Inferential Reasoning for Heterogeneous Multi-Agent Mission

We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.




References:
[1] S. Yusuf and C. Baber, “Handling Uncertainties in Distributed Constraint Optimization Problems using Bayesian Inferential Reasoning - ICAART 2020.” http://www.insticc.org/node/TechnicalProgram/icaart/presentationDetails/91571 (accessed Feb. 26, 2020).
[2] S. M. Yusuf and C. Baber, “Human-agents Interactions in Multi-Agent Systems: A Case Study of Human-UAVs Team for Forest Fire Lookouts - ICAART 2020.” http://www.insticc.org/node/TechnicalProgram/icaart/presentationDetails/93692 (accessed Feb. 29, 2020).
[3] T. Altameem and M. Amoon, “An agent-based approach for dynamic adjustment of scheduled jobs in computational grids,” J. Comput. Syst. Sci. Int., vol. 49, no. 5, pp. 765–772, Oct. 2010, doi: 10.1134/S1064230710050114.
[4] M. Yokoo, E. H. Durfee, T. Ishida, and K. Kuwabara, “The distributed constraint satisfaction problem: formalization and algorithms,” IEEE Transactions on Knowledge and Data Engineering, vol. 10, no. 5, pp. 673–685, Sep. 1998, doi: 10.1109/69.729707.
[5] M. Turpin, N. Michael, and V. Kumar, “Capt: Concurrent assignment and planning of trajectories for multiple robots,” The International Journal of Robotics Research, vol. 33, no. 1, pp. 98–112, Jan. 2014, doi: 10.1177/0278364913515307.
[6] “Information Exchange and Decision Making in Micro Aerial Vehicle Networks for Cooperative Search - IEEE Journals & Magazine.” https://ieeexplore.ieee.org/document/7097008 (accessed May 03, 2019).
[7] J. Qin, W. X. Zheng, and H. Gao, “Coordination of Multiple Agents With Double-Integrator Dynamics Under Generalized Interaction Topologies,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 1, pp. 44–57, Feb. 2012, doi: 10.1109/TSMCB.2011.2164523.
[8] S. M. Yusuf and C. Baber, “Conflicts Resolution and Situation Awareness in Heterogeneous Multi-agent Missions using Publish-subscribe Technique and Inferential Reasoning - ICAART 2020.” http://www.insticc.org/node/TechnicalProgram/icaart/presentationDetails/91474 (accessed Feb. 29, 2020).
[9] G. Pavlin, P. de Oude, M. Maris, J. Nunnink, and T. Hood, “A multi-agent systems approach to distributed bayesian information fusion,” Information Fusion, vol. 11, no. 3, pp. 267–282, Jul. 2010, doi: 10.1016/j.inffus.2009.09.007.
[10] Y. Xiang, Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach. New York, NY, USA: Cambridge University Press, 2002.
[11] T. Setter and M. Egerstedt, “Energy-Constrained Coordination of Multi-Robot Teams,” IEEE Transactions on Control Systems Technology, vol. 25, no. 4, pp. 1257–1263, Jul. 2017, doi: 10.1109/TCST.2016.2599486.
[12] X. Zhou, W. Wang, W. Tao, L. Xiaboo, and J. Tian, “Continuous patrolling in uncertain environment with the UAV swarm,” 2018. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0202328 (accessed Aug. 18, 2019).
[13] Y. Xiang, “Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach,” Cambridge Core, Aug. 2002. /core/books/probabilistic-reasoning-in-multiagent-systems/C78168FDA67EF5E2EEBB9C63AC70EAD2 (accessed Nov. 07, 2019).
[14] G. Pavlin, P. de Oude, M. Maris, J. Nunnink, and T. Hood, “A multi-agent systems approach to distributed bayesian information fusion,” Information Fusion, vol. 11, no. 3, pp. 267–282, Jul. 2010, doi: 10.1016/j.inffus.2009.09.007.
[15] J. Fransman, J. Sijs, H. Dol, E. Theunissen, and B. De Schutter, “Bayesian-DPOP for Continuous Distributed Constraint Optimization Problems,” in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, Richland, SC, 2019, pp. 1961–1963, Accessed: Nov. 06, 2019. (Online). Available: http://dl.acm.org/citation.cfm?id=3306127.3331977.
[16] J. Wang and Z. Xu, “Bayesian Inferential Reasoning Model for Crime Investigation,” p. 11, 2014.
[17] J. Williamson, “Bayesian Networks for Logical Reasoning,” p. 19, 2001.
[18] S. Mandt and M. D. Hoffman, “Stochastic Gradient Descent as Approximate Bayesian Inference,” p. 35, 2017.
[19] M. Romanycia, “Netica-J Reference Manual,” p. 119, 2019.
[20] M. Georgeff, B. Pell, M. Pollack, M. Tambe, and M. Wooldridge, “The Belief-Desire-Intention Model of Agency,” in Intelligent Agents V: Agents Theories, Architectures, and Languages, vol. 1555, J. P. Müller, A. S. Rao, and M. P. Singh, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999, pp. 1–10.
[21] S. Makonin, D. McVeigh, W. Stuerzlinger, K. Tran, and F. Popowich, “Mixed-Initiative for Big Data: The Intersection of Human + Visual Analytics + Prediction,” in 2016 49th Hawaii International Conference on System Sciences (HICSS), Jan. 2016, pp. 1427–1436, doi: 10.1109/HICSS.2016.181.
[22] C. Rich and C. L. Sidner, “DiamondHelp: A Generic Collaborative Task Guidance System,” 1, vol. 28, no. 2, pp. 33–33, Jun. 2007, doi: 10.1609/aimag.v28i2.2038.
[23] R. F. Stark, M. Farry, and J. Pfautz, “Mixed-initiative data mining with Bayesian networks,” in 2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support, Mar. 2012, pp. 107–110, doi: 10.1109/CogSIMA.2012.6188360.
[24] E. Yanmaz, S. Yahayanajeed, and R. Bernerd, “Drone Networks: Communications, Coordination, and Sensing,” Elsevier, Sep. 2017.
[25] J. Cortés and M. Egerstedt, “Coordinated Control of Multi-Robot Systems: A Survey,” SICE Journal of Control, Measurement, and System Integration, vol. 10, no. 6, pp. 495–503, 2017, doi: 10.9746/jcmsi.10.495.
[26] J. P. Desai, J. Ostrowski, and V. Kumar, “Controlling formations of multiple mobile robots,” 1998, vol. 4, pp. 2864–2869 vol.4, doi: 10.1109/ROBOT.1998.680621.
[27] G. Ferguson and J. Allen, “Mixed-Initiative Systems for Collaborative Problem Solving,” 1, vol. 28, no. 2, pp. 23–23, Jun. 2007, doi: 10.1609/aimag.v28i2.2037.
[28] K. Ghamry and Y. Zhang, “Cooperative control of multiple UAVs for forest fire monitoring and detection,” Aug. 2016, pp. 1–6, doi: 10.1109/MESA.2016.7587184.
[29] G. Vásárhelyi et al., “Outdoor flocking and formation flight with autonomous aerial robots,” Feb. 2014, Accessed: Apr. 21, 2019. (Online). Available: https://arxiv.org/abs/1402.3588v2.
[30] A. Weinstein, A. Cho, G. Loianno, and V. Kumar, “Visual Inertial Odometry Swarm: An Autonomous Swarm of Vision-Based Quadrotors,” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 1801–1807, Jul. 2018, doi: 10.1109/LRA.2018.2800119.
[31] M. M. Khan, “Speeding up GDL-based distributed constraint optimization algorithms in cooperative multi-agent systems,” phd, University of Southampton, 2018.
[32] M. Vasile and F. Zuiani, “Multi-agent collaborative search : an agent-based memetic multi-objective optimization algorithm applied to space trajectory design,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 225, pp. 1211–1227, Nov. 2011.
[33] A. Khan, E. Yanmaz, and B. Rinner, “Information Exchange and Decision Making in Micro Aerial Vehicle Networks for Cooperative Search,” IEEE Transactions on Control of Network Systems, vol. 2, no. 4, pp. 335–347, Dec. 2015, doi: 10.1109/TCNS.2015.2426771.
[34] A. Khan, E. Yanmaz, and B. Rinner, “Information merging in multi-UAV cooperative search,” in 2014 IEEE International Conference on Robotics and Automation (ICRA), May 2014, pp. 3122–3129, doi: 10.1109/ICRA.2014.6907308.
[35] S. Grayson, “Search & Rescue using Multi-Robot Systems,” 2014.
[36] S. Rabinovich, R. E. Curry, and G. H. Elkaim, “Toward Dynamic Monitoring and Suppressing Uncertainty in Wildfire by Multiple Unmanned Air Vehicle System,” Journal of Robotics, 2018. https://www.hindawi.com/journals/jr/2018/6892153/ (accessed Aug. 25, 2019).
[37] G. Bevacqua, J. Cacace, A. Finzi, and V. Lippiello, “Mixed-initiative Planning and Execution for Multiple Drones in Search and Rescue Missions,” in Proceedings of the Twenty-Fifth International Conference on International Conference on Automated Planning and Scheduling, Jerusalem, Israel, 2015, pp. 315–323, Accessed: Feb. 19, 2019. (Online). Available: http://dl.acm.org/citation.cfm?id=3038662.3038706.
[38] J. Cacace, A. Finzi, and V. Lippiello, “A mixed-initiative control system for an Aerial Service Vehicle supported by force feedback,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sep. 2014, pp. 1230–1235, doi: 10.1109/IROS.2014.6942714.
[39] J. Hu, L. Xie, K. Lum, and J. Xu, “Multiagent Information Fusion and Cooperative Control in Target Search,” IEEE Transactions on Control Systems Technology, vol. 21, no. 4, pp. 1223–1235, Jul. 2013, doi: 10.1109/TCST.2012.2198650.
[40] M. Kohlert and A. König, “Large, high-dimensional, heterogeneous multi-sensor data analysis approach for process yield optimization in polymer film industry,” Neural Comput & Applic, vol. 26, no. 3, pp. 581–588, Apr. 2015, doi: 10.1007/s00521-014-1654-5.
[41] L. Merino, O. Caballero, J. R. Martínez-de-dios, and I. Maza, Automatic Forest Fire Monitoring and Measurement using Unmanned Aerial Vehicles. .
[42] https://github.com/afrl-rq/OpenAMASE. afrl-rq, 2019.
[43] M. Chawla and M. Duhan, “Levy Flights in Metaheuristics Optimization Algorithms – A Review,” Applied Artificial Intelligence, vol. 32, no. 9–10, pp. 802–821, Nov. 2018, doi: 10.1080/08839514.2018.1508807.
[44] S. G. Nurzaman, Y. Matsumoto, Y. Nakamura, S. Koizumi, and H. Ishiguro, “Biologically inspired adaptive mobile robot search with and without gradient sensing,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA, Oct. 2009, pp. 142–147, doi: 10.1109/IROS.2009.5353998.
[45] D. K. Sutantyo, S. Kernbach, V. A. Nepomnyashchikh, and P. Levi, “Multi-Robot Searching Algorithm Using Levy Flight and Artificial Potential Field,” arXiv:1108.5624 [cs], Aug. 2011, Accessed: May 25, 2019. (Online). Available: http://arxiv.org/abs/1108.5624.
[46] J. Q. Hale and E. Zhou, “A Model-based Approach to Multi-objective Optimization,” in Proceedings of the 2015 Winter Simulation Conference, Piscataway, NJ, USA, 2015, pp. 3599–3609, Accessed: Aug. 02, 2019. (Online). Available: http://dl.acm.org/citation.cfm?id=2888619.2889103.
[47] A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 39, no. 1, pp. 1–38, 1977.