A model is proposed for estimating the efficiency of communication by robots capable of social interaction with humans. A generalized index of the robots effectiveness in communication is derived. That index is calculated by means of a fuzzy deduction modul

Balakshin, M. S.

Igityan, E. V.

Konstantinov, I. S.

Lazarev, S. A.

Polshchykov, K. A.

Polshchykova, O. N.

Svoikina, L. F.

DSpace at Belgorod State University

ISSN 1068-798X, Russian Engineering Research, 2020, Vol. 40, No. 11, pp. 936–938. © Allerton Press, Inc., 2020.Russian Text © The Author(s), 2020, published in STIN, 2020, No. 6, pp. 4–7.Assessing the Efficiency of Robot CommunicationK. A. Polshchykova, *, S. A. Lazareva, **, I. S. Konstantinova, ***, O. N. Polshchykovaa, ****, L. F. Svoikinaa, *****, E. V. Igityana, ******, and M. S. Balakshina, *******aBelgorod State University, Belgorod, 308015 Russia*e-mail: polshchikov@bsu.edu.ru**e-mail:lazarev_s@bsu.edu.ru***e-mail: konstantinov@bsu.edu.ru****e-mail: polshchikova@bsu.edu.ru*****e-mail: svojkina@bsu.edu.ru******e-mail: medvedeva_e@bsu.edu.ru*******e-mail: 1337150@bsu.edu.ruReceived January 24, 2020; revised January 24, 2020; accepted January 24, 2020Abstract—A model is proposed for estimating the efficiency of communication by robots capable of socialinteraction with humans. A generalized index of the robot’s effectiveness in communication is derived. Thatindex is calculated by means of a fuzzy deduction module.Keywords: robot systems, communication functions, effectiveness, performance index, fuzzy-deductionmodule, Matlab, SimulinkDOI: 10.3103/S1068798X20110155Today, robot systems are capable of a broad varietyof tasks. In recent years, interest in social capabilitieshas been growing. Manufacturers of software and hard-ware have proposed robots capable of social interactionwith humans [1, 2]. Examples include robot assistantsthat may perform domestic functions [3, 4]. The effec-tiveness of speech communication is very importanthere. How well the robot performs will depend ulti-mately on how comfortable with robot communicationthe human feels and how appropriately the robotbehaves in whatever situations might arise.In order to select social robots with the best commu-nication capabilities and to identify necessary areas forimprovement, we need means of assessing the effective-ness of robot communication with humans [5]. There islittle literature on the development of such assessmentprocedures. Accordingly, models must be developed forassessing the effectiveness of communication by robotscapable of social interaction with humans.The effectiveness of robot communication isassessed on the basis of multiple criteria, taking accountof the correctness and timeliness of robot speech withhumans. It is expedient to use the generalized index of the effectiveness of robot communication.The correctness of robot communication is relatedto its ability to conduct a reasonable dialog with ahuman. The timeliness of communication depends onthe delay time of the robot in the communication pro-cess. Since these characteristics are not specificnumerical values, their assessment requires the use offuzzy sets: in the first case, high and low correctness ofcommunication; in the second, high and low speed ofresponse in communication [6].In Matlab + Simulink software, the SysF model isdeveloped for determining the effectiveness of com-munication by the robot system (Fig. 1). The mostimportant element of this model is the fuzzy-deduc-tion module (Fuzzy_modul).The elements –  are intended to input assess-ments of the correctness of robot responses to test que-ries. The values of these elements are input in accor-dance with the following logic: if the robot response toquery  is correct, then ; otherwise, .The elements –  determine the wait time  ingetting a response to specified query  from the robot.For example, if —that is, the robot respondsafter 2.35 s to test query 4—then the value of element is 2.35.The element Calc_C of the model calculates thevalue of , which is the aggregate correctness of therobot’s responses to the test queries: . Theelements Add, Calc_R, and N calculate , which isthe mean delay time of the virtual robot in respondingto the test queries: . Here  is the num-ber of test queries.F1с 8сn 1nс = 0nс =1r 8r nrn4 2.35r =4rС1NnnС c== R11 NnnR rN ==  N936ASSESSING THE EFFICIENCY OF ROBOT COMMUNICATION 937Fig. 1. SysF model for determining the effectiveness ofcommunication by a robot system.c101c21c31c41c51c61c74.45r12.5r24.2r32.35r42.25r54.0r61.5r72.55r8c81++++++++++++++++8NCalc_CFuzzy_modulDisplay_F3.254AddCalc_R×÷The Fuzzy_modul element functions on the basisof the rules [7–9]If (C = Hc) and (R = Hr), then (F = F1);if (C = Hc) and (R = Lr), then (F = F2);if (C = Lc) and (R = Lr), then (F = F4).Here the descriptors of robot communication are asfollows:  is the fuzzy set <high correctness>;  isthe fuzzy set low correctness;  is the fuzzy sethigh speed of response;  is the fuzzy set low speedof response. The individual inferences from the fuzzyrules are as follows: ,  and , .The functions  and  characterizing the mem-bership of  in the fuzzy sets  and  and the func-tions  and  characterizing the membership of inthe fuzzy sets  and  are determined as followshere  and  are the boundaries of the internal seg-ment of the graph of ;  and  are the boundariesof the internal segment of the graph of ;  and are the boundaries of the internal segment of the graphof ;  and  are the boundaries of the internal seg-ment of the graph of ;  and  are the coefficientsof the direct membership function  in the section;  and  are the coefficients of the directmembership function  in the section ; and  are the coefficients of the direct membershipfunction  in the section ; and  and are the coefficients of the direct membership function in the section .cH cLrHrL1 4F = 2 3F = 3 2F = 4 1F =C + C −C cH cLR+ R− rrH rL01 1 0 110, ;, ;1, ;C cC a C b c C cC c++ + ++ <= + ≤ < ≥12 2 1 001, ;, ;0, ;C cC a C b c C cC c−− − −− <= + ≤ < ≥13 3 1 001, ;, ;0, ;R rR a R b r R rR r++ + ++ <= + ≤ < ≥04 4 0 110, ;, ;1, ;R rR a R b r R rR r−− − −− <= + ≤ < ≥0c+1c+C + 0c−1c−C − 0r+1r+R+ 0r−1r−R− 1a 1bC +0 1c C c+ +≤ < 2a 2bC − 1 0c C c− −≤ <3a 3bR+ 1 0r R r+ +≤ < 4a 4bR− 0 1r R r− −≤ <RUSSIAN ENGINEERING RESEARCH  Vol. 40  No. 1The generalized effectiveness of robot communica-tion is calculated from the formulawhere , , , and  are the results of aggregationcalculated from the expressions ;; ; and .The Display_F module shows the results of calcu-lating F.Operation of the fuzzy-deduction system requiresadjustment of the Fuzzy_modul element in accordancewith , , , , , , , and . To that end, experiments are conducted with a virtual robot speciallydeveloped for adjustment of the fuzzy-deduction sys-tem. In each experiment , experts must give a subjec-tive estimate of the correctness of communication withthe virtual robot and its speed of response.RESULTS OF COMPUTER EXPERIMENTSThe proposed model is used in a series of computerexperiments to assess the effectiveness of robot com-munication. As an example, we show the results of one1 1 2 2 3 3 4 41 2 3 4,G Y G Y G Y G YFG G G G+ + +=+ + +1G 2G 3G 4G1G C R+ += ∧2G C R+ −= ∧ 3G C R− += ∧ 4G C R− −= ∧0c+1c+0c−1c−0r+1r+0r−1r− KJ1  2020938 POLSHCHYKOV et al.Table 1Element Value Element Value, s0 4.451 2.50 4.21 2.351 2.250 4.01 1.51 2.551c 1r2c 2r3c 3r4c 4r5c 5r6c 6r7c 7r8c 8rexperiment in which the initial data for the elements–  and –  of the SysF model are as in Table 1.On the basis of the experiments, the effectivenessof robot communication F = 3.245 (shown by the Dis-play_F element).CONCLUSIONS(1) In Matlab + Simulink software, a model hasbeen developed for assessment of the effectiveness ofcommunication by robots capable of social interactionwith humans. The model is based on a fuzzy-deduc-tion system taking account of the correctness andresponse time of the robot in communication.(2) Computer experiments with this model yield thegeneralized effectiveness of robot communication F,which varies from 1 to 4. Higher F corresponds to moreeffective communication.(3) The next step is to develop software on the basisof the proposed model and to test it experimentally.1с 8с 1r 8rRUSSIAN REFERENCES1. Social robots: high technology against loneliness.https://newtonew.com/science/sociable-robots-and-human-loneliness. Accessed December 27, 2019.2. Gaschler, A., Huth, K., and Giuliani, M., Modelingstate of interaction from head poses for social human-robot interaction. https://mediatum.ub.tum.de/doc/1285788/file.pdf. Accessed December 27, 2019.3. Abdul Malik, N., Yussof, H., and Hanapiah, F.A., Po-tential use of social assistive robot based rehabilitationfor children with cerebral palsy, Proc. 2nd IEEE Int.Symp. on Robotics and Manufacturing Automation,Roma, 2016, pp. 782–784.4. Salem, M., Kopp, S., Wachsmuth, I., et al., Generationand evaluation of communicative robot gesture, Int. J.Soc. Rob., 2012, vol. 4, pp. 201–217.5. Suga, Y., Ikuma, Y., Nagao, D., et al., Interactive evo-lution of human-robot communication in real world,Proc. 2005 IEEE/RSJ Int. Conf. on Intelligent Robotsand Systems, Washington, DC: IEEE Comput. Soc.,2005, pp. 1438–1443.6. Polshchykov, K.A., Lazarev, S.A., Polshchykova, O.N.,and Igityan, E.V., The algorithm for decision-makingsupporting on the selection of processing means for bigarrays of natural language data, Lobachevskii J. Math.,2019, vol. 40, no. 11, pp. 1831–1836.7. Takagi, T. and Sugeno, M., Fuzzy identification of sys-tems and its applications to modeling and control,IEEE Trans. Syst., Man Cybern., 1985, vol. 15, no. 1,pp. 116–132.8. Konstantinov, I.S., Polshchykov, K.A., Lazarev, S.A.,and Polshchykova, O.N., Model of neuro-fuzzy pre-diction of confirmation timeout in a mobile ad-hocnetwork, Proc. Int. Conf. “Mathematical and Informa-tion Technologies,” MIT-2016, Vrnjacka Banja, 2017,pp. 174–186.9. Konstantinov, I.S., Lazarev, S.A., Polshchykov, K.A.,and Mihalev, O.V., Theoretical aspects of evaluation ofthe corporative portal network traffic management,Int. J. Appl. Eng. Res., 2015, vol. 10, no. 24, pp. 45691–45696.Translated by B. GilbertENGINEERING RESEARCH  Vol. 40  No. 11  2020

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