source: src/main/java/agents/anac/y2019/winkyagent/winkyAgent.java@ 316

Last change on this file since 316 was 202, checked in by Katsuhide Fujita, 5 years ago

Add ANAC 2019 agents (3)
  • Property svn:executable set to *
File size: 13.6 KB
Line 
1package agents.anac.y2019.winkyagent;
2
3import java.util.*;
4
5import genius.core.AgentID;
6import genius.core.Bid;
7import genius.core.actions.Accept;
8import genius.core.actions.Action;
9import genius.core.actions.Offer;
10import genius.core.issue.Issue;
11import genius.core.issue.IssueDiscrete;
12import genius.core.issue.Value;
13import genius.core.issue.ValueDiscrete;
14import genius.core.parties.AbstractNegotiationParty;
15import genius.core.parties.NegotiationInfo;
16import genius.core.timeline.DiscreteTimeline;
17import genius.core.uncertainty.BidRanking;
18
19public class winkyAgent extends AbstractNegotiationParty {
20
21 private Bid lastReceivedBid = null;
22 private Map<Bid, Double> receiveBids = new HashMap<Bid, Double>();
23 private List<Bid> bidOrder = null;
24 int utilitySize = 0;
25 int ranklistSize = 0;
26 double receivehighestUtility = 0.0;//接收过的最高出价的效用
27 List<Issue> issueList = null;//issue列表
28 int issueSize = 0;//issue个数
29 int valueSum = 0;//value个数
30 double initUtility = 0.0;//value初始效用
31 Map<ValueDiscrete, Double> valueCorrespond = new HashMap<ValueDiscrete, Double>();//value和对应效用
32 ValueDiscrete[] values = null;//value数组
33 double learningRate;
34 List<Map.Entry<Bid, Double>> list = new ArrayList<>();//对receiveBids按照效用进行排序后得到的list
35 boolean listSort = true;
36 boolean lastBidTag = true;
37
38 @Override
39 public void init(NegotiationInfo info) {
40
41 super.init(info);
42 utilitySize = (int) utilitySpace.getDomain().getNumberOfPossibleBids();//一共可能有多少种出价
43 bidOrder = userModel.getBidRanking().getBidOrder();
44 ranklistSize = bidOrder.size();
45 issueList = utilitySpace.getDomain().getIssues();
46 issueSize = issueList.size();
47 double[] results = new double[ranklistSize];//给定bid效用数组
48 for (int i = 0; i < ranklistSize; i++) { //results数组初始化赋值
49 results[i] = getBidOrderUtility(bidOrder.get(i));
50 }
51
52 int[] valueSize = new int[issueSize];//第i个问题有j种选择
53 for (int i = 0; i < issueSize; i++) {
54 Issue issue = issueList.get(i);
55 IssueDiscrete issued = (IssueDiscrete) issue;//某个issue的value
56 valueSize[i] = issued.getNumberOfValues();
57 valueSum += valueSize[i];
58 }
59 initUtility = 1.0 / valueSum; //value初始化的值
60 learningRate = initUtility / 10.0;
61
62 values = new ValueDiscrete[valueSum];//value数组
63 int valuesIndexCnt = 0;
64 while (valuesIndexCnt < valueSum) { //初始化values数组和map valueCorrespond
65 for (int i = 0; i < issueSize; i++) {
66 Issue issue = issueList.get(i);
67 IssueDiscrete issued = (IssueDiscrete) issue;//某个issue的value
68 for (int j = 0; j < issued.getNumberOfValues(); j++) {
69 values[valuesIndexCnt] = issued.getValue(j); //初始化values数组
70 valueCorrespond.put(values[valuesIndexCnt], initUtility); //初始化map valueCorrespond
71 valuesIndexCnt++;
72 }
73 }
74 }
75
76 int[][] features = new int[ranklistSize][valueSum];//bidOrder训练集
77 for (int i = 0; i < ranklistSize; i++) {
78 HashMap<Integer, Value> valueHashMap = bidOrder.get(i).getValues();
79 int vhmSize = valueHashMap.size();
80// for(int z=1;z<=vhmSize;z++){
81// log(z+" "+valueHashMap.get(z));
82// }
83 int p = 1;
84 for (int j = 0; j < valueSum; j++) {
85 Value valueTemp = values[j];
86 Value valueOfbidOrder = valueHashMap.get(p);
87 if (valueTemp.equals(valueOfbidOrder) && p <= vhmSize) {
88 features[i][j] = 1;
89 p++;
90 } else {
91 features[i][j] = 0;
92 }
93 }
94// log("\n");
95 }
96
97 double[] parameters = new double[valueSum];//训练得到的value值
98 for (int i = 0; i < valueSum; i++) {
99 parameters[i] = initUtility;
100 }
101
102 for (int i = 0; i < ranklistSize * valueSum; i++) { //训练
103 BGD(features, results, learningRate, parameters);
104 }
105 }
106
107
108 private void BGD(int[][] features, double[] results, double learningRate, double[] parameters) {
109 for (int t = 0; t < valueSum; t++) {
110 double sum = 0.0;
111 double parametersSum = 0.0;
112 for (int j = 0; j < results.length; j++) {
113 for (int i = 0; i < valueSum; i++) {
114 parametersSum += parameters[i] * features[j][i];
115 }
116 parametersSum = parametersSum - results[j];
117 parametersSum = parametersSum * features[j][t];
118 sum += parametersSum;
119 }
120 double updateValue = 2 * learningRate * sum / results.length;
121 parameters[t] = parameters[t] - updateValue;
122 valueCorrespond.put(values[t], parameters[t]);
123
124 }
125// double totalLoss = 0;
126// for (int j = 0; j < results.length; j++) {
127// totalLoss = totalLoss + Math.pow((parameters[0] * features[j][0] + parameters[1] * features[j][1]
128// + parameters[2] * features[j][2] + parameters[3] - results[j]), 2);
129// }
130// System.out.println(parameters[0] + " " + parameters[1] + " " + parameters[2] + " " + parameters[3]);
131// System.out.println("totalLoss:" + totalLoss);
132 }
133
134 private double linearEstUtility(Bid bid) {
135 double linearUtility = 0.0;
136 HashMap<Integer, Value> valueHashMap = bid.getValues();
137 int vhmSize = valueHashMap.size();
138 int p = 1;
139 for (int j = 0; j < valueSum; j++) {
140 Value valueTemp = values[j];
141 Value valueOfbidOrder = valueHashMap.get(p);
142 if (valueTemp.equals(valueOfbidOrder) && p <= vhmSize) {
143 linearUtility += valueCorrespond.get(valueTemp);
144 p++;
145 }
146 }
147 return linearUtility;
148 }
149
150 private double getBidOrderUtility(Bid bid) //估计已知出价效用，等分
151 {
152 BidRanking bidRanking = getUserModel().getBidRanking();
153 Double min = bidRanking.getLowUtility();
154 double max = bidRanking.getHighUtility();
155
156 int i = bidOrder.indexOf(bid);
157
158 // index:0 has utility min, index n-1 has utility max
159 return min + i * (max - min) / (double) (ranklistSize - 1);
160 }
161
162
163 @Override
164 public Action chooseAction(List<Class<? extends Action>> validActions) {
165
166 int round = ((DiscreteTimeline) timeline).getRound();
167 int tround = ((DiscreteTimeline) timeline).getTotalRounds();
168 double receiveBidUtility = 0.0;
169 double bidOrderMax = userModel.getBidRanking().getHighUtility();
170 Bid bid;
171 if (round < tround * 0.7) {
172 if (round > 10 && receiveBids.size() < 7) {
173 int temp = (int) Math.ceil(ranklistSize * 0.1);
174 int randz = rand.nextInt(temp);
175 bid = bidOrder.get(ranklistSize - 1 - randz);
176 log("receiveBid<7,bidOrder: " + getBidOrderUtility(bid));
177 return new Offer(getPartyId(), bid);
178 }
179 bid = generateBid(7, bidOrderMax);
180 return new Offer(getPartyId(), bid);
181 } else if (round < tround * 0.98) {
182 if (receiveBids.size() < 10) {
183 int temp = (int) Math.ceil(ranklistSize * 0.15);
184 int randz = rand.nextInt(temp);
185 bid = bidOrder.get(ranklistSize - 1 - randz);
186 log("receiveBid<10,bidOrder: " + getBidOrderUtility(bid));
187 return new Offer(getPartyId(), bid);
188 }
189 bid = generateBid(9, bidOrderMax);
190 return new Offer(getPartyId(), bid);
191 } else if (round < tround * 0.99) {
192 receiveBidUtility = linearEstUtility(lastReceivedBid);
193 if (listSort) {
194 sortReceive();
195 listSort = false;
196 for (Map.Entry<Bid, Double> entry : list) {
197 System.out.println(entry);
198 }
199 log(receivehighestUtility + "\n");
200 }
201 if (receiveBidUtility > (receivehighestUtility - 0.03)) {
202 return new Accept(getPartyId(), lastReceivedBid);
203 }
204 bid = generateReceiveBid();
205 log("receive bid Utility: " + linearEstUtility(lastReceivedBid) + " accept阈值: " + (receivehighestUtility - 0.07) + "\n");
206 return new Offer(getPartyId(), bid);
207 } else if (round < tround * 0.995) {
208 receiveBidUtility = linearEstUtility(lastReceivedBid);
209 if (receiveBidUtility > (receivehighestUtility - 0.07)) {
210 return new Accept(getPartyId(), lastReceivedBid);
211 }
212 bid = generateReceiveBid();
213 log("receive bid Utility: " + linearEstUtility(lastReceivedBid) + " accept阈值: " + (receivehighestUtility - 0.11) + "\n");
214 return new Offer(getPartyId(), bid);
215 } else if (round == (tround-1)) {
216 return new Accept(getPartyId(), lastReceivedBid);
217
218 } else {
219 receiveBidUtility = linearEstUtility(lastReceivedBid);
220 if (receiveBidUtility > (receivehighestUtility - 0.1)) {
221 return new Accept(getPartyId(), lastReceivedBid);
222 }
223 bid = generateReceiveBid();
224 log("receive bid Utility: " + linearEstUtility(lastReceivedBid) + " accept阈值: " + (receivehighestUtility - 0.15) + "\n");
225 return new Offer(getPartyId(), bid);
226 }
227 }
228
229
230 public Bid generateBid(int zcnt, double bidOrderMax) {
231 Bid randomBid = null;
232 if (lastReceivedBid == null) {
233 randomBid = userModel.getBidRanking().getMaximalBid();
234 } else if (zcnt == 7) {
235 if (bidOrderMax > 0.9) {
236 do {
237 randomBid = generateRandomBid();
238 } while (linearEstUtility(randomBid) < 0.82);
239 } else if (bidOrderMax > 0.8) {
240 do {
241 randomBid = generateRandomBid();
242 } while (linearEstUtility(randomBid) < 0.75);
243 } else {
244 do {
245 randomBid = generateRandomBid();
246 } while (linearEstUtility(randomBid) < 0.7);
247 }
248 } else if (zcnt == 9) {
249 if (bidOrderMax > 0.9) {
250 do {
251 randomBid = generateRandomBid();
252 } while (linearEstUtility(randomBid) < 0.8);
253 } else if (bidOrderMax > 0.8) {
254 do {
255 randomBid = generateRandomBid();
256 } while (linearEstUtility(randomBid) < 0.7);
257 } else {
258 do {
259 randomBid = generateRandomBid();
260 } while (linearEstUtility(randomBid) < 0.68);
261 }
262 }
263
264 log(((DiscreteTimeline) timeline).getRound() + "generateBid: " + linearEstUtility(randomBid) + "\n");
265 return randomBid;
266 }
267
268 private Bid generateReceiveBid() {
269 Bid bid;
270
271 int listSelectUtility = (int) Math.ceil(list.size() * 0.03);
272 double temp = list.get(listSelectUtility - 1).getValue();
273
274 if (temp < 0.7) {
275 temp = 0.7;
276 do {
277 bid = generateRandomBid();
278 } while (linearEstUtility(bid) < temp);
279 log(((DiscreteTimeline) timeline).getRound() + " generateRandomBid: " + linearEstUtility(bid) + " temp:" + temp);
280 return bid;
281 } else {
282 if (lastBidTag) {
283 int rand1 = rand.nextInt(listSelectUtility);
284 bid = list.get(rand1).getKey();
285 lastBidTag = false;
286 log(((DiscreteTimeline) timeline).getRound() + " generateReceiveBid: " + linearEstUtility(bid) + " temp:" + temp);
287 return bid;
288 } else {
289 do {
290 bid = generateRandomBid();
291 } while (linearEstUtility(bid) < temp);
292 lastBidTag = true;
293 log(((DiscreteTimeline) timeline).getRound() + " generateRandomBid: " + linearEstUtility(bid) + " temp:" + temp);
294 return bid;
295 }
296 }
297
298 }
299
300 @Override
301 public void receiveMessage(AgentID sender, Action action) {
302 super.receiveMessage(sender, action);
303 if (action instanceof Offer) {
304 lastReceivedBid = ((Offer) action).getBid();
305 double lastReceivedBidUtility = linearEstUtility(lastReceivedBid);
306 receiveBids.put(lastReceivedBid, lastReceivedBidUtility);
307 if (lastReceivedBidUtility > receivehighestUtility) {
308 receivehighestUtility = lastReceivedBidUtility;
309 }
310 }
311 }
312
313 private void sortReceive() { //将收到的出价进行排序
314 for (Map.Entry<Bid, Double> entry : receiveBids.entrySet()) {
315 list.add(entry); //将map中的元素放入list中
316 }
317
318 list.sort(new Comparator<Map.Entry<Bid, Double>>() {
319 @Override
320 public int compare(Map.Entry<Bid, Double> o1, Map.Entry<Bid, Double> o2) {
321 double result = o2.getValue() - o1.getValue();
322 if (result > 0)
323 return 1;
324 else if (result == 0)
325 return 0;
326 else
327 return -1;
328 }
329 //逆序（从大到小）排列，正序为“return o1.getValue()-o2.getValue”
330 });
331 }
332
333 @Override
334 public String getDescription() {
335 return "ANAC2019";
336 }
337
338 private static void log(String s) {
339 System.out.println(s);
340 }
341
342}
343
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