source: src/main/java/agents/anac/y2019/dandikagent/dandikAgent.java

Last change on this file was 201, checked in by Katsuhide Fujita, 5 years ago

Add ANAC 2019 agents (2)
  • Property svn:executable set to *
File size: 11.2 KB
Line 
1package agents.anac.y2019.dandikagent;
2
3import java.util.*;
4import java.util.stream.Collectors;
5import java.util.stream.DoubleStream;
6import java.util.stream.IntStream;
7
8import agents.org.apache.commons.math.stat.regression.OLSMultipleLinearRegression;
9import genius.core.AgentID;
10import genius.core.Bid;
11import genius.core.actions.Accept;
12import genius.core.actions.Action;
13import genius.core.actions.Offer;
14import genius.core.issue.Issue;
15import genius.core.issue.IssueDiscrete;
16import genius.core.issue.Value;
17import genius.core.issue.ValueDiscrete;
18import genius.core.parties.AbstractNegotiationParty;
19import genius.core.parties.NegotiationInfo;
20import genius.core.uncertainty.ExperimentalUserModel;
21import javafx.util.Pair;
22
23
24/**
25 * This is dandikAgent by Mehmet Mert Ozgun
26 */
27public class dandikAgent extends AbstractNegotiationParty {
28
29 private Bid currentBid = null;
30 double[] sortedUtils;
31 int oppMaxBidIndex = 0;
32 HashMap uniqueBids = new HashMap();
33 List<List<ValueDiscrete>> allIssues = new ArrayList<>();
34 int countAll;
35 OLSMultipleLinearRegression regression;
36 ArrayList<Bid> allBidsList;
37 double[] utilities;
38 List<List<String>> allIssuesAsString;
39 List<List<String>> allPossibleBids;
40 List<Pair<List<String>, Double>> ourUtilityModel;
41 ArrayList<Bid> sortedList;
42 double[] allBidPredictions;
43 boolean amIFirst = false;
44 List<Bid> bidOrder;
45 private double[][] myModel;
46 NegotiationInfo info_;
47 List<Issue> issues;
48 private int updateCount;
49 List<Bid> uniqueBids2 = new ArrayList<>();
50
51 @Override
52 public void init(NegotiationInfo info) {
53
54 super.init(info);
55 info_ = info;
56 issues = info.getUserModel().getDomain().getIssues();
57 utils.getIssueDiscrete(issues, allIssues);
58 allIssuesAsString = issuesAsString();
59 allPossibleBids = utils.generateAllPossibleBids(allIssuesAsString, 0);
60 reverse(allPossibleBids);
61 countAll = utils.getIssueCount(allIssues);
62 bidOrder = info.getUserModel().getBidRanking().getBidOrder();
63
64 if(countAll > bidOrder.size()){
65 myModel = new double[getUtilitySpace().getDomain().getIssues().size()][];
66
67 // Creating the 2d array by initializing non-fixed size rows
68 for (int i = 0; i < getUtilitySpace().getDomain().getIssues().size(); i++) {
69
70 IssueDiscrete issueDiscrete = (IssueDiscrete) generateRandomBid().getIssues().get(i);
71 myModel[i] = new double[issueDiscrete.getValues().size()];
72 }
73
74 updateWeights();
75 System.out.println(Arrays.deepToString(myModel));
76 } else {
77
78 double[][] oneHotEncoded = utils.encodeBids(bidOrder, countAll, allIssues);
79 utilitySpace = getUtilitySpace();
80 double[][] oneHotEncodedAll = utils.encodeListOfStrings(allPossibleBids, countAll, allIssues);
81 utilities = new double[bidOrder.size()];
82
83 utilities[0] = 1;
84 for (int i = 1; i < utilities.length; i++) {
85 utilities[i] = utilities[i - 1] + 1;
86 }
87
88 regression = new OLSMultipleLinearRegression();
89 regression.newSampleData(utilities, oneHotEncoded);
90
91 allBidPredictions = new double[oneHotEncodedAll.length];
92
93 for (int i = 0; i < oneHotEncodedAll.length; i++) {
94 allBidPredictions[i] = utils.predict(regression, oneHotEncodedAll[i]);
95 }
96
97 allBidsList = new ArrayList<>();
98 for (int i = 0; i < allPossibleBids.size(); i++) {
99 allBidsList.add(utils.asBid(info.getUserModel().getDomain(), convertToStringArray(allPossibleBids.get(i).toArray())));
100 }
101
102 final List<Bid> allBidsCopy = allBidsList;
103 sortedList = new ArrayList<>(allBidsCopy);
104 sortedList.sort(Comparator.comparing(s -> allBidPredictions[allBidsCopy.indexOf(s)]));
105 sortedUtils = Arrays.stream(allBidPredictions).sorted().toArray();
106 ourUtilityModel = new ArrayList<>();
107 for (int i = 0; i < sortedList.size(); i++) {
108 ourUtilityModel.add(new Pair<>(utils.bidToListOfString(sortedList.get(i)), sortedUtils[i]));
109 }
110
111
112 System.out.println(Arrays.toString(sortedUtils));
113 }
114
115
116
117
118
119
120 }
121
122 private int getIndexOfValueInIssue(Bid bid, int issueIndex, String value) {
123 IssueDiscrete is = (IssueDiscrete) bid.getIssues().get(issueIndex);
124 return is.getValueIndex(value);
125 }
126
127 private void updateWeights() {
128 int numberOfIssues = getUtilitySpace().getDomain().getIssues().size();
129 for(int j = 0; j < bidOrder.size(); j++){
130 for (int i = 0; i < numberOfIssues; i++) {
131 ValueDiscrete currentBidValue = (ValueDiscrete) (bidOrder.get(j).getValue(i + 1));
132 int currentBidValueIndex = getIndexOfValueInIssue(bidOrder.get(j), i, currentBidValue.getValue());
133
134 if(bidOrder.indexOf(bidOrder.get(j)) >= bidOrder.size() * 0.9){
135 myModel[i][currentBidValueIndex] += 1;
136 updateCount++;
137 }
138 }
139 }
140
141 }
142
143 @Override
144 public Action chooseAction(List<Class<? extends Action>> validActions) {
145
146
147 if(bidOrder.size() > countAll){
148
149 uniqueBids.put(currentBid, 1);
150
151
152 List<Double> sortedUtilsList = Arrays.stream(sortedUtils)
153 .boxed()
154 .collect(Collectors.toList());
155
156 int lower = 0;
157 if (timeline.getCurrentTime() > 998) {
158 lower = (int) (sortedUtilsList.size() - 1);
159 } else if (timeline.getCurrentTime() > 975) {
160 lower = (int) (0.999 * sortedUtilsList.size());
161 } else if (timeline.getCurrentTime() > 950) {
162 lower = (int) (0.997 * sortedUtilsList.size());
163 } else if (timeline.getCurrentTime() > 900) {
164 lower = (int) (0.996 * sortedUtilsList.size());
165 } else {
166 lower = (int) (0.995 * sortedUtilsList.size());
167 }
168
169 int upper = sortedUtilsList.size();
170
171 Double randomElement = sortedUtilsList.get((int) ((Math.random() * (upper - lower)) + lower));
172
173 int indexOfRandom = sortedUtilsList.indexOf(randomElement);
174
175
176
177 if (oppMaxBidIndex < sortedList.indexOf(currentBid)) {
178 oppMaxBidIndex = sortedList.indexOf(currentBid);
179 }
180
181 boolean acceptable = sortedList.indexOf(currentBid) > sortedUtilsList.size() * 0.93;
182
183 if (timeline.getCurrentTime() >= 999) {
184 if (oppMaxBidIndex < sortedList.size() * 0.85)
185 return new Offer(getPartyId(), sortedList.get(sortedList.size() - 1));
186 else if (acceptable)
187 return new Accept(getPartyId(), currentBid);
188 else
189 return new Offer(getPartyId(), sortedList.get(oppMaxBidIndex));
190 } else if (timeline.getCurrentTime() < 600 &&
191 sortedList.indexOf(currentBid) > sortedUtilsList.size() * 0.985) {
192 return new Accept(getPartyId(), currentBid);
193 } else if (timeline.getCurrentTime() < 990 && timeline.getCurrentTime() > 600 &&
194 sortedList.indexOf(currentBid) > sortedUtilsList.size() * 0.985) {
195 return new Accept(getPartyId(), currentBid);
196 } else if (timeline.getCurrentTime() > 990 && acceptable && timeline.getCurrentTime() < 999) {
197 if (oppMaxBidIndex < sortedList.size() / 2)
198 return new Offer(getPartyId(), sortedList.get(sortedList.size() - 1));
199 else
200 return new Accept(getPartyId(), currentBid);
201 } else {
202 return new Offer(getPartyId(), sortedList.get(indexOfRandom));
203 }
204 } else {
205
206 if(updateCount != 0){
207 HashMap<Integer, Value> values = new HashMap();
208 for(int i = 0; i < issues.size(); i++){
209 IssueDiscrete issueDiscrete = (IssueDiscrete) generateRandomBid().getIssues().get(i);
210 int rn = rand.nextInt(issueDiscrete.getValues().size()) ;
211
212 if(myModel[i][rn] == 0)
213 i--;
214 else {
215 Value val = new ValueDiscrete(issueDiscrete.getValue(rn).toString());
216 values.put(i+1, val);
217 }
218
219 }
220
221 Bid b = new Bid(info_.getUserModel().getDomain(), values);
222
223 if(!uniqueBids2.contains(b)){
224 uniqueBids2.add(b);
225 }
226
227 if(timeline.getCurrentTime() < 990){
228 return new Offer(getPartyId(), bidOrder.get(bidOrder.size()-1));
229 } else{
230 if(uniqueBids2.contains(currentBid)){
231 return new Accept(getPartyId(), currentBid);
232 } else if(estimateUtilitySpace().getUtility(currentBid) > 0.8){
233 return new Accept(getPartyId(), currentBid);
234 } else
235 return new Offer(getPartyId(), b);
236
237 }
238 } else {
239 if(timeline.getCurrentTime() < 999)
240 return new Offer(getPartyId(), bidOrder.get(bidOrder.size()-1));
241 else if (timeline.getCurrentTime() < 1000)
242 return new Offer(getPartyId(), generateRandomBid());
243 else
244 return new Accept(getPartyId(), currentBid);
245
246 }
247
248
249 }
250
251 }
252
253 // If we hold 2 bids, update the weights with it!
254 @Override
255 public void receiveMessage(AgentID sender, Action action) {
256 try {
257 if (action == null) {
258 System.out.println("Hey! I am first!");
259 amIFirst = true;
260
261 }
262
263 super.receiveMessage(sender, action);
264 if (action instanceof Offer) {
265 currentBid = ((Offer) action).getBid();
266
267 }
268 } catch (Exception e) {
269 e.printStackTrace();
270 }
271 }
272
273
274 private static String[] convertToStringArray(Object[] array) {
275 String[] result = new String[array.length];
276 for (int i = 0; i < array.length; i++) {
277 result[i] = array[i].toString();
278 }
279 return result;
280 }
281
282 private void reverse(List<List<String>> allPossibleBids) {
283 for (List<String> sublist : allPossibleBids)
284 Collections.reverse(sublist);
285 }
286
287 private List<List<String>> issuesAsString() {
288 List<List<String>> allIssuesAsString = new ArrayList<>();
289 for (int i = 0; i < allIssues.size(); i++) {
290 List<String> current = new ArrayList<>();
291 for (int j = 0; j < allIssues.get(i).size(); j++) {
292 current.add(allIssues.get(i).get(j).toString());
293
294 }
295 allIssuesAsString.add(current);
296 }
297 return allIssuesAsString;
298 }
299
300
301 @Override
302 public String getDescription() {
303 return "ANAC2019";
304 }
305
306}
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