Towards a Behavioural Agent-based Assistant for e-negotiations

Transcription

1 Towards a Behavoural Agent-based Assstant for e-negotatons Smone A. Ludwg, Gregory E. Kersten, Xanhua Huang Abstract Software agents typcally negotate on behalf of ther owners. For ths to be effectve the agent must be able to adequately represent the owners preferences. However, the process by whch ths knowledge s acqured s normally not taken nto consderaton. The dea of the proposed method s that a user uses an e-negotaton system and flls n the Thomas- Klmann questonnare whch s desgned to measure the conflct mode. The assstng software agent then uses an mplemented model to construct the user s concesson graph and suggests the concesson for each offer package e.g. n a mult-attrbute negotaton. The modelng of the concesson graph was desgned by comparng negotaton hstory graphs wth the fve conflct styles (competng, collaboratng, compromsng, avodng and accommodatng). Certan patterns were found whch allow to model and map the conflct styles to a concesson graph. It was found that people who had hgh compromsng styles produced a concave concesson graph, people who are hghly accommodatng generated a lnear graph, and people who had a hgh value for competng and compromsng had a convex concesson graph. I. INTRODUCTION Negotaton has been for decades a central subect of study n dscplnes such as economy, game theory, and management. When dscussng negotaton, t s mportant to dstngush between negotaton protocol and negotaton strategy. The protocol determnes the flow of messages between the negotatng partes, (.e. dctatng who can say what and when) and acts as the rules by whch the negotatng partes must abde by f they are to nteract. The protocol s necessarly publc. The strategy, on the other hand, s the way n whch a gven party acts wthn those rules n an effort to get the best outcome n the negotaton. For example, when and what to concede, and when to hold frm. The strategy of each partcpant s therefore necessarly prvate. Electronc negotatons are busness negotatons conducted electroncally (.e. va the Internet). Electronc negotaton systems can offer more features such as graph support, decson analyss, communcaton management, etc., than tradtonal negotatons due to the underlyng IT nfrastructure. All authors are wth the InterNeg Research Centre and the John Molson School of Busness at Concorda Unversty, Montreal, Quebec, Canada (emal: {sludwg,gregory,xanh_hu}@msb.concorda.ca). Recent technologcal and economcal advances provde the settng for e-negotaton research and requre effcent support of electronc negotatons, e.g. n electronc commerce. The goals of supportng negotatons through nformaton technology are to reduce transacton costs n e-negotatons, to fnd an optmal deal, to conduct checks durng the negotaton, to offer decson support or to provde argumentaton support for human or software agents. In electronc negotatons usually a seller and a buyer exchange offers wth the obectve of achevng a settlement. The goal of supportng negotatons through nformaton technology s to offer decson support for human or software agents. To some extent, agent technology can be helpful n automatng or assstng the buyer wth the need dentfcaton stage. Specfcally, agents can play an mportant role for those purchases that are repettve (e.g. supples) or predctable (e.g. habts). Interest n automated negotaton nvolvng mult-agent systems has been stmulated to a great extent by the vson of software agents negotatng wth other software agents to buy and sell goods and servces on behalf of ther owners n a future Internet-based global marketplace. Untl now, research has focused on accountng for partcular nteractons among agents by developng and mprovng specfcally talored negotaton protocols and strateges; where the former refers to the rules that the agent has to follow n order to partcpate n a negotaton, whle the latter refers to the ratonale for choosng a certan acton at a gven stage. Software agents typcally negotate on behalf of ther owners. For ths to be effectve the agent must be able to adequately represent the owners preferences. However, the process by whch ths knowledge s acqured s normally not taken nto consderaton. In order to overcome ths shortage, a possble soluton s presented by takng knowledge nto account to model owners preferences. The remander of ths paper s structured as follows. Secton II gves an ntroducton to electronc negotaton support systems. In Secton III related work s lsted by summarzng ther approaches and shortcomngs. Secton IV descrbes the research method proposed ncludng the modelng and mappng of the conflct styles to a concesson graph. Secton V concludes ths paper by presentng the fndngs and dscussng the shortcomngs as well as further works.

2 II. NEGOTIATIONS AND NEGOTIATION SUPPORT A. Negotaton support systems Negotaton support systems (NSS) are desgned to facltate the varous phases of the barganng process. Because negotatons are consdered complex and unstructured, NSS functonal requrements have emphaszed support capabltes whch are very general; as such, these systems nether lend themselves to nor are ntended to be fully automated. The tools for support are vared; many emphasze mathematcal support tools, such as decson trees, forecastng, and so forth. However, Jelass and Forough [1] have called for tools whch address behavoural characterstcs and cogntve perspectves of negotators. B. Negotaton stage model Durng the process of negotaton, the nteracton nvolves the dynamcs of ether escalaton or de-escalaton, pendng on the communcaton and negotaton sklls employed by both partes and medator [2]. In lterature, a negotaton process s descrbed by a three stage model [3]: antecedent phase, concurrent phase, and consequent phase. In the antecedent phase, negotators form ther barganng styles before the openng of talks. Snce barganng styles establshed before the negotaton affect the characterstcs of the subsequent nteracton, negotators who are postvely orented behave more cooperatvely than those who are negatvely orented [4, 5]. In the concurrent phase, negotators bargan and try to fnd solutons by exchangng nformaton and offers. In ths phase, they may choose to fght or cooperate pendng on the stuatonal factors and the progress of the negotaton. Fnally, n the consequence phase, negotators may ether reach an agreement or leave the conflct unresolved, dependng on the nteracton of both partes n the prevous stages. There are some ndependent varables reflectng negotator s characterstcs and stuatonal constrants n each stage [6, 7]. Generally, the barganng style and attractveness [4] are consdered most mportant n the antecedent phase; the concurrent phase can be descrbed n terms of tme spent, nformaton exchanged, offer proposed, degree of cooperaton, and concesson made [8]. Typcally, the consequence can be descrbed by obectve measures, (e.g. ont outcome, ndvdual outcome, contract balance) or by subectve measures (e.g. satsfacton and relatonshp). In the process of negotaton, the ways the negotators thnk about and approach to manage conflct are often more mportant n determnng ts outcome than the nature of the conflct tself [9]. Negotatons nvolve the nteracton between negotators, ncludng exchanges of ther arguments and counterarguments. Through these attempts to ustfy ther postons and persuade others, the negotators ontly arrve at the outcome. Because of ther nterdependence, the fact that the outcome can only be determned ontly and through the actons of both partes s the defnng characterstc of negotatons [10]. III. RELATED WORK Related research n ths area has mostly focused on an effcent and effectve algorthm enablng agents to be successful and obtan acceptable outcomes. Whle ths s defntely mportant, t s only one part of the soluton. Agents should represent as closely as possble the owner and negotate on behalf of ther owners. For ths to be effectve, agents must acqure the owners nterests, strateges, preferences and preudces n a gven doman. Wthout ths, software agents cannot execute ther task approprately. Therefore, the acquston of such knowledge s an essental requrement for applyng negotatng agents n practce. However, not much thought has been gven to the problems of determnng: What exactly knowledge an owner needs to mpart to ther agent n order to acheve hgh fdelty negotaton behavour. How such knowledge can be effectvely acqured from the owner. These are clearly serous shortcomngs of exstng research that need to be addressed f negotatng agents are to be wdely used. Guo et al. [11] nvestgate how agents that act on behalf of users n electronc negotatons by elctng nformaton about ther users preference structures. Based on a mult-attrbute utlty theoretc model of user preferences, an algorthm s proposed that enables an agent to learn the utlty functon over tme. The learnng method s based on an evolutonary framework wth three-step learnng n each generaton. It combnes populaton-based evoluton wth the possblty to apply external knowledge and wth ndvdual learnng through smulated annealng for further refnement of the soluton. The learnng method reveals good performance n the smulated experments. It s shown that a substantal mprovement of basc learnng can be acheved by addng the steps of knowledge ntegraton and local search. Luo et al. [12] analyze an automated negotaton model whereby user trade-off preferences were found to play a fundamental role n negotaton. Wth the method proposed users trade-off preferences were captured, modelng the man commonaltes of trade-off relatons and reflectng users ndvdualtes. The basc dea behnd the method has three steps: 1. The system queres the user about choce features n order to determne whch attrbutes the trade-off relatons exst between. 2. In order to determne the shape of the trade-off curve, the system queres the user about the relatve mportance degree of one attrbute aganst another and about some features of trade-off curves. 3. The system queres the user about hs satsfacton degree for each trade-off alternatve.

3 Luo et al. [13] devsed a default-then-adust acquston technque, whereby the system conducts a structured ntervew wth the user to suggest the attrbutes of the trade-off, and then t asks the user to adust the default preferences of the trade-off alternatves. Ther research seeks to start brdgng the knowledge acquston gap between the negotatng agents owners and the negotaton algorthms that ther agents use. Ther paper explores how the necessary knowledge about a user s negotaton trade-offs can be acqured. The overall method s descrbed as follows: 1. The system queres the user about choce features n order to determne whch attrbutes to trade-off. The system queres about related attrbutes that could be used to tradeoff. 2. In order to shape the trade-off strategy, the system ntally presents a default. Then t pretends that t s a seller and makes a number of concessons on one attrbute and asks the user (buyer) for the lmt of the other attrbute s/he would be wllng to worsen to compensate. It reshapes the trade-off strategy accordng to the user s adustments. Later on, the user can make more adustments n ths way or can ask the system to set the trade-off strategy back to ts default. 3. The system can re-shape the default preference accordng to the user s adustment of the parameters, whch confgure the trade-off preference on the alternatves n the strategy set. The goal of the descrbed related work s to model the user s preference models and trade-off alternatves. The modelng and acqurng of knowledge s done usng many dfferent approaches such as learnng algorthms and modelng of a range of strateges and tactcs to acqure necessary doman knowledge. Our approach s dfferent as t uses a measurement to dentfy the conflct mode, thereby acqurng behavoural knowledge. was ntroduced by Blake and Mouton [14] n the md 1960s. These fve classfcatons are competng, collaboratng, compromsng, accommodatng and avodng. Thomas and Klmann [15] developed these fve classfcatons to elct and test the fve conflct model posted by Blake and Mouton s model. It s a useful tool for probng barganng styles n a classroom settng. Shell [16] summarzed hs fndng of the usefulness as follows: ease of admnstraton (t takes only about ten mnutes to take and score); relatve freedom from socal desrablty bases n the way statements n the nstrument are presented; conflct styles that match up wth strategy concept wdely used n the negotaton lterature; and sgnfcant congruence between the classfcatons and ther perceptons of ther own behavour across a set of smulatons. Thomas and Klmann dd not develop the measures wth barganng or negotaton n mnd. Rather, they were nterested n fndng a measurement devce for probng the valdty and ndependence of the fve conflct modes hypotheszed by Blake and Mouton [14]. However, the dual concerns model had been plagued by problems as the varance n results appeared to be strongly lnked to subects desre to exhbt socally desrable trats rather than to ther actual preferences for one conflct mode. Thomas and Klmann addressed ths problem by parng smple, equally desrable or undesrable phrases representng each conflct atttude and forcng subects to choose between the statements n each par. It was the smplcty of the statements, ther repetton, and the need for subects to select one aganst other (.e. equally compellng or repellng statements mnmzes socal desrablty varance). The fve conflct styles, as shown n Fg. 1, are now descrbed n more detal. IV. PROPOSED METHOD The dea of the proposed method s that a user uses an e- negotaton system and flls n the Thomas-Klmann questonnare whch s desgned to measure the conflct mode. The assstng software agent uses an mplemented model to construct the user s concesson graph and suggests the concesson for each offer and attrbutes e.g. n a mult-attrbute negotaton. The model of the concesson graph s desgned by comparng utlty graphs from a real negotaton experment wth the fve conflct styles. Certan patterns are found whch allow to model and map the conflct styles to a concesson graph. A. Thomas-Klmann Instrument Many negotaton courses and executve tranng programs cover the subect of barganng styles. The Thomas-Klmann Conflct Mode Instrument s a commonly used psychologcal assessment tool and measures the fve dfferent behavoural classfcatons proposed by the Dual Concerns Model: whch Fg 1. Fve styles of Thomas-Klmann conflct mode Cooperatveness s defned as attemptng to satsfy the other party s concern; and assertveness s defned as attemptng to satsfy one s own concern. Accordng to the dual concern model, any person s conflct management style s a functon of people s concern for self and concern for others. These two dmensons of behavour can be used to defne fve knds of dfferent styles of dealng wth conflcts. Competng s defned n negotatons where the stakes are hgh, tme s lmted, and bluffng s possble. The compettve

4 style can domnate the barganng process, meanng compettve people can negatvely nfluence relatonshps. In addton, compettve negotators nstnctvely focus on the ssues that are easest to count n terms of wnnng and losng, e.g. money. No quanttatve ssues may be overlooked that can yeld substantal value. Accommodatng s used to descrbe behavour that s both unassertve and cooperatve. An ndvdual wth accommodatng style tends to neglect hs or her own concerns to satsfy the concerns of other persons. It means accommodators try to meet the needs of others at the cost of ther own nterest, whch s a self-sacrfce style. Avodng s used to descrbe behavour that s unassertve and uncooperatve. An ndvdual wth avodng style tends not to mmedately pursue hs or her own concerns or those of the others. They usually avod, postpone or wthdraw rather than address the conflct at hand. It s a passve style of conflct resoluton. Collaboratng s used to descrbe behavour that s both assertve and cooperatve. An ndvdual wth collaboratng style tends to work wth another person to fnd a soluton that fully satsfes the concerns of both partes. Ths knd of negotators follow the problem solvng strategy [10]. As a Wn-Wn style, t usually nvolves a process of fndng preferences of each party and reachng an agreement by a creatve soluton. It s an actve style seekng mutually benefcal solutons. Compromsng s a hybrd of the above four. It s used to descrbe behavour that s both ntermedate n assertveness and cooperatveness. In the process of conflct resoluton, the obect s to fnd a mutually acceptable soluton for both partes. As a result, some degree of concesson has to be made by partes nvolved. Compromsng s a style between competng and accommodatng; smlarly, t s also les n the mddle of avodng and collaboratng. Research provdes farly strong support for the dual-concern models. Multdmensonal plots of nterrelatonshps show that the fve conflct styles ft n a two-dmensonal space n the manner theorsts suggest [17, 18]. In addton, there s no better or worse among these fve styles. A style approprate n one stuaton mght be napproprate n another conflct context. In the process of conflct resoluton, people wth dfferent styles tend to exhbt dfferent strengths and weaknesses [16]. B. Invte NSS The Invte software [19] s a negotaton support system platform manly developed for the protocol-drven generaton of systems. Ther purpose s prmarly educatonal: they are used to teach the subect of negotaton. The maor features of the Invte platform are: 1. Implementaton of a negotaton methodology, n partcular the process model and ts varous actvtes. 2. Support for multple, concurrent negotaton protocols, decson models, and nterfaces. 3. Provsonng of an ntutve web-based user nterface. The Invte platform allows users to negotate a case ndependently of tme and place restrctons. The system provdes the user wth general and prvate nformaton about the case, allows to rate the ssues and optons, allows to send messages and offers, and provdes a hstory to vew exchanges n a graphcal form. Ths platform s currently under expermental use where dfferent protocols are beng nvestgated. The dfferent protocols are dstnct from each other by the avalablty of analytcal support and the provson of predetermned preferences. The experment has three stages: pre-negotaton stage (questonnare), negotaton stage and post-negotaton stage (questonnare). So far, one treatment has been evaluated whch conssted n the pre-questonnare stage of the Thomas-Klmann questonnare, quz, expectatons and BATNA (best alternatve to a negotated agreement), case ratngs of ssues and optons. In the negotaton stage the Invte system was used, and n the post-questonnare stage questons about system adopton and the user s and opponent s conflct modes were asked. 46 partcpants successfully negotated a case of a contract negotaton between a snger and a musc agency. Out of these 23 negotatons, 12 reached an agreement. Ths sample data was used for the proposed method. C. Negotaton Data Data was extracted from the database of the Invte system. It ncluded the Thomas-Klmann (TK) questonnare questons whch needed to be calculated for the fve TK conflct styles. Besdes that the negotaton graphs were taken and, where possble, the dstrbuton of the curve categorzed nto concave, lnear and convex dstrbuton. A concave dstrbuton means that bg concessons are made frst and then at the end only small concessons are gven. A lnear dstrbuton means that equal concessons are made each tme step, and a convex dstrbuton characterzes small concessons at the begnnng wth bgger concessons made at the end. Addtonally, the concesson of each tmestamp was taken to calculate the relatve concesson made and the absolute value. TABLE I TK MEASURES AND DEGREE OF CONCESSION Curve Relatve Concesson Value of Accuracy Concave TK3 62% Lnear TK1 + TK3 69% Convex TK5 / 2 72% By analysng the data t was found that the conflct style collaboratng was not strongly represented and therefore dd not nfluence the model. The conflct style avodng was delberately dscarded as ths conflct style descrbes behavour that s unassertve and uncooperatve, whch would mean that ths person would not pursue a negotaton n the frst place. Hence, the avodng conflct style was not consdered for the evaluaton. Used n the upcomng tables are the followng abbrevatons:

5 TK1 represents competng, TK3 represents compromsng, TK5 represents accommodatng. Table I shows the relatve concesson made n regard to the TK conflct styles. The relatve concesson represents the gradent of the dstrbuton. For the concave dstrbuton t was found that TK3 roughly results n the gradent of the curve. For the lnear dstrbuton the addton of TK1 and TK3 was found to be representatve, and n the case of the convex dstrbuton, 50% of TK5 was found representatve. The accuracy of the relatve concesson value s measured comparng the actual relatve concesson of the sample data wth the value of the proposed formula. TABLE II WHARTON-TKI GRID BORDER HIGH 25% TK1 TK3 TK The result of applyng the Wharton Grd Hgh 25% values s shown n Table III. It shows the summary of TK conflct styles n regard to the curve dstrbuton. The accuracy for each curve dstrbuton s gven n the last column and compares the actual TK values of the sample data wth the proposed curve dstrbuton. Therefore, hgh values n compromsng dctates a concave dstrbuton curve, hgh values n competng and compromsng results n a convex dstrbuton and hgh values n accommodatng results n a lnear dstrbuton. TABLE III TK MEASURES AND CURVE DISTRIBUTION TK1 TK3 TK5 Curve Accuracy 1 Concave 67% 1 1 Convex 71% 1 Lnear 65% D. Concesson Modelng Relatve Concesson x α e β x γ e x Tme Steps Fg. 3. Relatve Concesson Graph Fg 2. Wharton Barganng Styles Grd [16] To analyse the data, the Wharton Grd [16] was used. The Wharton Grd, shown n Fg. 2, was ntroduced by Shell n order to adapt the raw scores derved to the barganng context. TK scores were collected from over 1600 global executves partcpatng n negotaton tranng sessons. Taken for ths nvestgaton were the TK values for the Hgh 25% Wharton Grd as shown n Table II. The results of Table II and III allows the modelng of the concesson graph for the user behavour accordng to ther TK conflct styles. The concesson s measured wth utlty (or value) functon. The equaton for the concesson graph can be constructed as follows, assumng the shape of the concesson curve to be exponental: v = δ v1 + δ v2 + δ k v3 (1) where: x v1 = α e (1a) represents the concave dstrbuton of the concesson curve, v2 = β x (1b) represents the lnear dstrbuton of the concesson curve, x v3 = γ e (1c)

6 represents the convex dstrbuton of the concesson curve, 1 for = 1 δ = (1d) 0 for = 0 represents the factor for the concave part, 1 for = 1 δ = (1e) 0 for = 0 represents the factor for the lnear part, 1 for k = 1 δ k = (1f) 0 for k = 0 represents the factor for the convex part. Factor x represents the tme step normalzed by an nterval T. The three dfferent dstrbutons are shown n Fg. 3 where the relatve concesson s shown versus the tme steps durng a negotaton. E. Concesson Algorthm The followng algorthm (see Fg. 4) s derved from the prevous equatons. Input varables: TKmeasures[]: Vector of TK values before Wharton Grd TK[]: Vector of TK measure after Wharton Grd deltas[]: Delta value for concave, lnear and convex dstrbuton factors[]: concave, lnear, convex Output varable: formula: Equaton of concesson graph Intalzaton: deltas[] 0 TKmeasures[] calculatetkmeasuresfrom- Questonnare() TK[] applywhartongrdhgh25%(tkmeasures) factors[] calculatefactorvalues(tkmeasures) Computaton: f TK1=0 and TK3=1 and TK5=0 then deltas[] setdeltaconcave() end f f TK1=0 and TK3=0 and TK5=1 then deltas[] setdeltalnear() end f f TK1=1 and TK3=1 and TK5=0 then deltas[] setdeltaconvex() end f formula generateeq(factors[],deltas[]) return formula Fg. 4. Concesson Calculaton Algorthm Frst, the TK measures are calculated from the questonnare whch results n values n the range of 0 and 12 for all conflct styles. After that, the Wharton Grd s appled consderng only the hgh 25% score values. Dependng on the TK values, the delta values are defned accordng to equaton (1d)-(1f) and at last, the formula s constructed and returned (accordng equaton (1)). Then, the factor values for the concave, lnear and convex dstrbuton are calculated accordng equaton (1a)-(1c). Note that the algorthm only consders people wth conflct styles whch fall nto one of the three categores. F. Agent-based Assstant : User submts Questonnare Model OfferGeneraton Assstant regsters ask for offer returns offer decdes whether to modfy offer Fg. 5. Interacton dagram of concesson modelng and offer constructon The agent-based assstant s supportng the user durng the negotaton; more specfcally, the assstant suggests the offers whch were constructed wth the use of the model. The model contans the three dfferent curve dstrbutons, however to construct an offer for a blateral mult-attrbute negotaton, the use of the blateral negotaton model s necessary and s defned as follows: Let ( { a, b} ) represent the negotatng agents and, ( { 1,..., n} ) the ssues to negotate about. The set of ssues n real world negotatons s always fnte. Let x [mn, max ] be a value for ssue acceptable by agent. Each agent has a scorng functon: S : [mn, max ] [0,1], whch gves the score agent assgned to a value for ssue n the range of ts acceptable values, whereby the scores are kept n the nterval [0, 1]. The specfcaton of weght values to calculate the relatve mportance that an agent assgns to each ssue under negotaton s done as follows. w s the mportance of ssue for agent. Assumed s here that the weghted sum of both agents are normalzed,.e. w = n S ( x 1 ) 1, for all n { a, b}. Wth these elements n place, t s now possble to defne an agent s scorng functon for a negotaton that s, for a value x = ( x1,..., xn ) n the mult-dmensonal space defned by the ssues value ranges:

7 S ( x) = w S ( x ). 1 n Ths calculaton s used by the agent to construct the offer. However, for the agent to be able to compute the offer, the user s requested to enter weghted values and the mnmum and maxmum values for each ssue. The agent-based assstant wll use the concesson graph and wll suggest and construct an offer to the user. The user can then decde whether to take the offer and send t to the counterpart or whether an amendment should be done. Fg. 5 shows the nteracton dagram of the user and the assstant agent. The user s requested to fll n the TK questonnare, whch n turn s been used to construct the model. When a user wants to make the frst offer, a message s sent to the assstant. The assstant wll then compute and construct the offer usng the model and wll send the offer back to the user. The user wll then decde whether to accept the offer or to modfy t before sendng t to the counterpart. V. CONCLUSION Our approach asssts users n developng a strategy for electronc negotatons, whereby they are only requred to fll n the Thomas-Klmann questonnare, whch s desgned to measure the conflct mode. The assstng software agent uses an mplemented model, whch uses the data of the Thomas- Klmann questonnare to construct the user s concesson graph and suggests the concesson for each offer and attrbutes (e.g. n a mult-attrbute negotaton). Results showed that people who had hgh compromsng values had a concave concesson graph, people who had hgh accommodatng values had a lnear graph and people who had a hgh value for competng and compromsng had a convex concesson graph. Usng the Thomas-Klmann questonnare s clearly a good step forward to model behavour for concesson modelng n electronc negotatons. However, one of the drawbacks s that the assstant can only deal wth negotators whch fall nto one of the proposed categores. For example, people wth a strong conflct style n collaboratng are not accounted for. Ths clearly needs to be nvestgated further. Another drawback of ths approach s that for the modelng process a large sample sze s necessary. The sample sze of 46, avalable for ths nvestgaton, was too small for accurate results as evdent from the accuracy values shown n Table I and III. It s ntended to expand ths work as soon more data from the negotaton experments, whch are currently underway, becomes avalable. One consderaton has been left out throughout ths paper whch s the opponent s negotaton style. The reacton of an offer from the opponent s defntely an mportant nfluental factor for the counter offer of a negotator and also needs to be nvestgated. ACKNOWLEDGEMENT Ths research s supported by grants from the Intatve for New Economy of the Socal Scences and Humantes Research Councl Canada, and the Natural Scence and Engneerng Research Councl Canada. 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