Research on Interactive Design Based on Artificial Intelligence

Transcription

1 Revsta de la Facultad de Ingenería U.C.V., Vol. 32, N 11, pp , 2017 Research on Interactve Desgn Based on Artfcal Intellgence Abstract Yng Lu Zhengzhou Ralway Vocatonal & Techncal College, Zhengzhou, Henan , Chna Wth the development of market globalzaton, people's consumpton concept and consumpton patterns are constantly changng. The tradtonal product desgn method s gradually replaced by the new product desgn method represented by computer technology. Artfcal ntellgence has a great nfluence on the way of mutual percepton, cogntve logc and the method of nteractve desgn. In ths work, an nteractve product desgn model based on nteractve genetc algorthm s establshed by usng the nteractve product desgn method for practcal applcaton. On the bass of the nteractve genetc algorthm, the tradtonal nteractve genetc algorthm s mproved by usng the layered dea. After the establshment of the nteractve product desgn model based on the nteractve genetc algorthm, the overall structure of the product nteracton desgn system and the man functon modules are desgned, and the experment smulaton s carred out to verfy the effectveness of the desgn method and the mproved algorthm. Keywords: Artfcal ntellgence, Interactve desgn,genetc algorthm. 1. INTRODUCTION The development of nteractve desgn s closely related to the nteracton technology. Artfcal ntellgence s one of the mportant nteractve technologes, whch extends the vew and dmenson of nteractve desgn. Artfcal ntellgence and the development of prosperty, at the same tme, along wth the development of human-computer nteracton technology and nteractve desgn, from the man-machne nteractve technology orented, to experence the human centered desgn. The nteractve desgn of three phases s shown n Fgure 1(Km and Cho,2000): Technologycentrc People-centered Focus on purpose Fgure 1. Three stages of nteracton desgn Ths work s based on nteractve genetc algorthm nteractve product desgn method. Affnnova'spatented technology s a company's product desgn system known as "Interactve Desgn and Dscovery by Evolutonary Algorthm". Through the evolutonary algorthm analyss of consumer evaluaton of nteractve products and access to the consumer's specfc product desgn preferences, the collectve feedback of these consumers can provde product desgn company to provde the most reasonable product desgn, and the most sutable product desgn wll be the bggest success n the market (Rgolon and Németh, 2016). Ths patent s manly for consumers, the method s a form of network through the way to acheve the dstrbuton of users n dfferent places to partcpate n the whole process of product desgn process. The users here are both professonal product desgners and ordnary consumers. The man work of ths work s to use nteractve genetc algorthm to acheve the analyss of user evaluaton, access to user preferences for product desgn. The nteractve product desgn model s establshed based on the nteractve genetc algorthm and the effectveness of the algorthm s analyzed(candy and Edmonds, 1996). 2. INTERACTIVE PRODUCT DESIGN Interactve desgn s not equvalent to human-computer nteracton, t s n the human-computer nteracton based on the development of a new dscplne.interactve desgn refers to the desgn should focus on the 613

2 Revsta de la Facultad de Ingenería U.C.V., Vol. 32, N 11, pp , 2017 nteracton between people and products, to consder the user's background and experence n the operaton process, so as to meet the end-user products (Raymond et al., 2016). The human-computer nteracton manly refers to the nteracton between people and the computer. The purpose of the study s to solve the problem of usablty and ease of use of software systems supported by complex computng technques. Smlarly, for software systems, people wll confuse nteracton desgn and nterface desgn, and thnk that nteracton desgn s nterface desgn.. Interactve desgn s a new dscplne whch developed on the bass of human-computer nteracton. Its theoretcal bass nvolves socology,engneerng and other dscplnes, Related nterdscplnary felds and methods nclude human engneerng, cogntve engneerng and nformaton systems(fong et al., 2003). The nteractve desgn advocates the whole desgn through the basc vew of the system theory, that the desgn process tself s a system, ts lnks and elements are closely lnked to each other, for the specfc target of people, supportng the behavor of users n dfferent scenaros.the behavor taken under the scene,a system conssts of a set of nteractng or nterdependent elements. Accordng to the defnton of the system, the nteractve product desgn s the system of the four basc elements of the human, behavor, product scene and product fuson technology,nteractve desgn s desgned for nteractve systems (Coller et al., 2016). For the desgn of tangble products, the theoretcal and practcal sgnfcance of the nteractve system can be understood: the products to be desgned n the nteractve system, wth the theory and prncples of nteractve system desgn to gude the product desgn, the entre multple elements of the nteractve desgn system. The nteractve desgn system of the whole mult elements as an organc whole, of the nature and functon of elements n the process of product desgn and analyss, determne a sutable soluton, fnally achevng the desgn goal(bayazt,2004). 3. INTERACTIVE GENETIC ALGORITHM Generally, the genetc algorthm adopts bnary encodng, and ts dsadvantage s that the strng s too long when the number of varables s large and the range of value s large, whch leads to the reducton of the convergence speed of the algorthm.to ths end, ths work uses the floatng pont codng method x ( x1, x2,..., x ) T n drectly wth ts component form x1, x2,..., x n to represent.currently, the genetc algorthm s often converted by penalty functon.for the unconstraned form, but the lmt of the penalty functon s dffcult, the ntensty wll affect the operaton effcency of the genetc algorthm,and the small ntensty can not acheve the expected penalty effect. N samples of random varables k ( k 1,2,..., N) are obtaned frst,under the condton that the dstrbuton functon of the random varable s known under the condton. The number of 1 N,..., satsfyng g ( x, ) 0( j 1,... p) s N. If N / N, the x satsfes the constrant condton(wang et al.,2007). j The sze of the populaton s represented by pop-sze, and the selecton of the ntal populaton drectly affects the speed of the teraton. In order to ensure the dversty and valdty of the ntal populaton, the followng random weght method s adopted:in [0, 1], we obtan m random numbers ( 1,..., m), f m /, we can solve the numercal mnmzaton problem: 1 m mn Ef ( x, ) (1) 1 Eg ( x, ) 0, j 1,... p (2) j Ef ( x, )( 1,... m) and Eg ( x, )( j 1,... p) are the mathematcal expectatons of f ( x, ) and gj ( x, ) respectvely, to obtan the optmal soluton verfy whether populaton j 1 x of queston 1, and then use stochastc smulaton to 1 x s a feasble soluton for queston 2. Repeat the above steps pop-sze tmes, and the ntal 1 pop sze ( x,... x ) can be obtaned 614

3 Revsta de la Facultad de Ingenería U.C.V., Vol. 32, N 11, pp , 2017 In order to obtan the ftness of the ndvdual n the populaton, the target value s calculated by the stochastc smulaton method, that s, the N samples ( 1,..., N) are generated from the probablty dstrbuton k of. For the ndvdual x n the populaton, suppose f f ( x, )( 1,..., m; k 1,..., N), usng the law of large numbers, t can be assumed that the target value of ndvdual x s as follows(asada et al.,2001): k q q1 q m (,..., ) T (3) Now, the number of ndvduals n the populaton s pre-ordered based on the number of Pareto actve ndvduals, and the number of classes of ndvduals x A s defned as rank( x). The ftness of ndvduals wth the same rank can be expressed as: F (max rank 1) / SS (4) Where, 1,...,max rank max rank SS (max rank 1) P / pop sze (5) 1 P :Represents the number of ndvduals n A. For the usual genetc algorthm, n order to ensure ts convergence, t s necessary to keep the optmal chromosome for each step operaton.for the mult-objectve programmng problem, a technque of Pareto optmal flter s proposed, that s, a set of vald ndvduals s output n parallel n each teraton, It selects ndvduals wth a number of 1 n each generaton to avod mssng the Pareto effcent soluton. If the number of ndvduals appearng n the flter exceeds ts specfed sze, the method of sharng functon s used to re select. and then to ensure the sze of the Pareto optonal flter.ths work constructs an exponental form of flterng functon, through whch the functon can descrbe the degree of smlarty between ndvduals,defne a nche number as(felferng et al.,2004): m x s( d( x, y)) (6) y p Where, s s flter functon,t s as follows: exp( d / ) 0 d sd ( ) 0 d (7) Where, d( x, y ) :The Hammng dstance between x and y; In ths way, the nche number s chosen, and the ndvduals wth small nche number are retaned. That s to say, fewer ndvduals wth smlar ndvduals can have more chances to be nherted nto the next generaton of Pareto optmal flter. 4. INTERACTIVE DESIGN MODEL AND IMPLEMENTATION The purpose of ths work s to ntroduce an nteractve genetc algorthm to assst future users n fndng ther own preferences. Because the user has an understandng of the process of the product, at the begnnng of the evaluaton, there s not a complete understandng of the product desgn proposal, the evaluaton can not fully 615

4 Revsta de la Facultad de Ingenería U.C.V., Vol. 32, N 11, pp , 2017 reflect the real user preferences, the use of IGA aded analyss of user preferences and product desgn optmzaton, the whole flow chart the model shown n fgure 2: Users Users Users Interactve product desgn nterface User nformaton analyss Optmzed wth IGA Product desgn database Fgure 2.Flow Graph of Mode After addng the herarchcal strategy, the herarchcal decson s added to the search process, such as satsfyng the stratfcaton, then enter the local search, determne the local search for the key gene segment, generate the ntal group n the local search area.the algorthm flow s as follows: Start Determne the genetc parameters Produce the ntal populaton Evaluates the ndvdual N Meet the end condton? Y Determne the gene segment End, output the result Fgure 3.Herarchcal nteractve genetc algorthm flow In ths work, we select the degree of dsperson based on the genotype of evolutonary populaton as the crteron for global search. The determnaton of the termnaton of the global search s that at least one of the same gene sense unts n the whole evoluton algebra begns to enter the local collecton. The collecton area s for all ndvduals of ths generaton, but the overall collecton s searched on a daly bass, Wth the subjectvty of human evaluaton, t s possble to contnue untl many algebras do not have the same gene meanng unts, whch s the lmtaton of the algorthm. And the evolutonary algebra of the model nvolved n the practcal applcaton s as small as possble. In order to mprove the lmtaton of the algorthm search, In ths work, the optmal preservaton strategy s used to evaluate the probablty of generatng the same gene meanng unt n the same generaton after the evaluaton of the user's generaton, nstead of the worst ndvdual of the generaton. 616

5 Revsta de la Facultad de Ingenería U.C.V., Vol. 32, N 11, pp , 2017.Set the envronment stablty algebra to 2, that s, after the frst generaton of evaluaton, startng from the second generaton can be consdered that the user has a relatvely clear understandng of the product, the evaluaton of the basc representatve of the user's preferences, you can fnd all ndvduals of ths generaton to see f they meet the global search end condton. Table 1. Stuaton of Global Search Stoppng Global search termnator algebra Number of dentcal gene unts Number of optmal gene unts After fve experments, we compared the results of the layered IGA and the tradtonal IGA, t s as shown n Table 2: Table2. Experment Result Contrast Between layered IGA and tradtonal IGA Project Algorthm class Evolutonary algebra Layered IGA Tradtonal IGA The optmal result s obtaned? Layered IGA Yes Yes Yes Yes Yes Tradtonal IGA No No Yes Yes No We can conclude that the evolutonary algebra of IGA gets the optmzaton result wthn 10 generatons, whle the tradtonal IGA only gets the optmal result only 2 tmes n the 5 experment.. It can be seen that the layered IGA not only shortens the algebra of the populaton evoluton, reduces the fatgue of the user, and the result s also deal. Fgure 4 s the average evaluaton of the layered IGA and conventonal IGA obtaned n the fve experments. Fgure 4.Average Assessment Value It can be seen from the fgure that wth the ncrease of evolutonary algebra, the average score of each generaton s gettng hgher and hgher, and the number of ndvduals that do not lke the number of users n 617

6 Revsta de la Facultad de Ingenería U.C.V., Vol. 32, N 11, pp , 2017 each generaton s decreasng, and the number of ndvdual users s ncreasng at the same tme. From the expermental results, the results of the layered IGA mprovement based on stratfed deas are satsfactory. 5. CONCLUSIONS The object of ths work s an nteractve desgn method based on Artfcal Intellgence. The process of nteractve product desgn s studed by ntroducng the process of nteractve genetc algorthm, modelng and algorthm solvng. Amng at the core problem of nteractve genetc algorthm, human fatgue s easy to lead to naccurate optmzaton results, the use of herarchcal thnkng of the tradtonal IGA has been mproved, and the effcency of the mproved algorthm s verfed by theoretcal and expermental smulaton. REFERENCES Asada M., MacDorman K.F., Ishguro H.,Kunyosh Y.(2001). Cogntve developmental robotcs as a new paradgm for the desgn of humanod robots, Robotcs and Autonomous Systems, 37(2): Bayazt N.(2004). Investgatng desgn:a revew of forty years of desgn research, Desgn ssues, 20(1): Coller M., Connop S., Corcoran A.(2016). European unversty-communty partnershp-based research on urban sustanablty and reslence, Current Opnon n Envronmental Sustanablty, 23: Candy L.,Edmonds E.(1996). Creatve desgn of the Lotus bcycle: mplcatons for knowledge support systems research. Desgn Studes, 17(1): Felferng A., Fredrch G., Jannach D., Stumptner M. (2004). Consstency-based dagnoss of confguraton knowledge bases, Artfcal Intellgence,152(2): Fong T., Nourbakhsh I., Dautenhahn K.(2003). A survey of socally nteractve robots, Robotcs and autonomous systems, 42(3): KmH.S.,ChoS.B.(2000). Applcaton of nteractve genetc algorthm to fashon desgn. Engneerng applcatons of artfcal ntellgence, 13(6): Raymond C.M., Gottwald S., Kuoppa J.(2016). Integratng multple elements of envronmental justce nto urban blue space plannng usng publc partcpaton geographc nformaton systems, Landscape and Urban Plannng, 153, Rgolon A., Németh J.(2016). A QUalty INdex of Parks for Youth (QUINPY): Evaluatng urban parks through geographc nformaton systems, Envronment and Plannng B: Plannng and Desgn, Wang F.Y., Carley K.M., Zeng D.(2007). Socal computng: From socal nformatcs to socal ntellgence, IEEE Intellgent Systems, 22(2). 618