To BDI, or not to BDI: design choices in an agent-based traffic flow management simulation

ABSTRACT

Belief-Desire-Intention (BDI) is a powerful agent paradigm that allows for the development of so-called intelligent agents - agents that can reason and act based on their beliefs and intentions. However, this power often comes at the cost of increased computational overhead. We describe our experience using a BDI agent framework for developing a simulation of collaborative air traffic flow management and the efficiency problems we encountered. By using BDI more judiciously in our simulation, we were able to address these issues and greatly reduce the execution time of our simulation. From our successes and failures, we derive several guidelines that may enable other researchers to avoid similar efficiency issues in BDI-based simulations.

REFERENCES

• 1: Federal Aviation Administration, FAA Long-Range Aerospace Forecasts Fiscal Years 2015, 2020 And 2025. 2000.
• 2: Idris, H., et al. Field Observations of Interactions Between Traffic Flow Management and Airline Operations. in AIAA 6th Aviation, Technology, Integration, and Operations Conference (ATIO). 2006. Wichita, Kansas.
• 3: Idris, H., et al. Operational Concept for Collaborative Traffic Flow Management based on Field Observations. in AIAA 5th Aviation, Technology, Integration, and Operations Conference (ATIO). 2005. Arlington, Virginia.
• 4: Levent Yilmaz , Tuncer Ören , Nasser-Ghasem Aghaee, Intelligent agents, simulation, and gaming, Simulation and Gaming, v.37 n.3, p.339-349, September 2006 [doi>10.1177/1046878106289089]
• 5: Sweet, D. N., et al. Fast-Time Simulation System for Analysis of Advanced Air Transportation Concepts. in AIAA Modeling and Simulation Technologies Conference and Exhibit. 2002. Monterey, California.
• 6: Couluris, G. J., et al. National Airspace System Simulation Capturing the Interactions of Air Traffic Management and Flight Trajectories. in AIAA Guidance, Navigation, and Control (GNC) Conference. 2003. Austin, Texas.
• 7: Kevin M. Corker, Human performance simulation in the analysis of advanced air traffic management, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.821-828, December 05-08, 1999, Phoenix, Arizona, United States [doi>10.1145/324138.324508]
• 8: Keith C, C., et al., Modeling Distributed Human Decision-Making in Traffic Flow Management Operations, in 3rd USA/Europe Air Traffic Management R&D Seminar. 2000: Napoli, Italy.
• 9: Kagan Tumer , Adrian Agogino, Distributed agent-based air traffic flow management, Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, May 14-18, 2007, Honolulu, Hawaii [doi>10.1145/1329125.1329434]
• 10: Brendan Hogan , Leonard A. Wojcik, Traffic flow management modeling and operational complexity, Proceedings of the 36th conference on Winter simulation, December 05-08, 2004, Washington, D.C.
• 11: Bilimoria, K., et al. FACET: Future ATM Concepts Evaluation Tool. in 3rd USA/Europe ATM 2001 R&D Seminar. 2000. Napoli, Italy.
• 12: Smith, P., et al., The design of FACET to support use by airline operations centers. 2004, IEEE.
• 13: Flight Explorer, Flight Explorer Inc.: http://www.flightexplorer.com/.
• 14: Shawn R. Wolfe , Francis Y. Enomoto , Peter A. Jarvis , Maarten Sierhuis, Comparing Route Selection Strategies in Collaborative Traffic Flow Management, Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, p.59-62, November 02-05, 2007 [doi>10.1109/IAT.2007.40]
• 15: Sierhuis, M., W. J. Clancey, and R. v. Hoof, Brahms: A multiagent modeling environment for simulating work processes and practices. International Journal of Simulation and Process Modelling, Inderscience Publishers, 2007. 3(3): p. 134--152.
• 16: William J. Clancey, The advantages of abstract control knowledge in expert system design, Stanford University, Stanford, CA, 1983
• 17: William J. Clancey, Heuristic classification, Artificial Intelligence, v.27 n.3, p.289-350, Dec. 1985 [doi>10.1016/0004-3702(85)90016-5]
• 18: William J. Clancey , Patricia Sachs , Maarten Sierhuis , Ron van Hoof, Brahms: simulating practice for work systems design, International Journal of Human-Computer Studies, v.49 n.6, p.831-865, Dec. 1998 [doi>10.1006/ijhc.1998.0229]
• 19: Garcia-Chico, J.-L., et al., Collaboration for Mitigating Local Traffic Flow Management (TFM) Constraints due to Weather, Special Use Airspace (SUA), Complexity, and Arrival Metering: Prototype Algorithms for Collaborative TFM. 2007, L-3 Communications and Metron Aviation: Billerica, MA
• 20: Sierhuis, M., "It's not just goals all the way down" - "It's activities all the way down" in LNAI 4457: Engineering Societies in the Agents World, Seventh International Workshop (ESAW'06). 2007, Springer-Verlag: Dublin, Ireland. p. 1--24.
• 21: Sierhuis, M., Modeling and Simulating Work Practice; Brahms: A multiagent modeling and simulation language for work system analysis and design, in Social Science Informatics (SWI). 2001, University of Amsterdam, SIKS Dissertation Series No. 2001-10: Amsterdam, The Netherlands. p. 350.
• 22: Bordini, R. H., et al., eds. Multi-Agent Programming: Languages, Platforms and Applications. Multiagent Systems, Artificial Societies, and Simulated Organizations, ed. G. Weiss. 2005, Springer Science+Business Media, Inc.: New York, NY.
• 23: John Thangarajah , Lin Padgham , James Harland, Representation and reasoning for goals in BDI agents, Proceedings of the twenty-fifth Australasian conference on Computer science, p.259-265, January 01, 2002, Melbourne, Victoria, Australia
• 24: Michael J. Wooldridge , Nicholas R. Jennings, Software Engineering with Agents: Pitfalls and Pratfalls, IEEE Internet Computing, v.3 n.3, p.20-27, May 1999 [doi>10.1109/4236.769419]

INDEX TERMS

Keywords:
air traffic control, belief-desire-intent, efficiency