on Using Plans in Intelligent User Interfaces, Cambridge, MA, MERL

    Google Scholar 

  8. Cox MT, Edwin G, Balasubramanian K, Elahi M (2001) Multiagent goal transformation and mixed-initiative planning using Prodigy/Agent. In: Proceedings of the 5th World Multiconference on Systemics, Cybernetics and Informatics, vol VII, pp 1–6

  9. Cox MT, Kerkez B, Srinivas C, Edwin G, Archer W (2000) Toward agent-based mixed-initiative interfaces. In: Arabnia HR (ed) Proceedings of the 2000 International Conference on Artificial Intelligence, CSREA, vol. 1 Press, pp 309–315

  10. Cox MT, Veloso MM (1997) Supporting combined human and machine planning: an interface for planning by analogical reasoning. In: Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning, pp 531–540

  11. Cox MT, Veloso MM (1998) Goal transformations in continuous planning. In: Proceedings of the AAAI Fall Symposium on Distributed Continual Planning, AAAI Press

  12. Cox MT, Zhang C (2005) Planning as mixed-initiative goal manipulation. In: Biundo S, Myers K, Rajan K (eds) Proceedings of the Fifteenth International Conference on Automated Planning and Scheduling, AAAI Press, Menlo Park, CA, pp 2282–2291

    Google Scholar 

  13. Cox MT, Elahi M, Cleereman K (2003) A distributed planning approach using multiagent goal transformations. In: Proceedings of the 14th Midwest Artificial Intelligence and Cognitive Science Conference, pp 18–23

  14. DeLoach SA (2001) Analysis and design using mase and agenttool. In: Proceedings of the 12th Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2001), Miami University, Oxford, Ohio, March 31–April 1 2001

    Google Scholar 

  15. DeLoach SA, Matson ET, Li Y (2003) Exploiting agent oriented software engineering in the design of a cooperative robotics search and rescue system. The International Journal of Pattern Recognition and Artificial Intelligence

  16. DeLoach SA, Wood M (2001) Developing multiagent systems with agenttool. In: Intelligent Agents VII - Proceedings of the 7th International Workshop on Agent Theories, Architectures, and Languages (ATAL’2000), 2000 Springer Lecture Notes in AI, Springer Verlag, Berlin

    Google Scholar 

  17. Edwin G, Cox MT (2001) Resource coordination in single agent and multiagent systems. In: Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence, IEEE Computer Society, Los Alamitos, CA, pp 18–24

    Chapter  Google Scholar 

  18. Elahi MM (2003) A distributed planning approach using multiagent goal transformations. Master’s thesis, Wright State University

  19. Elahi MM, Cox MT (2003) User’s manual for Prodigy/Agent, Ver 1.0. Technical Report WSU-CS-03-02, Dept. of Computer Science and Engineering, Wright State University

  20. Finin T, McKay D, Fritzson R (1992) An overview of KQML: a knowledge query and manipulation language. Technical report, Computer Science Department, University of Maryland

  21. Fox MS, Chionglo JF, Barbuceanu M (1993) The integrated supply chain management system Technical report, University of Toronto

  22. Holzmann GJ (1991) Design and Validation of Computer Protocols. Prentice Hall

  23. Holzmann GJ (1997) The model checker SPIN. IEEE Trans Softw Engi 23(5):279–295

    Article  MathSciNet  Google Scholar 

  24. Kataoka N, Koizumi H, Simizu H (1997) Architecture of an autonomous distributed system and verification of implementation as a logistics information management system. In: Proceedings of the 3rd International Workshop on Object-Oriented Real-Time Dependable Systems (WORDS’97)

  25. Kendall E (1988) Agent roles and role models: new abstractions for multiagent system analysis and design. In: Proceedings of the International Workshop on Intelligent Agents in Information and Process Management, Bremen, Germany

    Google Scholar 

  26. Kent GA, Simons WE (1994) Objective-based planning. In: Davis PK (ed), New Challenges for Defense Planning: Rethinking How Much is Enough RAND, pp 59–71

  27. Kerkez B, Cox MT (2000) Planning for the user interface: window characteristics. In: Proceedings of the 11th Midwest Artificial Intelligence and Cognitive Science Conference, AAAI Press, Menlo Park, MA, pp 79–84

    Google Scholar 

  28. Kerkez B, Cox MT, Srinivas C (2000) Planning for the user interface: Window content. In: Arabnia HR (ed), Proceedings of the 2000 International Conference on Artificial Intelligence CSREA Press, vol. 1, pp 345–351

  29. Kramer KH, Minar N, Maes P (1999) Tutorial: mobile software agents for dynamic routing. http://www.media.mit.edu/~nelson/research/routes/sigmobile.ps

  30. Kutanoglu E, Wu SD (1999) An Auction-Theoretic Modeling of Production Scheduling to Achieve Distributed Decision Making. PhD thesis, Dept. of Industrial and Manufacturing Systems Engineering, Lehigh University

  31. Lacey TH (2000) A formal methodology and technique for verifying communication protocols in a multi-agent environment. Afit/eng/00m-12, School of Engineering, Air Force Institute of Technology (AU), Wright-Patterson Air Force Base Ohio, USA

    Google Scholar 

  32. Lacey TH, DeLoach SA (2000) Automatic verification of multiagent conversations. In: Proceedings of the 11th Annual Midwest Artificial Intelligence and Cognitive Science Conference, Fayetteville, Arkansas

    Google Scholar 

  33. Liu J-S, Sycara KP (1997) Coordination of multiple agents for production management. Ann Oper Res 75:235–289

    Article  MATH  Google Scholar 

  34. Luh PB, Hoitomt DJ (1993) Scheduling of manufacturing systems using the lagrangian relaxation technique. IEEE Trans Autom Contr 38:1066–1080

    Article  MathSciNet  Google Scholar 

  35. McDonald JT, Talbert ML, DeLoach SA (2000) Heterogeneous database integration using agent oriented information systems. In: Proceedings of the International Conference on Artificial Intelligence (IC-AI’2000), Monte Carlo Resort, Las Vegas, Nevada June 26–29 2000

  36. O’Malley SA, Self AL, DeLoach SA (2000) Comparing performance of static versus mobile multiagent systems. In: National Aerospace and Electronics Conference (NAECON), Dayton, OH, October 10–12, 2000

  37. Quéinnec P, Padiou G (1993) Flight plan management in a distributed air traffic control system. In: First International Symposium on Autonomous Decentralized Systems (ISADS-93)

  38. Saba GM, Santos E, Jr (2000) The multi-agent distributed goal satisfaction system. In: Proceedings of the International ICSC Symposium on Multi-Agents and Mobile Agents in Virtual Organizations and E-Commerce, pp 389–394

  39. Sadeh NM, Hildum DW, Kjenstad D, Tseng A (1999) Mascot: an agent-based architecture for coordinated mixed-initiative supply chain planning and scheduling. In: Proc. 3rd Int’l. Conf. on Autonomous Agents (Agents’99) Workshop on Agent-Based Decision Support for Managing the Internet-Enabled Supply Chain, Seattle, WA

  40. Santos E, Jr, Zhang F, Luh PB (2001) Multi-agent logistics management. In: Proceedings of the International Conference on Internet Computing (IC ’2001), pp 240–246

  41. Santos E, Jr, Zhang F, Luh PB (2003) Intra-organizational logistics management through multi-agent systems. Electr Comm Res 3:337–364

    Article  Google Scholar 

  42. Self A (2001) Design and specification of dynamic, mobile, and reconfigurable multiagent systems. Afit/eng/01m-11, School of Engineering, Air Force Institute of Technology (AU), Wright-Patterson Air Force Base Ohio, USA

    Google Scholar 

  43. Shen W, Norrie DH (1998) An agent-based approach for manufacturing enterprise integration and supply chain management. In: Jacucci G (ed), Globalization of Manufacturing in the Digital Communications Era of the 21st Century: Innovation, Agility, and the Virtual Enterprise Kluwer Academic Publishers, pp 579–590

  44. Shi L, Chen C-H, Yûcesan E (1999) Simultaneous simulation experiments and nested partition for discrete resource allocation in supply chain management. In: The 1999 Winter Simulation Conference (WSC’99)

  45. Sparkman CH (2001) Transforming analysis models into design models for the multiagent systems engineering methodology. Afit/eng/01m-21, School of Engineering, Air Force Institute of Technology (AU), Wright-Patterson Air Force Base Ohio, USA

    Google Scholar 

  46. Veloso MM, Carbonell JG, Perez A, Borrajo D, Fink E, Blythe J (1995) Integrating planning and learning: The PRODIGY architecture. J Theor Exper Artif Intell 7(1):81–120

    MATH  Google Scholar 

  47. Wagner T, Garvey A, Lesser VR (1997) Design-to-criteria scheduling: Managing complexity through goal-directed satisficing. In: Proceedings of the AAAI Workshop on Building Resource-Bounded Reasoning Systems

  48. Walsh WE, Wellman MP (1998) A market protocol for decentralized task allocation. In: Proceedings of the Third International Conference on Multi-Agent Systems, Paris, France, pp 325–332

  49. Wellman MP (1996) Market-oriented programming: Some early lessons. In: Clearwater S (ed) Market-Based Control: A Paradigm for Distributed Resource Allocation, chapter 4 World Scientific

  50. Yung SK, Yang CC (1999) A new approach to solve supply chain management problem by integrating multi-agent technology and constraint network. In: Proceedings of the 32nd Hawaii International Conference on System Sciences(HICSS-1999), Maui, Hawaii

    Google Scholar 

  51. Zeng DD, Sycara K (1999) Dynamic supply chain structuring for electronic commerce among agents. In: Klusch M, (ed) Intelligent Information Agents, Springer

  52. Zhang C (2002) Cognitive models for mixed-initiative planning. Master’s thesis, Wright State University

  53. Zhang C, Cox MT, Immaneni T (2002) GTrans version 2.1 user manual and reference. Technical Report WSU-CS-02-02, Dept. of Computer Science and Engineering, Wright State University

Download references

Author information

Authors and Affiliations

  1. Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755

    Eugene Santos Jr

  2. Department of Computing and Information Sciences, Kansas State University, Manhattan, KS, 66506-2302

    Scott A. DeLoach

  3. Intelligent Distributed Computing Department, BBN Technologies, Cambridge, MA, 02138

    Michael T. Cox

Authors
  1. Eugene Santos Jr
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Scott A. DeLoach
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael T. Cox
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eugene Santos Jr.

Additional information

Eugene Santos, Jr. received the B.S. degree in mathematics and Computer science and the M.S. degree in mathematics (specializing in numerical analysis) from Youngstown State University, Youngstown, OH, in 1985 and 1986, respectively, and the Sc.M. and Ph.D. degrees in computer science from Brown University, Providence, RI, in 1988 and 1992, respectively. He is currently a Professor of Engineering at the Thayer School of Engineering, Dartmouth College, Hanover, NH, and Director of the Distributed Information and Intelligence Analysis Group (DI2AG). Previously, he was faculty at the Air Force Institute of Technology, Wright-Patterson AFB and the University of Connecticut, Storrs, CT. He has over 130 refereed technical publications and specializes in modern statistical and probabilistic methods with applications to intelligent systems, multi-agent systems, uncertain reasoning, planning and optimization, and decision science. Most recently, he has pioneered new research on user and adversarial behavioral modeling. He is an Associate Editor for the IEEE Transactions on Systems, Man, and Cybernetics: Part B and the International Journal of Image and Graphics.

Scott DeLoach is currently an Associate Professor in the Department of Computing and Information Sciences at Kansas State University. His current research interests include autonomous cooperative robotics, adaptive multiagent systems, and agent-oriented software engineering. Prior to coming to Kansas State, Dr. DeLoach spent 20 years in the US Air Force, with his last assignment being as an Assistant Professor of Computer Science and Engineering at the Air Force Institute of Technology. Dr. DeLoach received his BS in Computer Engineering from Iowa State University in 1982 and his MS and PhD in Computer Engineering from the Air Force Institute of Technology in 1987 and 1996.

Michael T. Cox is a senior scientist in the Intelligent Distributing Computing Department of BBN Technologies, Cambridge, MA. Previous to this position, Dr. Cox was an assistant professor in the Department of Computer Science & Engineering at Wright State University, Dayton, Ohio, where he was the director of Wright State’s Collaboration and Cognition Laboratory. He received his Ph.D. in Computer Science from the Georgia Institute of Technology, Atlanta, in 1996 and his undergraduate from the same in 1986. From 1996 to 1998, he was a postdoctoral fellow in the Computer Science Department at Carnegie Mellon University in Pittsburgh working on the PRODIGY project. His research interests include case-based reasoning, collaborative mixed-initiative planning, intelligent agents, understanding (situation assessment), introspection, and learning. More specifically, he is interested in how goals interact with and influence these broader cognitive processes. His approach to research follows both artificial intelligence and cognitive science directions.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Santos, E., DeLoach, S.A. & Cox, M.T. Achieving dynamic, multi-commander, multi-mission planning and execution. Appl Intell 25, 335–357 (2006). https://doi.org/10.1007/s10489-006-0111-2

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-006-0111-2

Keywords

Search

Navigation