Kun Chang Lee and Namho Lee (2007)
CARDS: Case-Based Reasoning Decision Support Mechanism for Multi-Agent Negotiation in Mobile Commerce
Journal of Artificial Societies and Social Simulation
vol. 10, no. 2, 4
<https://www.jasss.org/10/2/4.html>
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Received: 01-Nov-2004 Accepted: 01-Jan-2007 Published: 31-Mar-2007
Figure 1. Schematic diagram of CARDS architecture |
(1) |
where Nj indicates jth attribute value of a new case ( j =1,2,…,n), and Sijdenotes jth attribute value of ith case in the case base of CBR ( i =1,2,…, m). Netlogo source code for implementing the CBR function of CARDS using SI is listed in Table 1. N1,…, N4 are attributes of cases (N1 : Current inventory level, N2 : remaining period of validity, N3 : freshness level, N4 : number of potential buyers within reasonable range of distance from store). Table 1 describes how the case based reasoning performs.
to change-CBR-price locals [temp_t temp_i temp_si temp_optimal_si temp_item] set temp_t(1) repeat seller_number [ ask seller with [reg_number = temp_t and mobile_service = 1 and CARDS_service = 1 ] set temp_i (0) repeat length CBR_price [ set temp_si sqrt((N1_Current_Value - item (temp_i) CBR_N1_List) ^ 2 + ((N2_Current_Value - item (temp_i) CBR_N2_List) ^ 2 + (N3_Current_Value - item (temp_i) CBR_N3_List) ^ 2 + (N4_Current_Value - item (temp_i) CBR_N4_List) ^ 2) if (temp_i = 0)[set temp_optimal_si (temp_si) set temp_item (temp_i)] if (temp_i >= 1) [ if(temp_si < temp_optimal_si) [set temp_optimal_si (temp_si) set temp_item (temp_i )] ] set temp_i (temp_i + 1) ] set offer_product_price (item temp_item CBR_price) ] set temp_t(temp_t + 1) ] end |
Table 1. CBR function of CARDS |
(2) |
Ui denotes ith buyer's utility, Wij buyer's preference for jth utility factor, and Fij ith buyer's jth utility factor. It is certain that . Examples of utility factors include not only price, product, and quality, but also contextual information such as the buyer's current location and environmental constraints. As shown in Table 5, we consider five factors such as F1:Distance, F2:Freshness, F3:Category, F4:Price, F5:Timeliness. Therefore, five weights W1 ∼ W5 are assigned to each factor. Table 2 shows Netlogo source code for calculating the B-agent's utility function.
;; to Calculate-Util set temp (1) set temp_id (1) repeat customer_number [ ask customer with [reserve != 1 and id_number = temp_id] [ set temp_distance (p_distance ) set temp_price (p_price ) set temp_time (p_time ) set temp_customer_x (current_x) set temp_customer_y (current_y) set utility (0) set temp_selected_seller (0) repeat seller_number [ ask seller in-radius-nowrap (remaining_time / time_per_patch) with [available_product_number > 0 and reg_number = temp1][ set actual_distance (abs (sqrt((temp_customer_x - location_x) ^ 2 + (temp_customer_y - location_y) ^ 2 ) )) ;; Convert_factor_point set temp_util ( temp_W1 * temp_point_F1 + temp_W2 * temp_point_F2 + temp_W3 * temp_point_F3 + temp_W4 * temp_point_F4 + temp_W5 * temp_point_F5) if (temp_util > utility) [ set utility (temp_util) set temp_selected_seller (reg_number) ] ] set temp1 (temp1 + 1)] set temp1 (1) set utility_without_mobile (utility) set normal_selected_seller (temp_selected_seller) ] set temp_id (temp_id + 1) ] end |
Table 2. B-agent's utility calculation |
ask buyer with [deal !=1 ] [ set goal_utility (Current_utility + (utility_adjustment / 100) * Current_utility ) set temp (selected_buyer) ask seller with [reg_number = temp ][ if (available_product_number > 0) [ if (p_temp > 0 ) [ set temp_price_down_request int((goal_utility - Current_utility) / p_temp) ]]] |
Table 3. B-agent's price update process |
Figure 2. Logical diagram showing how CARDS works to coordinate between sellers and buyers |
to give-offer-to-CARDS-buyer locals [temp] ask customer [set negotiating_shop_list []] ask shop with [unsold_product_number > 0 and mobile_service = 1 ] [ set temp (reg_number) without-interruption [ foreach negotiating_customer_list [ ask customer with [id_number = ? and reserve != 1 and mobile_service = 1] [set negotiating_shop_list lput temp negotiating_shop_list ] ] ] ] end to nego-offer-to-shop [remaining_time] locals [temp temp_id temp_seller_no temp_no actual_distance ] set temp_no (1) ask shop [set mss_nego_custom_list []] repeat customer_number [ ask customer with [id_number = temp_no][ if ( reserve != 1 and CARDS_service = 1) [ set temp_id (id_number) ask shop in-radius-nowrap (remaining_time / time_per_patch) with [unsold_product_number > 0 and CARDS_service = 1] [set CARDS_nego_custom_list lput temp_id CARDS_nego_custom_list ] ] ] set temp_no (temp_no + 1) ] end |
Table 4. CARDS negotiation processing process |
(3) |
Depending on the situation that the potential buyer is facing at the time, the utility is determined as follows (Table 5).
Table 5: Buyer's utility | ||
Utility factor | Condition | Converted utility |
Distance (D) | Within 20 minutes Within 30 minutes Within 40 minutes Within 50 minutes More than 60 minutes | 50 40 30 20 10 |
Freshness (F) | 1,2 3,4 5,6 7,8 9,10 | 50 40 30 20 10 |
Category (C) | Preferred category Otherwise | 50 0 |
Price (P) | Price negotiation | 50 - (new price/list price) * 50 |
Timeliness (T) | If the buyer wants the product on offer Otherwise | 50 0 |
Figure 3. CARDS prototype implemented on Netlogo |
which is derived from the utility function
and send the adjusted price to the S-agents as a counter-offer. By consulting with CBR, S-agents determine whether to accept the counter-offer or not. If output attribute is 1, then the S-agents accept the counter-offer. Otherwise, the counter-offer is rejected.
(a) Buyer's average utility curve |
(b) Seller's average profit |
Figure 4. Average utility and profit curves |
Table 6: Simulation results for three mobile groups | ||||||
Simulation Round | Buyer's Average Utility | Seller's Average Profit | ||||
Non-Mobile Group | Passive-Mobile Group | Active-Mobile Group | Non-Mobile Group | Passive-Mobile Group | Active-Mobile Group | |
1 | 202 | 259 | 458 | 228,750 | 349,475 | 472,046 |
2 | 319 | 388 | 445 | 237,500 | 290,900 | 346,985 |
3 | 361 | 506 | 492 | 160,500 | 454,500 | 387,420 |
4 | 299 | 365 | 482 | 134,250 | 295,250 | 505,576 |
5 | 311 | 441 | 410 | 66,000 | 441,925 | 467,940 |
6 | 289 | 406 | 587 | 188,500 | 208,225 | 496,251 |
7 | 285 | 431 | 559 | 423,000 | 253,225 | 353,273 |
8 | 309 | 432 | 497 | 178,000 | 438,750 | 377,081 |
9 | 259 | 428 | 477 | 165,750 | 389,725 | 354,953 |
10 | 402 | 439 | 568 | 197,250 | 219,950 | 477,409 |
11 | 326 | 355 | 436 | 332,000 | 204,250 | 479,741 |
12 | 332 | 405 | 498 | 214,750 | 265,500 | 410,366 |
13 | 327 | 485 | 506 | 190,250 | 356,425 | 501,517 |
14 | 252 | 398 | 412 | 151,750 | 451,000 | 402,775 |
15 | 331 | 463 | 533 | 220,000 | 385,975 | 473,341 |
16 | 445 | 393 | 448 | 242,750 | 400,125 | 370,576 |
17 | 305 | 438 | 508 | 101,000 | 321,400 | 469,929 |
18 | 265 | 354 | 534 | 274,250 | 317,650 | 361,821 |
19 | 245 | 370 | 480 | 99,250 | 394,975 | 484,309 |
20 | 387 | 464 | 565 | 52,000 | 475,425 | 497,923 |
21 | 381 | 449 | 488 | 204,250 | 429,150 | 400,736 |
22 | 339 | 444 | 551 | 176,250 | 347,400 | 492,709 |
23 | 245 | 411 | 512 | 143,000 | 328,175 | 501,041 |
24 | 371 | 444 | 562 | 178,000 | 330,050 | 470,821 |
25 | 334 | 431 | 597 | 158,750 | 344,250 | 480,545 |
26 | 225 | 413 | 547 | 102,750 | 235,650 | 502,448 |
27 | 338 | 416 | 492 | 192,000 | 283,175 | 498,108 |
28 | 225 | 392 | 457 | 123,750 | 288,000 | 451,497 |
29 | 319 | 434 | 487 | 169,250 | 396,650 | 448,992 |
30 | 213 | 322 | 401 | 255,000 | 304,525 | 457,196 |
31 | 321 | 491 | 492 | 190,250 | 408,975 | 401,334 |
32 | 257 | 381 | 474 | 220,000 | 347,750 | 287,404 |
33 | 350 | 469 | 509 | 157,000 | 410,750 | 482,582 |
34 | 247 | 426 | 492 | 146,500 | 431,200 | 388,419 |
35 | 349 | 350 | 406 | 263,750 | 337,250 | 440,755 |
Average | 215 | 290 | 347 | 130,760 | 242,752 | 307,916 |
Table 7a: F-test for buyer's utility | |||||
Sum of Squares | df | Mean Square | F | Sig. | |
Between Groups | 624,207 | 2 | 312103.4 | 111.2982 | 0.00 |
Within Groups | 286,029 | 102 | 2804.209 | ||
Total | 910,236 | 104 | |||
Table 7b: F-test for seller's profit | |||||
Sum of Squares | df | Mean Square | F | Sig. | |
Between Groups | 1,146,975,891,893 | 2 | 573,487,945,946 | 121.8049 | 0.00 |
Within Groups | 480,241,710,489 | 102 | 4,708,252,064 | ||
Total | 1,627,217,602,382 | 104 | |||
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