Yi-Shin Chen and Cyrus shahabi n ext, the system randomly generates a list of conl dence values e and fuzzy cut value for each active user. Each conl dence value is represented y a fuzzy term, which is an integer in the range of [0, d. i inally, the sys- tem populates the preference values to g by aggregating Eand fig using Equation(6) GA, all perfect dge will be tuned in a noisy process. Based on the preference values in thi imperfect knowledge, the system then randomly selects a set of items and assigned feature values as the user navigation behaviors. These feature val ues are generated by a reverse procedure of Equation (11). i or example, the higher the preference value of an item is, the longer periods of view time is assigned to this item. i inally, the system reinitializes the user prol le by assigning imperfect conl dence values of navigation-pattern clusters and a fuzzy cut value to the imperfect user prol le 5.2 Experiu eyahl Resulas We conducted several sets of experiments to verify our sy stem performances and to compare the results of different Ga parameter settings. In these exper iments, we observed a signil cant margin of improvement after incorporating the ga in mat ching the user expectations in various settings. It is also sho that the performance improvement of our learning mechanism is independent of the number of users. Moreover, the improvement is linearly increased with he number of items. However, due to the space limit, in this paper, we only stress the improvements achieved by applying our learning mechani The results shown for each set of experiments are averaged over twent, runs, where each run is executed with different seeds for the random genera tor functions. The parameter settings of GA operators 20, 1d are: pop one-point crossover, tournament selection, keep the elitism, and mu tation rate=0.25. The benchmark set tings of the following l gures, i. e, the number of items, the number of cut-off point, the number of fuzzy terms, the number of experts, and the number of navigation-pattern clusters, are 1 xed 10, and N=5%, respectivel i igure 2 demonstrates the improvements achieved by our learning anism. The X-axis of i igure 2 depicts the number of generations. The Y-axis of i igure 2. a illustrates the similarity distance(between query results and perfect user feedback ) computed by Equation(10). The Y-axis of i igure 2. b represents average satisfaction computed by Equation(11), respectively. In this experiment, i igure 2 indicates that the accuracy of wish-lists improves drastically after incorporating the learning mechanism. As observed, the aver age satisfaction increases 50% within ten generations of the evolution and the similarity on ranking improves 100% after 40 generations of learning Overall, in the ma-ority of our experiments, GA can acquire nearly ideal user ol le the retrieval wish-list has 90% similarity to thos n the perfect wish-list, within 40 generations. It should be noticed that Yoda￾✣❹ ➳✸❵ ❸❡❜✦✇♥❵❚①❤✕✇♥❳✽❚✵❬✍❚❉❪①❤✈❝✦❩❇♠♥❞❍❜✦✇❉❬✍✇❉❬✍➝♥❵ ✝✖■✣é❉❒❦❐✕❒P❈❊■✿◗❭▼⑧◗❇❒P■✍✃ ◆✽❏✦❋▲❑⑧➱❛✃✵Ï▼ Ð❛■✍❋❊■✍◆✽❏❲❒P■❦◗✮❏✢Ï ❃④◗❇❒✵➱✦❮➽➮✣➱❛❋❆￾➩❑⑧■✍❋▲➮✣■➧Ò❲❏✦Ï❖❊■❦◗✁￾☎ ❏✦❋▲❑ ❏➢❮➊❖❊❰✍❰✍▼✢➮✣❖⑧❒❣Ò❲❏✦Ï❖❊■✮❮➊➱❛◆➽■❦❏❛➮✽❈✢❏❛➮❫❒P❃Ò❛■✮❖▲◗❭■✍◆❦ä ✟❍❏❛➮✽❈á➮✣➱❛❋❆￾➩❑⑧■✍❋▲➮✣■✮Ò❲❏✦Ï❖❊■✵❃④◗❄◆P■✍ß❊◆P■❦◗❭■✍❋♥❒P■❦❑ ❘❉▼✢❏➧❮➊❖❊❰✍❰✍▼å❒P■✍◆P✃✝❐✂ç✱❈❊❃④➮✽❈á❃④◗❣❏✦❋ ❃❋♥❒P■✍Ð❛■✍◆➽❃❋á❒P❈❊■①◆✽❏✦❋❊Ð❛■✮➱✦❮✰✯❭★✔✓❪✶✸❡ä❀❃✕❃❋▲❏✦Ï Ï▼❛❐❁❒P❈❊■➢◗❭▼⑧◗❇❅ ❒P■✍✃ ß➩➱❛ß❊❖❊Ï④❏❲❒P■❦◗❣❒P❈❊■☞ß❊◆P■✣❮➊■✍◆P■✍❋▲➮✣■➢Ò❲❏✦Ï❖❊■❦◗✛❒P➱ ✚✢✜ ✖ ➧❘❉▼à❏✦Ð❛Ð❛◆P■✍Ð♥❏❲❒P❃❋❊Ð✂￾☎ ❏✦❋▲❑ ￾✌ ✜➧❖▲◗❭❃❋❊Ð ✟❍Ñ♥❖▲❏❲❒P❃➱❛❋❵✜✪✵ ✣❫ä æ✈➱á◗❭❃✃✧❖❊Ï④❏❲❒P■☞❃✃✵ß➩■✍◆❭❮➊■❦➮❫❒①❖▲◗❭■✍◆✮❮➊■✍■❦❑⑧❘▲❏❛➮✽Óà❑⑧❖❊◆P❃❋❊Ð✢❒P❈❊■✕✮Þ ❐✟❏✦Ï Ï✸ß➩■✍◆❭❮➊■❦➮❫❒①Ó❉❋❊➱❲ç✱Ï ❅ ■❦❑⑧Ð❛■➽ç✱❃ Ï Ï✂❘➩■➽❒P❖❊❋❊■❦❑☞❃❋✿❏✵❋❊➱❛❃④◗❭▼➢ß❊◆P➱⑧➮✣■❦◗P◗✍ä❆✘✸❏❛◗❭■❦❑➧➱❛❋➧❒P❈❊■❣ß❊◆P■✣❮➊■✍◆P■✍❋▲➮✣■➽Ò❲❏✦Ï❖❊■❦◗✱❃❋☞❒P❈❊❃④◗ ❃✃✵ß➩■✍◆❭❮➊■❦➮❫❒✵Ó❉❋❊➱❲ç✱Ï■❦❑⑧Ð❛■❛❐✂❒P❈❊■☞◗❭▼⑧◗❇❒P■✍✃✓❒P❈❊■✍❋➯◆✽❏✦❋▲❑⑧➱❛✃✵Ï▼ ◗❭■✍Ï■❦➮❫❒✽◗✮❏å◗❭■✣❒✵➱✦❮✖❃❒P■✍✃①◗✮❏✦❋▲❑ ❏❛◗P◗❭❃Ð❛❋❊■❦❑✝❮➊■❦❏❲❒P❖❊◆P■✮Ò❲❏✦Ï❖❊■❦◗➽❏❛◗✖❒P❈❊■✵❖▲◗❭■✍◆❄❋▲❏tÒ❉❃Ð♥❏❲❒P❃➱❛❋å❘➩■✍❈▲❏tÒ❉❃➱❛◆✽◗✍ä➤æ✸❈❊■❦◗❭■✛❮➊■❦❏❲❒P❖❊◆P■✮Ò❲❏✦Ï ❅ ❖❊■❦◗✵❏✦◆P■☞Ð❛■✍❋❊■✍◆✽❏❲❒P■❦❑à❘❉▼ ❏á◆P■✍Ò❛■✍◆✽◗❭■➢ß❊◆P➱⑧➮✣■❦❑⑧❖❊◆P■☞➱✦❮ ✟❍Ñ♥❖▲❏❲❒P❃➱❛❋ ✜ ✱❉✱✤✣➢ä❁❃❊➱❛◆✵■✣é❊❏✦✃✵ß❊Ï■❛❐ ❒P❈❊■❄❈❊❃Ð❛❈❊■✍◆✐❒P❈❊■❄ß❊◆P■✣❮➊■✍◆P■✍❋▲➮✣■✡Ò❲❏✦Ï❖❊■❄➱✦❮✕❏✦❋➧❃❒P■✍✃ ❃④◗✍❐♥❒P❈❊■❄Ï➱❛❋❊Ð❛■✍◆✐ß➩■✍◆P❃➱⑧❑❊◗✐➱✦❮✈Ò❉❃■✍ç⑩❒P❃✃✵■ ❃④◗✛❏❛◗P◗❭❃Ð❛❋❊■❦❑✿❒P➱☞❒P❈❊❃④◗❣❃❒P■✍✃✝ä ❃✕❃❋▲❏✦Ï Ï▼❛❐❁❒P❈❊■➢◗❭▼⑧◗❇❒P■✍✃✩◆P■✍❃❋❊❃❒P❃④❏✦Ï ❃❰✍■❦◗❄❒P❈❊■①❖▲◗❭■✍◆❣ß❊◆P➱✥￾▲Ï■✵❘❉▼ ❏❛◗P◗❭❃Ð❛❋❊❃❋❊Ð✧✸✩❃✃✵ß➩■✍◆❭❮➊■❦➮❫❒✖➮✣➱❛❋❆￾➩❑⑧■✍❋▲➮✣■❄Ò❲❏✦Ï❖❊■❦◗❍➱✦❮✈❋▲❏tÒ❉❃Ð♥❏❲❒P❃➱❛❋⑧❅❡ß▲❏❲❒❭❒P■✍◆P❋➢➮✣Ï❖▲◗❇❒P■✍◆✽◗✐❏✦❋▲❑➧❏ ❮➊❖❊❰✍❰✍▼☞➮✣❖⑧❒✖Ò❲❏✦Ï❖❊■❄❒P➱✵❒P❈❊■❣❃✃✵ß➩■✍◆❭❮➊■❦➮❫❒✖❖▲◗❭■✍◆✱ß❊◆P➱✥￾▲Ï■❛ä ✖ ✎☛✡ ✡✌☞▲❍➚♥➘✞❍✪❖➚✙▼✴❏✶❈❋❊✠☞➧➚❉➷✴✢❊❑❏❦➷ ûá■❣➮✣➱❛❋▲❑⑧❖▲➮❫❒P■❦❑☞◗❭■✍Ò❛■✍◆✽❏✦Ï❁◗❭■✣❒✽◗✐➱✦❮✕■✣é⑧ß➩■✍◆P❃✃✵■✍❋♥❒✽◗✐❒P➱✵Ò❛■✍◆P❃❮➊▼①➱❛❖❊◆✱◗❭▼⑧◗❇❒P■✍✃ ß➩■✍◆❭❮➊➱❛◆P✃①❏✦❋▲➮✣■❦◗ ❏✦❋▲❑➽❒P➱➽➮✣➱❛✃✵ß▲❏✦◆P■✟❒P❈❊■✐◆P■❦◗❭❖❊Ï❒✽◗✈➱✦❮➩❑⑧❃â➤■✍◆P■✍❋♥❒✑✮Þ ß▲❏✦◆✽❏✦✃✵■✣❒P■✍◆✕◗❭■✣❒❭❒P❃❋❊Ð♥◗✍ä❦èq❋✛❒P❈❊■❦◗❭■✐■✣é⑧ß➩■✍◆❭❅ ❃✃✵■✍❋♥❒✽◗✍❐⑧ç✐■❄➱❛❘▲◗❭■✍◆PÒ❛■❦❑①❏✮◗❭❃Ð❛❋❊❃￾➩➮✍❏✦❋♥❒✸✃①❏✦◆PÐ❛❃❋➢➱✦❮✕❃✃✵ß❊◆P➱❲Ò❛■✍✃✵■✍❋♥❒✸❏❲❮➀❒P■✍◆✸❃❋▲➮✣➱❛◆Pß➩➱❛◆✽❏❲❒P❃❋❊Ð ❒P❈❊■✢✮Þ ❃❋✵✃①❏❲❒✽➮✽❈❊❃❋❊Ð❄❒P❈❊■✱❖▲◗❭■✍◆✟■✣é⑧ß➩■❦➮❫❒✽❏❲❒P❃➱❛❋▲◗②❃❋✮Ò❲❏✦◆P❃➱❛❖▲◗✟◗❭■✣❒❭❒P❃❋❊Ð♥◗✍ä❲è❡❒✼❃④◗✟❏✦Ï④◗❭➱❣◗❭❈❊➱❲ç✱❋ ❒P❈▲❏❲❒✟❒P❈❊■✸ß➩■✍◆❭❮➊➱❛◆P✃①❏✦❋▲➮✣■✸❃✃✵ß❊◆P➱❲Ò❛■✍✃✵■✍❋♥❒②➱✦❮➩➱❛❖❊◆✟Ï■❦❏✦◆P❋❊❃❋❊Ð❄✃✵■❦➮✽❈▲❏✦❋❊❃④◗❭✃ ❃④◗②❃❋▲❑⑧■✍ß➩■✍❋▲❑⑧■✍❋♥❒ ➱✦❮➤❒P❈❊■✡❋❉❖❊✃✧❘➩■✍◆❍➱✦❮✂❖▲◗❭■✍◆✽◗✍ä❉✿✿➱❛◆P■✍➱❲Ò❛■✍◆❦❐t❒P❈❊■✡❃✃✵ß❊◆P➱❲Ò❛■✍✃✵■✍❋♥❒✼❃④◗❍Ï ❃❋❊■❦❏✦◆PÏ▼✧❃❋▲➮✣◆P■❦❏❛◗❭■❦❑✮ç✱❃❒P❈ ❒P❈❊■➽❋❉❖❊✃✧❘➩■✍◆✸➱✦❮✕❃❒P■✍✃①◗✍ä❋✦✖➱❲ç✐■✍Ò❛■✍◆❦❐♥❑⑧❖❊■❄❒P➱✧❒P❈❊■❣◗❭ß▲❏❛➮✣■❄Ï ❃✃✵❃❒❦❐⑧❃❋➧❒P❈❊❃④◗✸ß▲❏✦ß➩■✍◆❦❐❉ç✐■❄➱❛❋❊Ï▼ ◗❇❒P◆P■❦◗P◗✐❒P❈❊■❣❃✃✵ß❊◆P➱❲Ò❛■✍✃✵■✍❋♥❒✽◗✸❏❛➮✽❈❊❃■✍Ò❛■❦❑➢❘❉▼➧❏✦ß❊ß❊Ï▼❉❃❋❊Ð✵➱❛❖❊◆✱Ï■❦❏✦◆P❋❊❃❋❊Ð✵✃✵■❦➮✽❈▲❏✦❋❊❃④◗❭✃✝ä æ✸❈❊■✵◆P■❦◗❭❖❊Ï❒✽◗➽◗❭❈❊➱❲ç✱❋å❮➊➱❛◆❣■❦❏❛➮✽❈á◗❭■✣❒❣➱✦❮✐■✣é⑧ß➩■✍◆P❃✃✵■✍❋♥❒✽◗➽❏✦◆P■✵❏tÒ❛■✍◆✽❏✦Ð❛■❦❑✝➱❲Ò❛■✍◆❄❒❇ç✐■✍❋♥❒❇▼ ◆P❖❊❋▲◗✍❐❉ç✱❈❊■✍◆P■✡■❦❏❛➮✽❈➧◆P❖❊❋➢❃④◗✐■✣é⑧■❦➮✣❖⑧❒P■❦❑➢ç✱❃❒P❈✝❑⑧❃â➤■✍◆P■✍❋♥❒✱◗❭■✍■❦❑❊◗❍❮➊➱❛◆❍❒P❈❊■➽◆✽❏✦❋▲❑⑧➱❛✃✭Ð❛■✍❋❊■✍◆✽❏❲❅ ❒P➱❛◆✕❮➊❖❊❋▲➮❫❒P❃➱❛❋▲◗✍ä❛æ✸❈❊■✸ß▲❏✦◆✽❏✦✃✵■✣❒P■✍◆②◗❭■✣❒❭❒P❃❋❊Ð♥◗②➱✦❮❀✮Þ ➱❛ß➩■✍◆✽❏❲❒P➱❛◆✽◗❄✯ ✷✥❭❊❐ ✱✞❪✶✸⑧❏✦◆P■❉✂tß➩➱❛ß❊❖❊Ï④❏❲❒P❃➱❛❋ ◗❭❃❰✍■✏❩❚❉❭❊❐t➱❛❋❊■✣❅❡ß➩➱❛❃❋♥❒✼➮✣◆P➱♥◗P◗❭➱❲Ò❛■✍◆❦❐✣❒P➱❛❖❊◆P❋▲❏✦✃✵■✍❋♥❒✼◗❭■✍Ï■❦➮❫❒P❃➱❛❋✂❐❲Ó❛■✍■✍ß✧❒P❈❊■✱■✍Ï ❃❒P❃④◗❭✃✝❐❛❏✦❋▲❑✧✃✧❖⑧❅ ❒✽❏❲❒P❃➱❛❋✝◆✽❏❲❒P■ ✏ ❭❊ä ✷ ✁ ä❊æ✸❈❊■❣❘➩■✍❋▲➮✽❈❊✃①❏✦◆PÓ☞◗❭■✣❒❭❒P❃❋❊Ð♥◗✱➱✦❮✕❒P❈❊■➽❮➊➱❛Ï Ï➱❲ç✱❃❋❊Ð❂￾▲Ð❛❖❊◆P■❦◗✍❐⑧❃❯ä ■❛ä ❐❊❒P❈❊■ ❋❉❖❊✃✧❘➩■✍◆❍➱✦❮❁❃❒P■✍✃①◗✍❐❛❒P❈❊■✖❋❉❖❊✃✧❘➩■✍◆❍➱✦❮✂➮✣❖⑧❒❭❅❡➱✦â☞ß➩➱❛❃❋♥❒❦❐❛❒P❈❊■✖❋❉❖❊✃✧❘➩■✍◆❍➱✦❮➤❮➊❖❊❰✍❰✍▼✮❒P■✍◆P✃①◗✍❐✦❒P❈❊■ ❋❉❖❊✃✧❘➩■✍◆✡➱✦❮✟■✣é⑧ß➩■✍◆❭❒✽◗✍❐➩❏✦❋▲❑➧❒P❈❊■✧❋❉❖❊✃✧❘➩■✍◆✡➱✦❮✟❋▲❏tÒ❉❃Ð♥❏❲❒P❃➱❛❋⑧❅❡ß▲❏❲❒❭❒P■✍◆P❋☞➮✣Ï❖▲◗❇❒P■✍◆✽◗✍❐➩❏✦◆P■✭￾❊é⑧■❦❑ ❏❲❒ ✏ ✏ ✁ ❭❉❭❉❭❊❐✡✘ ✏ ✱✶❭❉❭❊❐✛✜✓✏✫❱❊❐ ￾ ✏ ✁ ❭❊❐ ✸☛✏ ✱✶❭❊❐❊❏✦❋▲❑ ✘✓✏ ✁☎✄ ❐⑧◆P■❦◗❭ß➩■❦➮❫❒P❃Ò❛■✍Ï▼❛ä ❃✕❃Ð❛❖❊◆P■✰✷✮❑⑧■✍✃✵➱❛❋▲◗❇❒P◆✽❏❲❒P■❦◗❍❒P❈❊■➽❃✃✵ß❊◆P➱❲Ò❛■✍✃✵■✍❋♥❒✽◗✸❏❛➮✽❈❊❃■✍Ò❛■❦❑①❘❉▼➢➱❛❖❊◆✸Ï■❦❏✦◆P❋❊❃❋❊Ð✮✃✵■❦➮✽❈⑧❅ ❏✦❋❊❃④◗❭✃✝ä♥æ✸❈❊■✝✆✖❅q❏❲é⑧❃④◗✼➱✦❮◆❃✕❃Ð❛❖❊◆P■❩✷❣❑⑧■✍ß❊❃④➮❫❒✽◗✟❒P❈❊■✡❋❉❖❊✃✧❘➩■✍◆❍➱✦❮❁Ð❛■✍❋❊■✍◆✽❏❲❒P❃➱❛❋▲◗✍ä✦æ✸❈❊■✖❂✡❅q❏❲é⑧❃④◗ ➱✦❮✢❃✕❃Ð❛❖❊◆P■✚✷⑧ä ❏✿❃ Ï Ï❖▲◗❇❒P◆✽❏❲❒P■❦◗❣❒P❈❊■☞◗❭❃✃✵❃ Ï④❏✦◆P❃❒❇▼ ❑⑧❃④◗❇❒✽❏✦❋▲➮✣■P✜➊❘➩■✣❒❇ç✐■✍■✍❋➯Ñ♥❖❊■✍◆P▼á◆P■❦◗❭❖❊Ï❒✽◗✧❏✦❋▲❑ ß➩■✍◆❭❮➊■❦➮❫❒✱❖▲◗❭■✍◆✐❮➊■✍■❦❑⑧❘▲❏❛➮✽Ó❋✣❍➮✣➱❛✃✵ß❊❖⑧❒P■❦❑➧❘❉▼ ✟❍Ñ♥❖▲❏❲❒P❃➱❛❋ ✜ ✱✶❭ ✣❫ä❊æ✸❈❊■✡❂✡❅q❏❲é⑧❃④◗✐➱✦❮ ❃✕❃Ð❛❖❊◆P■✰✷⑧ä ❘ ◆P■✍ß❊◆P■❦◗❭■✍❋♥❒✽◗➽❏tÒ❛■✍◆✽❏✦Ð❛■✧◗P❏❲❒P❃④◗❇❮❾❏❛➮❫❒P❃➱❛❋ ➮✣➱❛✃✵ß❊❖⑧❒P■❦❑á❘❉▼ ✟❍Ñ♥❖▲❏❲❒P❃➱❛❋❨✜ ✱❉✱✤✣❫❐❁◆P■❦◗❭ß➩■❦➮❫❒P❃Ò❛■✍Ï▼❛ä❁èq❋ ❒P❈❊❃④◗❄■✣é⑧ß➩■✍◆P❃✃✵■✍❋♥❒❦❐✴❃✕❃Ð❛❖❊◆P■✻✷①❃❋▲❑⑧❃④➮✍❏❲❒P■❦◗✖❒P❈▲❏❲❒❄❒P❈❊■✮❏❛➮✍➮✣❖❊◆✽❏❛➮✣▼☞➱✦❮✼ç✱❃④◗❭❈⑧❅❡Ï ❃④◗❇❒✽◗✡❃✃✵ß❊◆P➱❲Ò❛■❦◗ ❑⑧◆✽❏❛◗❇❒P❃④➮✍❏✦Ï Ï▼❄❏❲❮➀❒P■✍◆✕❃❋▲➮✣➱❛◆Pß➩➱❛◆✽❏❲❒P❃❋❊Ð✐❒P❈❊■❍Ï■❦❏✦◆P❋❊❃❋❊Ð✖✃✵■❦➮✽❈▲❏✦❋❊❃④◗❭✃✝ä Þ◗✈➱❛❘▲◗❭■✍◆PÒ❛■❦❑❁❐❫❒P❈❊■✐❏tÒ❛■✍◆❭❅ ❏✦Ð❛■✖◗P❏❲❒P❃④◗❇❮❾❏❛➮❫❒P❃➱❛❋①❃❋▲➮✣◆P■❦❏❛◗❭■❦◗✟❋❊■❦❏✦◆PÏ▼ ✁ ❭✄ ç✱❃❒P❈❊❃❋①❒P■✍❋➢Ð❛■✍❋❊■✍◆✽❏❲❒P❃➱❛❋▲◗✟➱✦❮➤❒P❈❊■✡■✍Ò❛➱❛Ï❖⑧❒P❃➱❛❋✂❐ ❏✦❋▲❑✧❒P❈❊■✖◗❭❃✃✵❃ Ï④❏✦◆P❃❒❇▼✛➱❛❋✮◆✽❏✦❋❊Ó❉❃❋❊Ð➽❃✃✵ß❊◆P➱❲Ò❛■❦◗❄✱✶❭❉❭✄ ❏❲❮➀❒P■✍◆❁✲❭❄Ð❛■✍❋❊■✍◆✽❏❲❒P❃➱❛❋▲◗✕➱✦❮➤Ï■❦❏✦◆P❋❊❃❋❊Ð▲ä ✺Ò❛■✍◆✽❏✦Ï Ï❯❐❲❃❋✮❒P❈❊■✖✃①❏✞❅❇➱❛◆P❃❒❇▼❣➱✦❮❁➱❛❖❊◆✟■✣é⑧ß➩■✍◆P❃✃✵■✍❋♥❒✽◗✍❐ ✮Þ ➮✍❏✦❋✵❏❛➮✍Ñ♥❖❊❃ ◆P■✸❋❊■❦❏✦◆PÏ▼✧❃④❑⑧■❦❏✦Ï❊❖▲◗❭■✍◆ ß❊◆P➱✥￾▲Ï■❦◗✍❐✦❃❯ä ■❛❐❛❒P❈❊■✖◆✽❏✦❋❊Ó❉❃❋❊Ð♥◗✟❃❋①❒P❈❊■✖◆P■✣❒P◆P❃■✍Ò❲❏✦Ï▲ç✱❃④◗❭❈⑧❅❡Ï ❃④◗❇❒❍❈▲❏❛◗✑❯❉❭✄ ◗❭❃✃✵❃ Ï④❏✦◆P❃❒❇▼✛❒P➱❣❒P❈❊➱♥◗❭■ ❃❋✮❒P❈❊■✱ß➩■✍◆❭❮➊■❦➮❫❒✼ç✱❃④◗❭❈⑧❅❡Ï ❃④◗❇❒❦❐✦ç✱❃❒P❈❊❃❋❂✲❭❄Ð❛■✍❋❊■✍◆✽❏❲❒P❃➱❛❋▲◗✍ätè❡❒❍◗❭❈❊➱❛❖❊Ï④❑✧❘➩■✱❋❊➱✦❒P❃④➮✣■❦❑✧❒P❈▲❏❲❒✟❂✼➱⑧❑❊❏