Cover not available
Article published In:
Human Robot Collaborative Intelligence: Theory and applications
Edited by Chenguang Yang, Xiaofeng Liu, Junpei Zhong and Angelo Cangelosi
[Interaction Studies 20:1] 2019
► pp. 61–77

Study on decision-making of soccer robot based on rough set theory

| Xi’an Polytechnic University
| Xi’an Polytechnic University

“Rough set” is a theory put forward by the polish scholar Z. Pawlak, which is a useful mathematics tool for dealing with vague and uncertain information. Rough set theory can achieve a subset of all attribute which preserves the discernible ability of original features, by using the data only with no additional information. As a typical system of multi-agent, the decision-making system of soccer robot has the features of multi-layered, antagonism, and cooperation. On the bases of rough set theory, this paper established a decision making system with complete information for soccer robot, and then reduce the condition and decision attributes and their values, to get the simply decision rules. On the otherwise, considering the situation of information loss, we study decision making of imperfect information system, extract the decision rules and calculate the reliability, so that the rules can assist the agent to make right decision in competition. The simulation result shows that the algorithm is correct and effective.

Keywords: decision making, soccer robot, rough set theory, attribute reduce, imperfect information
Article outline
  • Introduction
    • 1.Information system
    • 2.Attribute reduction of information system
    • 3.Decision-making information system
      • Decision-making table
      • Decision rules
  • 1.Establish the decision-making system of soccer robot
    • 1.1Scenarios of soccer robot confrontation system
    • 1.2Establish the decision information system of soccer robot
  • 2.Getting decision rules from perfect information system
    • 2.1Reduce the attribute of decision making table
    • 2.2Reduce the attribute value of decision making table
    • 2.3Get decision rules
  • 3.Getting decision rules from imperfect information system
    • 3.1Get decision rules when partial condition attributes loss
    • 3.2Get decision rules when decision attributes loss
  • 4.Conclusion
  • References
Published online: 15 July 2019
DOI logo
https://doi.org/10.1075/is.18020.zha
References
Abhaya, M., Manju, A., Dev Anand, M., Sharolyn, V.
(2014) Intelligent modeling and decision making for the control of industrial robot system based on neuro fuzzy approach 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies, ICCICCT, 1453–1458.Google Scholar
AI-Araji, A. S., Abbod, M. F. and AI-Raweshidy, H. S.
(2013) Applying posture identifier in designing an adaptive nonlinear predictive controller for nonholonomic mobile robot, 991, 543–554. DOI logoGoogle Scholar
de Cooman, Gert, Makila C. M. Throffaes
(2005) Dynamic programming for deterministic discrete-time systems with uncertain gain, International Journal of Approximate Reasoning, 391, 257–278. DOI logoGoogle Scholar
Dorigo, M., Gambardella, L. M., Middenorf, et al.
(2002) Guest editorial: special section on ant colony optimization. IEEE Transactions on Evolutionary Computation, 6(4), 317–319. DOI logoGoogle Scholar
Drichta, David S.
(2006) The applicability of the military decision-making process in the air operations center. AD Report, ADA415800.Google Scholar
Ever, Yoney Kirsal
(2017) Using simplified swarm optimization on path planning for intelligent mobile robot, 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW 2017, Procedia Computer Science, 1201, 83–90. DOI logoGoogle Scholar
Gao, Guoqin, Zhou Haiyan, Niu Xuemei
(2013) An Intelligent variable spraying decision-making system based on fuzzy neural network for greenhouse mobile robot, Communications in Computer and Information Science, 3551, 257–265. DOI logoGoogle Scholar
Gao, Jian, Tong Mingan
(2003) Extracting decision rules for cooperative team air combat based on rough set theory, Chinese Journal of Aeronautics, 16(4), 223–228. DOI logoGoogle Scholar
Gonzalo, Nápoles, Grau Isel, Vanhoof Koen, Bello Rafael
(2014) Hybrid model based on rough sets theory and fuzzy cognitive maps for decision-making, Lecture Notes in Computer Science, 8537 LNAI, 169–178.Google Scholar
Gurnani, Ashwin P., Kemper Lewis
(2004) An approach to robust decision making in multidisciplinary selection problems under uncertainty, AIAA, ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York, 2004–4472.
Jolly, K. G., Ravindran, K. P., Vijayakumar, R., Sreerama Kumar, R.
(2007) Intelligent decision making in multi-agent robot soccer system through compounded artificial neural networks, Robotics and Autonomous Systems, 55(7), 589–596. DOI logoGoogle Scholar
Li, Yongchang, Dimitri Mavris, Daniel DeLaurentis
(2004) The investigation of a decision-making technique using the loss function, AIAA 2004–6205, AIAA 4th Aviation Technology, Integration and Operations (ATIO) Forum, Chicago, Illinois, 30–41.
Liu, Baoding
(2001) Uncertain Programming: A unifying optimization theory in various uncertain environments, Applied Mathematics and Computation, 1201, 227–234. DOI logoGoogle Scholar
Liu, Yong, Marwan A. Simaan, Jose B. Cruz Jr.
(2003) An application of dynamic Nash task assignment strategies to multi-team military air operations. Automatic, 391, 1469–1478. DOI logoGoogle Scholar
Lju, G.
(2005) The ant algorithm for solving robot path planning problem. Third International Conf. on Information Technology and Applications. (ICITA 2005), 2(4–7), 25–27.
McEnenaey, William, Rajdeep Singh
(2005) Deception in autonomous vehicle decision making in an adversarial environment, AIAA Guidance, Navigation, and Control Conference and Exhibit, San Francisco, California, 2005–6152.
Mosadeghzad, M., Naderi, D. and Ganjefar, S.
(2012) Dynamic modeling and stability optimization of a redundant mobile robot using a genetic algorithm. Robotica, 301, 505–514. DOI logoGoogle Scholar
Nie, Puyan
(2005) Dynamic stackelberg games under open-loop complete information, Journal of Franklin Institute, 1–12.Google Scholar
Omrane, Hajer, Masmoudi, Mohamed Slim, Masmoudi, Mohamed
(2017) Intelligent mobile robot navigation International Conference on Smart, Monitored and Controlled Cities, SM2C 2017, 27–31.
Pelta, David, Alejandro Sancho-Royo, Carlos Cruz, etc.
(2006) Using memory and fuzzy rules in a cooperative multi-thread strategy for optimization, Information Sciences (176), 1849–1868. DOI logoGoogle Scholar
Radek, Doskočil, Doubravský Karel
(2014) Decision-making rules based on rough set theory: Creditworthiness case study, Proceedings of the 24th International Business Information Management Association Conference – Crafting Global Competitive Economies: 2020 Vision Strategic Planning and Smart Implementation, 321–327.
Remesh, K. M., Nair Latha, R.
(2016) Rough set theory and three way decisions: Refinement of boundary region in the decision making process Proceedings-2016 International Conference on Information Science, ICIS, 156–159.
Russel, Stuart J.(Stuart Jonathan), Norvig, Peter, Jiang Zhe
(2004) Artificial intelligence: a kind of modern way. Posts & Telecom Press, 1–3.Google Scholar
Skowron, A.. Zw.uraj
(1996) A parallel algorithm for real-time decision making: A rough set approach. Journal of Intelligent Information systems, 1–28.Google Scholar
Song, Chong-yuan
(2016) Research and design of soccer robot decision system based on SARA algorithm, Harbin Institute of Technology, 1–34.Google Scholar
Stephan, K. D., Michael, M. G., Jacob, L., Anesta, E. P.
(2012) Social implications of technology: the present, and the future, Proceedings of the IEEE, 1001, 1752–1781. DOI logoGoogle Scholar
Zhao, Wei, Huang Wenjuan, Yao Hongping, Gao Jianyu
(2014) The research of centralized robot-soccer decision-making system based on three-layered hierarchical model, Applied Mechanics and Materials, 543–547, 1327–1330.Google Scholar
Zhang, Wenxiu, Wu Weizhi, Liang Jiye
(2001) Rough set theory and method, Beijing Science Press, 1–210.Google Scholar
Yager, Ronald R.
(2004) Intelligent decision making and information fusion, Second IEEE international conference in Intelligent systems, 1–4.Google Scholar
Yang, C., Y. Jiang, Z. Li, W. He, C.-Y. Su
(2017) Neural control of bimanual robots with guaranteed global stability and motion precision, IEEE Transactions on Industrial Informatics, 13(3), 1162–1171. DOI logoGoogle Scholar
Yang, C., K. Huang, H. Cheng, Y. Li and C.-Y. Su
(2017) Haptic Identification by ELM Controlled Uncertain Manipulator, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(8), 2398–2409. DOI logoGoogle Scholar
Yang, C., H. Wu, Z. Li, W. He, N. Wang, C.-Y. Su
(2017) Mind control of a robot arm with visual fusion technology, IEEE Transactions on industrial informatics, DOI logoGoogle Scholar
Yang, C., Y. Jiang, W. He, J. Na, Z. Li, B. Xu
(2018) Adaptive parameter estimation and control design for robot manipulators with finite-time convergence, IEEE Transactions on Industrial Electronics, 65 (10), 8112–8123. DOI logoGoogle Scholar
Yang, C., C. Zeng, P. Liang, Z. Li, R. Li, C.-Y. Su
(2018) Interface Design of a Physical Human Robot Interaction System for Human Impedance Adaptive Skill Transfer, in press IEEE Transactions on Automation Science and Engineering, 15(1), 329–340.Google Scholar
Yee, Leuing, Deyu Li
(2003) Maximal consistent block technique for rule acquisition in incomplete information systems. Information Science, 1531, 58–106.Google Scholar
Zhang, Haiying, Fan Jinzhen
(2011) Research progress and future development if mobile robot path planning, Microcomputer & its Applications, 30(2), 5–8.Google Scholar
Zhang, Lin, Shi Haobin, Wu Jiabin
(2013) Applying intelligent confrontation decision-making system to robot soccer based on improved cbr(case-based reasoning), Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnic University.12, 31(6), 991–996.Google Scholar
Zhao, Yuxuan, Man Ka Lok, Liang Hai-Ning, Wang Wei, Yue Yong, Jeong
(2015) Taikyeong Design of intelligent algorithms for multi-mobile robot systems ISOCC 2015 – International SoC Design Conference: SoC for Internet of Everything (IoE) 2016, ISOCC, 177–178.Google Scholar
Cited by

Cited by 1 other publications

Yang, Xuxu, Xueen Wang & Jie Kang
2022. Multi‐granularity decision rough set attribute reduction algorithm under quantum particle swarm optimization. IET Cyber-Systems and Robotics 4:1 ► pp. 25 ff. DOI logo

This list is based on CrossRef data as of 15 april 2022. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.