Cooperative solutions to exploration tasks under speed and budget constraints

References

• Agrawal, P., & Rao, S. October 2014. Energy-aware scheduling of distributed systems. IEEE, Trans. Autom. Sci. Eng. 11 (4): 1163–1175. https://doi.org/10.1109/TASE.2014.2308955)
   Web of Science ®Google Scholar
• Anceaume, E., Cabillic, G., Chevochot, P., & Puaut, I. (1999). A flexible run-time support for distributed dependable hard real-time applications. In Proceedings 2nd ieee international symposium on object-oriented real-time distributed computing (isorc’99)(cat. no. 99-61702) (pp. 310–319).
   Google Scholar
• Ashutosh, K., Consul, S., Dedhia, B., Khirwadkar, P., Shah, S., & Kalyanakrishnan, S. (2020). Lower bounds for policy iteration on multi-action mdps. In 2020 59th ieee conference on decision and control (cdc) (pp. 1744–1749).
   Google Scholar
• Burgard, W., Moors, M., Stachniss, C., & Schneider, F. (2005). Coordinated multi-robot exploration. IEEE Transactions on Robotics, 21(3), 376–386. https://doi.org/10.1109/TRO.2004.839232
   Web of Science ®Google Scholar
• Cares, J. R. (2002). The use of agent-based models in military concept development. Proceedings of the winter simulation conference. (Vol. 1 , pp. 935–939). IEEE.
   Google Scholar
• Carr, P. B., & Walton, G. M. (2014). Cues of working together fuel intrinsic motivation. Journal of Experimental Social Psychology, 53(53), 169–184. https://doi.org/10.1016/j.jesp.2014.03.015
   Google Scholar
• Choi, H., Crump, C., Duriez, C., Elmquist, A., Hager, G., Han, D., Hearl, F., Hodgins, J., Jain, A., Leve, F., Li, C., Meier, F., Negrut, D., Righetti, L., Rodriguez, A., Tan, J., & Trinkle, J. (2021). On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward. Proceedings of the National Academy of Sciences, 118 (1), e1907856118. https://doi.org/10.1073/pnas.1907856118.
   PubMed Web of Science ®Google Scholar
• David, L., Cottet, F., & Nissanke, N. (2001). Jitter control in on-line scheduling of dependent real-time tasks. In Proceedings 22nd ieee real-time systems symposium (rtss 2001) (cat. no. 01pr1420) (pp. 49–58). IEEE.
   Google Scholar
• Dawson, S., Wellman, B. L., & Anderson, M. (2010). Using simulation to predict multi-robot performance on coverage tasks. 2010 ieee/rsj international conference on intelligent robots and systems (pp. 202–208). IEEE.
   Google Scholar
• Dear, R. G., & Sherif, J. S. Using simulation to evaluate resource utilization strategies. (2000). SIMULATION, 74(2), 75–83. https://doi.org/10.1177/003754970007400202
   Google Scholar
• Dorri, A., Kanhere, S. S., & Jurdak, R. (2018). Multi-agent systems: A survey. IEEE Access, 6, 28573–28593. https://doi.org/10.1109/ACCESS.2018.2831228
   Web of Science ®Google Scholar
• Eramo, V., Listanti, M., Lavacca, F. G., Iovanna, P., Bottari, G., & Ponzini, F. (2016). Trade-off between power and bandwidth consumption in a reconfigurable xhaul network architecture. IEEE Access, 4, 9053–9065. https://doi.org/10.1109/ACCESS.2016.2639578
   Web of Science ®Google Scholar
• Fiscko, C., Kar, S., & Sinopoli, B. (2021). Efficient solutions for targeted control of multi-agent mdps. 2021 American control conference (acc) (pp. 690–696). IEEE.
   Google Scholar
• Ghassemi, P., DePauw, D., & Chowdhury, S. (2019). Decentralized dynamic task allocation in swarm robotic systems for disaster response: Extended abstract. 2019 international symposium on multi-robot and multi-agent systems (mrs) (pp. 83–85). IEEE.
   Google Scholar
• Gruler, A., Fikar, C., Juan, A. A., Hirsch, P., & Contreras-Bolton, C. (2017). Supporting multi-depot and stochastic waste collection management in clustered urban areas via simulation–optimization. Journal of Simulation, 11(1), 11–19. https://doi.org/10.1057/s41273-016-0002-4
   Web of Science ®Google Scholar
• Guo, M., & Dimarogonas, D. V. (2017). Task and motion coordination for heterogeneous multiagent systems with loosely coupled local tasks. IEEE Transactions on Automation Science and Engineering, 14(2), 797–808. https://doi.org/10.1109/TASE.2016.2628389
   Web of Science ®Google Scholar
• Gupta, S., & Pujari, S. (2009). A multi-agent system (mas) based scheme for health care and medical diagnosis system. cinternational conference on intelligent agent multi-agent systems (pp. 1–3). IEEE.
   Google Scholar
• Hill, M. D., & Marty, M. R. (2008). Amdahl’s law in the multicore era. Computer, 41(7), 33–38. https://doi.org/10.1109/MC.2008.209
   Web of Science ®Google Scholar
• Hongwei, A., Xiong, L., & Xie, X. (2010). Multi-agent interactions centric virtual battlefield simulation model. 2010 2nd international conference on advanced computer control (Vol. 3 , pp. 315–319). IEEE.
   Google Scholar
• Hubmann, C., Schulz, J., Becker, M., Althoff, D., & Stiller, C. (2018). Automated driving in uncertain environments: planning with interaction and uncertain maneuver prediction. IEEE Transactions on Intelligent Vehicles, 3(1), 5–17. https://doi.org/10.1109/TIV.2017.2788208
   Google Scholar
• Ismail, S., Shaikh Ali, S. H., & Abu Bakar, M. H. (2018). Agent-based self-regulated learning simulation adopting the concept of gusc model. 2018 international symposium on agent, multi-agent systems and robotics (isamsr) (pp. 1–6). IEEE.
   Google Scholar
• Jianqiang Wang, Wenjuan Zhou, Shiwei Li, Danlei Shan. (2019). Impact of personalised route recommendation in the cooperation vehicle-infrastructure systems on the network traffic flow evolution. Journal of Simulation, 13(4), 239–253. doi:10.1080/17477778.2018.1515579
   Web of Science ®Google Scholar
• Kurniawati, H., Hsu, D., & Lee, W. S. (2008). Sarsop: Efficient point-based pomdp planning by approximating optimally reachable belief spaces. In In Robotics: Science and systems (Vol. 2008).
   Google Scholar
• Lee, S., Jain, S., & Son, Y.-J. (2022, January). A hierarchical decision-making framework in social networks for efficient disaster management. ACM Transactions on Modeling and Computer Simulation, 32(1), 1–26. https://doi.org/10.1145/3490027
   Web of Science ®Google Scholar
• Lee, Y. C., & Zomaya, A. Y. (2011, August). Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Transactions on Parallel and Distributed Systems, 22(8), 1374–1381. https://doi.org/10.1109/TPDS.2010.208
   Web of Science ®Google Scholar
• Li, S., Maddah-Ali, M. A., Yu, Q., & Avestimehr, A. S. (2018, January). A fundamental tradeoff between computation and communication in distributed computing. IEEE Transactions on Information Theory, 64(1), 109–128. https://doi.org/10.1109/TIT.2017.2756959
   Web of Science ®Google Scholar
• Littman, M. L., Dean, T. L., & Kaelbling, L. P. (2013). On the complexity of solving markov decision problems. arXiv preprint arXiv, 1302.4971. https://arxiv.org/abs/1302.4971
   Google Scholar
• Liu, M., Chang, W., Li, C., Ji, Y., Li, R., & Feng, M. (2020). Discrete interactions in decentralized multiagent coordination: A probabilistic perspective. IEEE Transactions on Cognitive and Developmental Systems, 13(4), 1010–1022, Dec. 2021. doi:10.1109/TCDS.2020.3040769
   Web of Science ®Google Scholar
• Liu, C., Hamrick, J. B., Fisac, J. F., Dragan, A. D., Hedrick, J. K., Sastry, S. S., & Griffiths, T. L. (2016). Goal inference improves objective and perceived performance in human- robot collaboration. In C. M. Jonker, S. Marsella, J. Thangarajah, & K. Tuyls (Eds.) Proceedings of the 2016 international conference on autonomous agents & multiagent systems. may 9-13, 2016. ACM. 940–948.
   Google Scholar
• Liu, F., & Liu, Z. (2018). A neighborhood-based value iteration algorithm for pomdp problems. 2018 ieee 30th international conference on tools with artificial intelligence (ictai) (pp. 808–812). IEEE.
   Google Scholar
• Lu, Y., Nolte, T., Bate, I., & Norström, C. (2010). Timing analyzing for systems with task execution dependencies. 2010 ieee 34th annual computer software and applications conference (pp. 515–524). IEEE.
   Google Scholar
• Lu, Y., Nolte, T., Kraft, J., & Norstrom, C. (2010). Statistical-based response-time analysis of systems with execution dependencies between tasks. 2010 15th ieee international conference on engineering of complex computer systems (pp. 169–179). IEEE.
   Google Scholar
• Mahela, O. P., Khosravy, M., Gupta, N., Khan, B., Alhelou, H. H., Mahla, R., & Siano, P. (2020). Comprehensive overview of multi-agent systems for controlling smart grids. CSEE Journal of Power and Energy Systems, 8(1), 115–131, Jan. 2022. doi:10.17775/CSEEJPES.2020.03390
   Web of Science ®Google Scholar
• Merton, R. K. (1968). The matthew effect in science: The reward and communication systems of science are considered. Science, 159(3810), 56–63. https://doi.org/10.1126/science.159.3810.56
   PubMed Web of Science ®Google Scholar
• Mukhopadhyay, S., & Jain, B. (2001). Multi-agent markov decision processes with limited agent communication. Proceeding of the 2001 ieee international symposium on intelligent control (isic ’01) (cat. no. 01ch37206) (pp. 7–12). IEEE.
   Google Scholar
• Ndoye, F., & Sorel, Y. (2013). Monoprocessor real-time scheduling of data dependent tasks with exact preemption cost for embedded systems. 2013 ieee 16th international conference on computational science and engineering (pp. 714–721). IEEE.
   Google Scholar
• Qin, L., Ouyang, F., & Xiong, G. (2018). Dependent task scheduling algorithm in distributed system. 2018 4th international conference on computer and technology applications (iccta) (pp. 91–95). IEEE.
   Google Scholar
• Rigney, D. (2010). The matthew effect: how advantage begets further advantage. Columbia University Press.
   Google Scholar
• Salmerón-Garcı, J., Inigo-Blasco, P., Dı, F., Cagigas-Muniz, D., et al. (2015). A tradeoff analysis of a cloud-based robot navigation assistant using stereo image processing. IEEE Transactions on Automation Science and Engineering, 12(2), 444–454. https://doi.org/10.1109/TASE.2015.2403593
   Web of Science ®Google Scholar
• Shani, G., Pineau, J., & Kaplow, R. (2013). A survey of point-based pomdp solvers. Autonomous Agents and Multi-Agent Systems, 27(1), 1–51. https://doi.org/10.1007/s10458-012-9200-2
   Web of Science ®Google Scholar
• Shi, J., Ueter, N., von der Brüggen, G., & Chen, J.-J. (2019). Multiprocessor synchronization of periodic real-time tasks using dependency graphs. 2019 ieee real-time and embedded technology and applications symposium (rtas) (pp. 279–292). IEEE.
   Google Scholar
• Squazzoni, F., & Gandelli, C. (2012). Saint matthew strikes again: an agent-based model of peer review and the scientific community structure. Journal of Informetrics, 6 (2), 265–275. https://doi.org/10.1016/j.joi.2011.12.005.
   Web of Science ®Google Scholar
• Tavanpour, M., Kazi, B. U., & Wainer, G. (2020). Discrete Event Systems Specifications Modelling and Simulation of Wireless Networking Applications. Journal of Simulation, 16(1), 1–25. doi:10.1080/17477778.2020.1750313
   Web of Science ®Google Scholar
• Topcuoglu, H., Hariri, S., & Min-You, W. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 13(3), 260–274. https://doi.org/10.1109/71.993206
   Web of Science ®Google Scholar
• Viseras, A., Xu, Z., & Merino, L. (2020). Distributed multi-robot information gathering under spatio-temporal inter-robot constraints. Sensors, 20 (2), 484. https://doi.org/10.3390/s20020484.
   PubMed Web of Science ®Google Scholar
• Vlassis N, Nikos, Spaan, Matthijs .(2004). A fast point-based algorithm for pomdps. In bene- learn 2004: proceedings of the annual machine learning conference of Belgium and the Netherlands (pp. 170–176).
   Google Scholar
• Waeber, R., Frazier, P. I., & Henderson, S. G. (2012, August). A framework for selecting a selection procedure. ACM Transactions on Modeling and Computer Simulation, 22(3), 1–23. https://doi.org/10.1145/2331140.2331144
   Web of Science ®Google Scholar
• Wilsdorf, P., Pierce, M. E., Hillston, J., & Uhrmacher, A. M. (2019). Round- based Super-Individuals—Balancing Speed and Accuracy. In Proceedings of the 2019 ACM SIGSIM Conference On Principles of Advanced Discretesimulation(SIGSIM-PADS '19) (pp. 95–98). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3316480.3322894
   Google Scholar
• Xiao, J., & Peng, J. (2019, July). Trade-offs between computation, communication, and synchronization in stencil-collective alternate update. CCF Transactions on High Performance Computing, 1(2), 144–160. https://doi.org/10.1007/s42514-019-00011-x
   Google Scholar
• Xu, H., & Yang, Y. (2009). Research and design on dynamic multi-agent cooperative processing model. 2009 international conference on web information systems and mining (pp. 432–436). IEEE.
   Google Scholar
• Yang, C., Yu, Z., Liu, Y., Wang, L., & Guo, B. (2019). Dynamic allocation for complex mobile crowdsourcing task with internal dependencies. In 2019 ieee smartworld, ubiquitous intelligence computing, advanced trusted computing, scalable computing communications, cloud big data computing, internet of people and smart city innovation (smart- world/scalcom/uic/atc/cbdcom/iop/sci) (pp. 818–825). IEEE.
   Google Scholar
• Zhang, J., Wei, L., Liu, M., & Deng, Y. (2021). A competition model for modeling and describing matthew effect in computational social systems. 2021 11th international conference on intelligent control and information processing (icicip) (pp. 438–443). IEEE.
   Google Scholar
• Zhao, Y., Chen, L., Li, Y., & Tian, W. (2014). Efficient task scheduling for many task computing with resource attribute selection. China Communications, 11(12), 125–140. https://doi.org/10.1109/CC.2014.7019847
   Web of Science ®Google Scholar
• Zhao, L., Du, M., & Chen, L. (2018). A new multi-resource allocation mechanism: A tradeoff between fairness and efficiency in cloud computing. China Communications, 15(3), 57–77. https://doi.org/10.1109/CC.2018.8331991
   Web of Science ®Google Scholar
• Zomaya, A. Y., & Lee, Y. C. (2012). Comparison and analysis of greedy energy-efficient scheduling algorithms for computational grids. Energy-efficient distributed computing systems (pp. 189–214). Wiley-IEEE Computer Society.
   Google Scholar
• ZongZhang, Zhang, David Hsu, & d Wee Sun, Lee. (2014). Covering number for efficient heuristic-based pomdp planning. In Proceedings of the 31st International Conference on Machine Learning, 32(ICML'14). JMLR.org, I–28–I–36.
   Google Scholar