source: src/main/java/agents/anac/y2016/farma/etc/negotiationStrategy.java@ 345

Last change on this file since 345 was 1, checked in by Wouter Pasman, 6 years ago

Initial import : Genius 9.0.0
File size: 5.6 KB
Line 
1package agents.anac.y2016.farma.etc;
2
3import java.util.ArrayList;
4import java.util.HashMap;
5
6import genius.core.Bid;
7import genius.core.utility.AbstractUtilitySpace;
8
9public class negotiationStrategy {
10 private AbstractUtilitySpace utilitySpace;
11 private negotiationInfo negotiationInfo;
12 private double df = 0.0; // 割引係数
13 private double rv = 0.0; // 留保価格
14
15 private boolean isPrinting = false; // デバッグ用
16
17 public negotiationStrategy(AbstractUtilitySpace utilitySpace,
18 negotiationInfo negotiationInfo, boolean isPrinting) {
19 this.utilitySpace = utilitySpace;
20 this.negotiationInfo = negotiationInfo;
21 this.isPrinting = isPrinting;
22 df = utilitySpace.getDiscountFactor();
23 rv = utilitySpace.getReservationValue();
24 }
25
26 // 受容判定
27 public boolean selectAccept(Bid offeredBid, double time) {
28 try {
29 if (utilitySpace.getUtility(offeredBid) >= getThreshold(time)) {
30 return true;
31 } else {
32 return false;
33 }
34 } catch (Exception e) {
35 System.out.println("受容判定に失敗しました");
36 e.printStackTrace();
37 return false;
38 }
39 }
40
41 // 交渉終了判定
42 public boolean selectEndNegotiation(double time) {
43 // 時刻timeの割引効用を考慮した留保価格が閾値を上回った時EndNegotiation
44 if (utilitySpace.getReservationValueWithDiscount(time) >= getThreshold(time))
45 return true;
46
47 double weightedDF = df * 2.0;
48 if (time > 0.5 && rv > weightedDF) {
49 ArrayList<Object> opponents = negotiationInfo.getOpponents();
50 double minDIST = 1.0;
51 double maxEmax = 0.0;
52 for (Object sender : opponents) {
53 minDIST = Math.min(minDIST,
54 negotiationInfo.calSpaceDistByInitOffer(sender));
55 maxEmax = Math.max(maxEmax, calEmax(sender));
56 }
57 if (maxEmax < minDIST)
58 return true;
59 }
60 return false;
61 }
62
63 public boolean isWorthNegotiation() {
64 boolean isWorth = true;
65 // HashMap
66
67 return isWorth;
68 }
69
70 // 閾値を返す
71 public double getThreshold(double time) {
72 double lowerLimitThreshold = calLowerLimitThreshold(time);
73 double threshold = 1.0;
74
75 // 割引効用が存在するかどうか
76 if (utilitySpace.isDiscounted()) {
77 /*
78 * 割引効用が存在する場合、早期の合意形成が要求される したがって、閾値を早めに下げておく必要性がある
79 */
80 threshold = 0.95; // 序盤から閾値を少し下げておくことでOfferの柔軟性を少し上げる
81 double temp = 1.0 - (1 - df) * Math.log1p(time * (Math.E - 1));
82 // double temp = 1.0 - (1-df) * Math.pow(Math.E, time - df);
83 threshold = Math.min(threshold, temp);
84
85 } else {
86 /*
87 * 割引効用が存在しない場合、早くに交渉を成立させる意味がなくなるため、より長く交渉を続けることが要求される
88 * つまり、bidデータを多く活用できるため統計的なアプローチでも一定の意味を持つという判断のもと以下のような閾値をとることとする
89 */
90 threshold = statisticalThreshold(time, threshold);
91
92 }
93 // 閾値の下限との比較
94 threshold = Math.max(threshold, lowerLimitThreshold);
95
96 return threshold;
97
98 }
99
100 public double calEmax(Object sender) {
101 double m = negotiationInfo.getAverage(sender);
102 double sd = negotiationInfo.getStandardDeviation(sender);
103 return m + (1 - m) * calWidth(m, sd);
104 }
105
106 public double statisticalThreshold(double time, double threshold) {
107 double originalAlpha = 3.5 + rv; // 最初は線形的に閾値を下げていく
108 HashMap<Object, Double> emaxs = new HashMap<Object, Double>();
109 HashMap<Object, Double> targets = new HashMap<Object, Double>();
110
111 ArrayList<Object> opponents = negotiationInfo.getOpponents();
112 for (Object sender : opponents) {
113 // alphaが大きいほど強硬、小さいほど譲歩に柔軟な姿勢を示す
114 double alpha = Math
115 .max(0.1,
116 originalAlpha
117 - calAlpha(negotiationInfo
118 .calAssertiveness(sender),
119 negotiationInfo
120 .calCooperativeness(sender)));
121
122 // double m = negotiationInfo.getAverage(sender);
123 // double v = negotiationInfo.getVariancer(sender);
124 // double sd = negotiationInfo.getStandardDeviation(sender);
125
126 emaxs.put(sender, calEmax(sender));
127 double target = 1 - (1 - emaxs.get(sender)) * Math.pow(time, alpha);
128 targets.put(sender, target);
129
130 threshold = Math.min(threshold, target);
131 }
132 return threshold;
133 }
134
135 /**
136 * 統計情報を元に相手の変位幅を推定
137 *
138 * @param m
139 * @param sd
140 * @return
141 */
142 public double calWidth(double m, double sd) {
143 if (m > 0.0 && m < 1.0) {
144 return Math.sqrt(3.0 / (m - m * m)) * sd;
145 } else {
146 return Math.sqrt(12) * sd;
147 }
148 }
149
150 /**
151 * 割引効用がない場合の譲歩の強硬・柔軟性を調整する
152 *
153 * @param assertiveValue
154 * @param cooperativeValue
155 * @return
156 */
157 public double calAlpha(double assertiveValue, double cooperativeValue) {
158 double ans = cooperativeValue - assertiveValue;
159 return ans;
160 }
161
162 /**
163 * 現在の割引効用下の留保価格以上の効用値を得ることができる効用値を閾値の下限とする
164 *
165 * @param time
166 * @return
167 */
168 public double calLowerLimitThreshold(double time) {
169 double lowerLimitThreshold = rv;
170 if (utilitySpace.isDiscounted()) {
171 lowerLimitThreshold = 0.30 + rv * 0.2;
172 } else {
173 lowerLimitThreshold = 0.55 + rv * 0.2;
174 }
175
176 ArrayList<Object> opponents = negotiationInfo.getOpponents();
177 double minEmax = 1.0;
178 for (Object sender : opponents) {
179 minEmax = Math.min(minEmax, calEmax(sender));
180 }
181
182 lowerLimitThreshold = Math.max(lowerLimitThreshold, minEmax);
183 if (rv == 0.0 && time > 0.99) {
184 lowerLimitThreshold *= 0.85;
185 }
186 System.out.println("LowerThreshold4: " + lowerLimitThreshold);
187 return lowerLimitThreshold;
188 }
189
190}
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