Abstract
Various Agent Based Modeling and Simulation software frameworks exist (see Cherniak (Exploring behavioral patterns in complex adaptive systems. PhD thesis, University of Pittsburgh, 2014); Namatame and Chen (Agent based modeling and network dynamics. Oxford University Press, Oxford, 2016); Railsback and Grimm (Agent-based and individual-based modeling. Princeton University Press, Princeton, NJ, 2012)), however, a specific framework was developed here in order to find a set of symbiotic UAS behaviors and UTM policies. The framework is instrumented to allow measurement of crucial features, including local statistics and flow metrics, contingencies (and if possible their causes), and higher-level system features and emergent behaviors. Our previous work on the BRECCIA system (Sacharny et al., Breccia: unified probabilisitic dynamic geospatial intelligence. In IEEE conference on intelligent robots and systems (IROS 2017 Late Breaking Paper), Vancouver, September 2017; Sacharny et al., BRECCIA: a novel multi-source fusion framework for dynamic geospatial data analysis. In IEEE conference on multisensor fusion and integration, Daegu, September 2017) included a BDI-agent based framework built from a Java library called Jason (Bordini et al., Programming multi-agent systems in AgentSpeak using Jason. Wiley, Hoboken, NJ, 2007). However, the ability to rapid-prototype different models of communication, or create complex agents, is inhibited by having to switch between Java and the Jason domain-specific language. To better enable rapid prototyping an object-oriented framework was developed in Matlab.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Ⓒ[2022] IEEE. Reprinted, with permission, from [IEEE-T Intelligent Transportation Systems, “Lane-Based Large-Scale UAS Traffic Management,” David Sacharny, Thomas C. Henderson and Vista Marston, 2022, Print ISSN: 1524–9050, Online ISSN: 1558–0016, Digital Object Identifier: 10.1109/TITS.2022.3160378].
- 2.
As noted by Nagel and Rasmussen [64], this model can be treated analytically [103], but the analytical results are “more difficult to obtain” than measurements from simulation. A result that would support this thesis would show that free-flight systems, the least structured of airspace designs proposed, are more difficult to analyze than lane-based systems and therefore less ideal for contingency handling.
References
R. Alur, Formal verification of hybrid systems, in Proceedings of the Ninth ACM International Conference on Embedded Software (EMSOFT) (ACM Press, New York, 2011), pp. 273–278
P.-L. Bacon, J. Harb, D. Precup, The option-critic architecture, in The Thirty-First AAAI Conference on Artificial Intelligence, San Francisco (2017)
J. Baculi, C. Ippolito, Onboard decision-making for nominal and contingency sUAS flight, in AIAA Scitech 2019 Forum (American Institute of Aeronautics and Astronautics, Reston, 2019)
R.H. Bordini, J.F. Huebner, M. Wooldridge, Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley, Hoboken, 2007)
A. Cherniak, Explorng behavioral patterns in complex adaptive systems. Ph.D. Thesis, University of Pittsburgh (2014)
D. Easley, J. Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World (Cambridge University Press, Cambridge, 2010)
M.R. Jardin, Analytical relationships between conflict counts and air-traffic density. J. Guid. Control Dyn. 28(6), 1150–1156 (2005)
J.B. Kenney, Dedicated short-range communications (DSRC) standards in the united states. Proc. IEEE 99(7), 1162–1182 (2011)
K. Nagel, S. Rasmussen, Traffic at the edge of chaos, in Artificial Life IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems (MIT Press, Cambridge, 1994), pp. 222–235
NASA, Unmanned aircraft systems (UAS) traffic management (UTM) concept of operations, V2.0. Technical Report, Federal Aviation Administration, Washington (2020)
A.S. Rao, AgentSpeack(L): BDI agents speak out in a logical computable language, in Proceedings of the 7th European Workshop on Modeling Autonomous Agents in a Multi-Agent World, ed. by W. Van de Velde, J.W. Perram (Springer, Eindhoven, 1996), pp. 42–55
S. Russell, D. Dewey, M. Tegmark, Research priorities for robust and beneficial artificial intelligence. AI Mag. 36(4), 105–114 (2015)
D. Sacharny, T.C. Henderson, A. Mitiche, R. Simmons, T. Welker, X. Fan, BRECCIA: a multi-agent data fusion and decision support system for dynamic mission planning, in 2nd Conference on Dynamic Data Driven Application Systems (DDDAS 2017), Cambridge (2017)
D. Sacharny, T. Henderson, M. Cline, B. Russon, Reinforcement learning at the cognitive level in a belief, desire, intention UAS agent. Technical Report UUCS-20-013, University of Utah (2020)
A. Schadschneider, M. Schreckenberg, Cellular automation models and traffic flow. J. Phys. A: Math. Gen. 26(15), L679–L683 (1993)
A.S. Vezhnevets, S. Osindero, T. Schaul, N. Heess, M. Jaderberg, D. Silver, K. Kavukcuoglu, FeUdal networks for hierarchical reinforcement learning, in Proceedings of the 34th International Conference on Machine Learning - Volume 70, Sydney. JMLR.org (2017), pp. 3540–3549
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sacharny, D., Henderson, T. (2022). Agent Based Modeling and Simulation. In: Lane-Based Unmanned Aircraft Systems Traffic Management. Unmanned System Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-98574-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-98574-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98573-8
Online ISBN: 978-3-030-98574-5
eBook Packages: EngineeringEngineering (R0)