Computer Science ›› 2020, Vol. 47 ›› Issue (4): 169-177.doi: 10.11896/jsjkx.190900188

• Artificial Intelligence • Previous Articles     Next Articles

Emotional Robot Collaborative Task Assignment Auction Algorithm Based on Positive GroupAffective Tone

LI Hu, FANG Bao-fu   

  1. School of Computer and Information,Hefei University of Technology,Hefei 230009,China
  • Received:2019-09-17 Online:2020-04-15 Published:2020-04-15
  • Contact: FANG Bao-fu,born in 1978,Ph.D,associate professor,postgraduate supervisor.His main research interests include multi robot/agent system,emotion/self-interest robot and machine learning.
  • About author:LI Hu,born in 1989,postgraduate.His main research interests include multi robot collaboration and emotion computing.

Abstract: Multi robot system (MRS) can effectively improve individual's autonomous cooperation ability,decision-making ability and overall intelligent level of multi robot system by introducing individual emotional factors.However,previous researches mainly focus on individual emotional state (emotion,personality,etc.),lacking of exploring the influence of group emotional state on group cooperation ability and group effectiveness from positive group affective tone(PGAT).In order to improve positive effects of PGAT in task allocation and reduce the risk of group dissolution caused by group members’ emotional decaying,as well as increasing group cooperation ability and group effectiveness,this paper proposed collaborative task allocation auction algorithm based on PGAT.The results of simulation show that compared with modified contract network protocol multi-robot task allocation algorithm based on anxiety model and distributed task allocation method based on self-awareness of autonomous robots,the emotional robot collaborative task assignment auction algorithm based on positive group affective tone improves the pursuit success rate by 269.3% and 6.5%,and increases the task allocation success rate by 138.7% and 5% respectively,and reduces the average pursuit time by 14.5% and 26.3% respectively.Besides,in 150 episodes of pursuit comparison experiment,the proportion of the number of episodes whose pursuit time is less than the comparison algorithm is 87.3% and 90.7% respectively.

Key words: Multi-robot system, Positive group affective tone (PGAT), Emotion, Personality, Collaborative task allocation

CLC Number: 

  • TP242.6
[1]FARINELLI A,IOCCHI L,NARDI D.Distributed on-line dynamic task assignment for multi-robot patrolling[J].Autonomous Robots,2016,41(6):1-25.
[2]KOKUTI A,HUSSEIN A,DE LA ESCALERA A,et al.Market-based approach for cooperation and coordination among multiple autonomous vehicles[C]//2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).IEEE,2017:534-539.
[3]GOLDHOORN A,GARRELL A,ALQUÉZAR R,et al.Searching and tracking people with cooperative mobile robots[J].Autonomous Robots,2018,42(4):739-759.
[4]WANG H,ZHANG C,SONG Y,et al.Robot SLAM with Ad hoc wireless network adapted to search and rescue environments[J].Journal of Central South University,2018,25(12):3033-3051.
[5]SUGIYAMA H,TSUJIOKA T,MURATA M.Real-time exploration of a multi-robot rescue system in disaster areas[J].Advanced Robotics,2013,27(17):1313-1323.
[6]HUANG Y,ZHANG Y,XIAO H.Multi-robot system task allocation mechanism for smart factory[C]//2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC).IEEE,2019:587-591.
[7]HAN Y,LI D,CHEN J,et al.Task allocation algorithm based on robot ability and relevance with group collaboration in a robot team[C]//2009 Second International Conference on Intelligent Networks and Intelligent Systems.IEEE,2009:273-277.
[8]BANIK S C,WATANABE K,HABIB M K,et al.An emotion-based task sharing approach for a cooperative multiagent robotic system[C]//2008 IEEE International Conference on Mechatronics and Automation.IEEE,2008:77-82.
[9]DING Y,ZHU M,HE Y,et al.An autonomous task allocation method of the multi-robot system[C]//2006 9th International Conference on Control,Automation,Robotics and Vision.IEEE,2006:1-6.
[10]FANG B,GUO X,WANG Z,et al.Collaborative task assignment of interconnected,affective robots towards autonomous healthcare assistant[J].Future Generation Computer Systems,2019,92:241-251.
[11]WANG X,SHENG B.Multi-robot task allocation algorithmbased on anxiety model and modified contract network protocol[C]//2017 IEEE 2nd Information Technology,Networking,Electronic and Automation Control Conference (ITNEC).IEEE,2017:1606-1612.
[12]WANG Z,ZHU J,GUO X P,et al.Distributed task allocation method based on self-awareness of autonomous robots[J].The Journal of Supercomputing,2019,76(2):1-13.
[13]GEORGE J M.Handbook of work group psy-chology[M].Chichester,UK:Wiley,1996:77-93.
[14]GEORGE J M,KING E B.Chapter 5 Potential Pitfalls of Affect Convergence in Teams:Functions and Dysfunctions of Group Affective Tone[J].Research on Managing Groups & Teams,2007,10:97-123.
[15]WALTER F,BRUCH H.The positive group affect spiral:a dynamic model of the emergence of positive affective similarity in work groups .Journal of Organizational Behavior,2008,29(2):239-261.
[16]COLLINS A L,JORDAN P J,LAWRENCE S A,et al.Positive affective tone and team performance:The moderating role of collective emotional skills[J].Cognition and Emotion,2016,30(1):167-182.
[17]SHIN Y,KIM M,LEE S H.Positive Group Affective Tone and Team Creative Performance and Change-Oriented Organizational Citizenship Behavior:A Moderated Mediation Model[J].The Journal of Creative Behavior,2019,53(1):52-68.
[18]TSAI Y H,MA H C,LIN C P,et al.Group social capital in virtual teaming contexts:A moderating role of positive affective tone in knowledge sharing[J].Technological Forecasting and Social Change,2014,86:13-20.
[19]LIN C P,HE H,BARUCH Y,et al.The effect of team affective tone on team performance:The roles of team identification and team cooperation[J].Human Resource Management,2017,56(6):931-952.
[20]TANG Y Y,TSAUR S H.Supervisory support climate andservice-oriented organizational citizenship behavior in hospitality:The role of positive group affective tone[J].International Journal of Contemporary Hospitality Management,2016,28(10):2331-2349.
[21]TSAI W C,CHI N W,GRANDEY A A,et al.Positive group affective tone and team creativity:Negative group affective tone and team trust as boundary conditions[J].Journal of Organizational Behavior,2012,33(5):638-656.
[22]COLLINS A L,LAWRENCE S A,TROTH A C,et al.Group affective tone:A review and future research directions[J].Journal of Organizational Behavior,2013,34(S1):S43-S62.
[23]FANG B,WANG Z,CHEN L,et al.Research on pursuit task allocation algorithm of emotional robot based on personality[C]//2015 Chinese Automation Congress (CAC).IEEE,2015:457-462.
[24]TANASESCU V,JONES C B,COLOMBO G,et al.The Personality of Venues:Places and the Five-Factors (Big Five) Model of Personality[C]//2013 Fourth International Conference on Computing for Geospatial Research and Application.IEEE,2013:76-81.
[1] ZHAO Cheng, YE Yao-wei, YAO Ming-hai. Stock Volatility Forecast Based on Financial Text Emotion [J]. Computer Science, 2020, 47(5): 79-83.
[2] ZHENG Chun-jun, WANG Chun-li, JIA Ning. Survey of Acoustic Feature Extraction in Speech Tasks [J]. Computer Science, 2020, 47(5): 110-119.
[3] XU Yuan-yin,CHAI Yu-mei,WANG Li-ming,LIU Zhen. Emotional Sentence Classification Method Based on OCC Model and Bayesian Network [J]. Computer Science, 2020, 47(3): 222-230.
[4] LI Yuan,LI Zhi-xing,TENG Lei,WANG Hua-ming,WANG Guo-yin. Comment Sentiment Analysis and Sentiment Words Detection Based on Attention Mechanism [J]. Computer Science, 2020, 47(1): 186-192.
[5] ZHANG Lu, SHEN Chen-lin, LI Shou-shan. Emotion Classification Algorithm Based on Emotion-specific Word Embedding [J]. Computer Science, 2019, 46(6A): 93-97.
[6] LU Zhu-bing, LI Yu-zhou. Recommendation Strategy Based on Trust Model via Emotional Analysis of Online Comment [J]. Computer Science, 2019, 46(6): 75-79.
[7] WU Liang-qing, ZHANG Dong, LI Shou-shan, CHEN Ying. Multi-modal Emotion Recognition Approach Based on Multi-task Learning [J]. Computer Science, 2019, 46(11): 284-290.
[8] WEN Wen, CHEN Ying, CAI Rui-chu, HAO Zhi-feng, WANG Li-juan. Emotion Classification for Readers Based on Multi-view Multi-label Learning [J]. Computer Science, 2018, 45(8): 191-197.
[9] ZHANG Chun-xia,NIU Zhen-dong,SHI Chong-yang, SHANG Jian-yun. Learning Effect Evaluation Method Based on Fine-granularity Learning Emotion Ontology
——Taking Algorithm Design and Analysis Course as Example
[J]. Computer Science, 2018, 45(6A): 58-62.
[10] CHEN Zhi-xiong, WANG Shi-hui and GAO Rong. Recognition Model of Microblog Opinion Leaders Based on Sentiment Orientation Analysis [J]. Computer Science, 2018, 45(5): 168-175.
[11] YIN Hao, XU Jian, LI Shou-shan, ZHOU Guo-dong. Emotion Recognition on Microblog Based on Character and Word Features [J]. Computer Science, 2018, 45(11A): 105-109.
[12] CHEN Jin-yin, FANG Hang, LIN Xiang, ZHENG Hai-bin, YANG Dong-yong, ZHOU Xiao. Personal Learning Recommendation Based on Online Learning Behavior Analysis [J]. Computer Science, 2018, 45(11A): 422-426.
[13] ZHANG Xiao-yang, QIN Gui-he, ZOU Mi, SUN Ming-hui and GAO Qing-yang. Research on Recommendation Method of Restaurant Based on LDA Model [J]. Computer Science, 2017, 44(7): 180-184.
[14] CAO Meng-xiao, ZHANG Gui-juan, HUANG Li-jun and LIU Hong. Crowd Animation Generation Method Based on Personalized Emotional Contagion [J]. Computer Science, 2017, 44(6): 306-311.
[15] HUANG Lei, LI Shou-shan and ZHOU Guo-dong. Emotion Recognition of Chinese Microblogs with Syntactic Information [J]. Computer Science, 2017, 44(2): 244-249.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .