source: anac2020/DUOAgent/src/main/java/nego2020/group9/DUOAgent.java

Last change on this file was 34, checked in by wouter, 3 years ago

#3
File size: 11.3 KB
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1package nego2020.group9;
2
3import java.util.ArrayList;
4import java.util.Arrays;
5import java.util.Collections;
6import java.util.Comparator;
7import java.util.HashMap;
8import java.util.HashSet;
9import java.util.List;
10import java.util.Map;
11import java.util.Random;
12import java.util.Set;
13import java.util.logging.Level;
14
15import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression;
16
17import geniusweb.actions.Accept;
18import geniusweb.actions.Action;
19import geniusweb.actions.Comparison;
20import geniusweb.actions.Offer;
21import geniusweb.actions.PartyId;
22import geniusweb.inform.ActionDone;
23import geniusweb.inform.Finished;
24import geniusweb.inform.Inform;
25import geniusweb.inform.Settings;
26import geniusweb.inform.YourTurn;
27import geniusweb.issuevalue.Bid;
28import geniusweb.issuevalue.DiscreteValue;
29import geniusweb.issuevalue.NumberValue;
30import geniusweb.issuevalue.Value;
31import geniusweb.issuevalue.ValueSet;
32import geniusweb.party.Capabilities;
33import geniusweb.party.DefaultParty;
34import geniusweb.profile.Profile;
35import geniusweb.profileconnection.ProfileConnectionFactory;
36import geniusweb.profileconnection.ProfileInterface;
37import geniusweb.progress.Progress;
38import geniusweb.progress.ProgressRounds;
39import tudelft.utilities.logging.Reporter;
40
41public class DUOAgent extends DefaultParty {
42
43 private Bid lastReceivedBid = null;
44 private PartyId me;
45 private Random random;
46 private OLSMultipleLinearRegression regression;
47 protected ProfileInterface profileint;
48 private SimpleLinearOrdering estimatedProfile = null;
49 private List<List<String>> allPossibleBids;
50 private List<Bid> sortedPredictedBids;
51 private int turn_count = 0;
52 private List<Bid> opponets_bid;
53 private List<Bid> my_bids;
54 private int current_bid;
55 private int total_round;
56 private boolean dont_repeat = true;
57 private int repeating_count = 0;
58 private double least_w = Double.MAX_VALUE;
59
60 public DUOAgent() {
61 }
62
63 public DUOAgent(Reporter reporter) {
64 super(reporter);
65 }
66
67 @Override
68 public Capabilities getCapabilities() {
69 return new Capabilities(new HashSet<>(Arrays.asList("SHAOP")), Collections.singleton(Profile.class));
70 }
71
72 @Override
73 public String getDescription() {
74 return "DUO AGENT";
75 }
76
77 @Override
78 public void notifyChange(Inform info) {
79 try {
80 if (info instanceof Settings) {
81 Settings settings = (Settings) info;
82 this.profileint = ProfileConnectionFactory.create(settings.getProfile().getURI(), getReporter());
83 this.me = settings.getID();
84 Progress progress = settings.getProgress();
85 if (progress instanceof ProgressRounds) {
86 total_round = ((ProgressRounds) progress).getTotalRounds();
87 }
88 this.opponets_bid = new ArrayList<>();
89 this.sortedPredictedBids = new ArrayList<>();
90 this.my_bids = new ArrayList<>();
91 current_bid = 0;
92 estimatedProfile = new SimpleLinearOrdering(profileint.getProfile());
93 elicitation();
94 } else if (info instanceof ActionDone) {
95 Action otheract = ((ActionDone) info).getAction();
96 if (otheract instanceof Offer) {
97 lastReceivedBid = ((Offer) otheract).getBid();
98 opponets_bid.add(lastReceivedBid);
99 } else if (otheract instanceof Comparison) {
100
101 estimatedProfile = estimatedProfile.with(((Comparison) otheract).getBid(),
102 ((Comparison) otheract).getWorse());
103 turn_count++;
104 myTurn();
105 }
106 } else if (info instanceof YourTurn) {
107 turn_count++;
108 myTurn();
109 } else if (info instanceof Finished) {
110 getReporter().log(Level.INFO, "Final outcome:" + info);
111 }
112 } catch (Exception e) {
113 throw new RuntimeException("Failed to handle info", e);
114 }
115
116 }
117
118 public void myTurn() throws Exception {
119
120 Action actiontotake;
121 boolean chec = true;
122 boolean last_turns = false;
123
124 if (total_round - 1 < opponets_bid.size() / 2) {
125 actiontotake = new Accept(me, lastReceivedBid);
126 getConnection().send(actiontotake);
127 last_turns = true;
128
129 }
130 if (turn_count > 15 && !last_turns) {
131 // acceptance
132 if (my_bids.size() > 1 && calculate_weight(lastReceivedBid) >= least_w) {
133 actiontotake = new Accept(me, lastReceivedBid);
134 getConnection().send(actiontotake);
135 chec = false;
136 }
137
138 }
139 // bidding
140 if (chec && !last_turns) {
141 Bid bid = opponentSituation();
142 my_bids.add(bid);
143 actiontotake = new Offer(me, bid);
144 if (calculate_weight(bid) < least_w)
145 least_w = calculate_weight(bid);
146 current_bid++;
147 getConnection().send(actiontotake);
148 }
149 }
150
151 public Bid opponentSituation() {
152 Map<Bid, Integer> last10round = new HashMap<>();
153 int max_repeated_count = Integer.MIN_VALUE;
154 int bid_count = 0;
155 if (opponets_bid.size() > 9) {
156 for (int i = opponets_bid.size() - 10; i < opponets_bid.size(); i++) {
157 if (last10round.containsKey(opponets_bid.get(i))) {
158 last10round.replace(opponets_bid.get(i), last10round.get(opponets_bid.get(i)) + 1);
159 if (last10round.get(opponets_bid.get(i)) > max_repeated_count)
160 max_repeated_count = last10round.get(opponets_bid.get(i));
161 } else {
162 last10round.put(opponets_bid.get(i), 1);
163 if (max_repeated_count == Integer.MIN_VALUE)
164 max_repeated_count = 1;
165 bid_count++;
166 }
167 }
168
169 if (max_repeated_count >= 5 && dont_repeat) {
170
171 current_bid = current_bid - max_repeated_count > 0 ? current_bid - max_repeated_count : 0;
172 dont_repeat = false;
173 } else if (bid_count < 5 && dont_repeat) {
174 current_bid = current_bid - (5 - bid_count) > 0 ? current_bid - 5 + bid_count : 0;
175 dont_repeat = false;
176 } else {
177 random = new Random();
178 double rr = random.nextDouble();
179 repeating_count++;
180 if (repeating_count > 3) {
181 dont_repeat = true;
182 repeating_count = 0;
183 }
184 if (rr < 0.5)
185 current_bid = current_bid - (int) (rr * 10) > 0 ? current_bid - (int) (rr * 10) : current_bid;
186 }
187 }
188
189 return sortedPredictedBids.get(current_bid);
190 }
191
192 public double calculate_weight(Bid x) {
193 return 1.0 - ((double) sortedPredictedBids.indexOf(x) / (double) sortedPredictedBids.size());
194 }
195
196 public static List<List<String>> generateAllPossibleBids(List<List<String>> input, int i) {
197
198 if (i == input.size()) {
199 List<List<String>> result = new ArrayList<>();
200 result.add(new ArrayList<>());
201 return result;
202 }
203
204 List<List<String>> result = new ArrayList<>();
205 List<List<String>> recursive = generateAllPossibleBids(input, i + 1);
206
207 for (int j = 0; j < input.get(i).size(); j++) {
208 for (int k = 0; k < recursive.size(); k++) {
209 List<String> newList = new ArrayList<String>(recursive.get(k));
210 newList.add(input.get(i).get(j));
211 result.add(newList);
212 }
213 }
214 return result;
215 }
216
217 public void elicitation() {
218 int howmanyvalue = 0;
219 try {
220
221 List<List<String>> allValues = new ArrayList<>();
222 List<List<Value>> allValuesAsValues = new ArrayList<>();
223 List<String> types = new ArrayList<>();
224 Set<String> issues = profileint.getProfile().getDomain().getIssues();
225
226 for (String s : issues) {
227 ArrayList<String> temp = new ArrayList<>();
228 ArrayList<Value> temp_v = new ArrayList<>();
229 ValueSet vs = profileint.getProfile().getDomain().getValues(s);
230 for (Value v : vs) {
231 howmanyvalue++;
232 if (v instanceof DiscreteValue) {
233 temp.add(((DiscreteValue) v).getValue());
234 temp_v.add(v);
235 types.add("Discrete");
236 } else if (v instanceof NumberValue) {
237 temp.add(((NumberValue) v).getValue().toString());
238 temp_v.add(v);
239 types.add("Number");
240 }
241 }
242 allValues.add(temp);
243 allValuesAsValues.add(temp_v);
244 }
245 this.allPossibleBids = generateAllPossibleBids(allValues, 0);
246 double[][] encodedValues = oneHotEncoder(allPossibleBids, howmanyvalue, allValuesAsValues);
247
248 List<List<String>> givenBids = new ArrayList<>();
249 List<Bid> sb = estimatedProfile.getSortedBids((profileint.getProfile()));
250 for (Bid b : sb) {
251 List<String> temp_ls = new ArrayList<>();
252 for (String is : issues) {
253 if (b.getValue(is) instanceof DiscreteValue) {
254 temp_ls.add(((DiscreteValue) b.getValue(is)).getValue());
255 } else if (b.getValue(is) instanceof NumberValue) {
256 temp_ls.add(((NumberValue) b.getValue(is)).getValue().toString());
257 }
258 }
259 givenBids.add(temp_ls);
260 }
261 double[][] encodedTrainValues = oneHotEncoder(givenBids, howmanyvalue, allValuesAsValues);
262
263 double[] indexes = new double[encodedTrainValues.length];
264 for (int i = 1; i <= indexes.length; i++)
265 indexes[i - 1] = i * 2;
266 regression = new OLSMultipleLinearRegression();
267 double[] bid_prediction = new double[encodedValues.length];
268 boolean axet = true;
269 try {
270 regression.newSampleData(indexes, encodedTrainValues);
271 } catch (Exception e) {
272 for (int i = 0; i < encodedValues.length; i++) {
273 random = new Random();
274 bid_prediction[i] = random.nextDouble();
275 axet = false;
276 }
277 }
278
279 try {
280 for (int i = 0; i < encodedValues.length && axet; i++) {
281 bid_prediction[i] = predict(regression, encodedValues[i]);
282 }
283 } catch (Exception e) {
284 for (int i = 0; i < encodedValues.length; i++) {
285 random = new Random();
286 bid_prediction[i] = random.nextDouble();
287 }
288 }
289 List<Bid> lob = new ArrayList<>();
290 for (int i = 0; i < encodedValues.length; i++) {
291 Map<String, Value> msv = new HashMap<>();
292 for (String s : issues) {
293 for (int j = 0; j < allPossibleBids.get(i).size(); j++) {
294 if (types.get(j).equals("Discrete") && profileint.getProfile().getDomain().getValues(s)
295 .toString().contains(allPossibleBids.get(i).get(j)))
296 msv.put(s, new DiscreteValue(allPossibleBids.get(i).get(j)));
297 else if (types.get(j).equals("Number") && profileint.getProfile().getDomain().getValues(s)
298 .toString().contains(allPossibleBids.get(i).get(j)))
299 msv.put(s, new NumberValue(allPossibleBids.get(i).get(j)));
300 }
301 }
302 sortedPredictedBids.add(new Bid(msv));
303 lob.add(new Bid(msv));
304 }
305
306 sortedPredictedBids.sort(Comparator.comparing(s -> bid_prediction[lob.indexOf(s)]));
307
308 } catch (Exception e) {
309 System.out.println("Couldn't elicitate");
310 e.printStackTrace();
311 }
312 }
313
314 public static double[][] oneHotEncoder(List<List<String>> bidOrder, int countAll, List<List<Value>> allIssues) {
315 double[][] oneHotEncoded = new double[bidOrder.size()][countAll];
316 int count = 0;
317 for (int i = 0; i < oneHotEncoded.length; i++) {
318 for (int j = 0; j < oneHotEncoded[0].length; j++) {
319 for (int k = 0; k < bidOrder.get(i).size(); k++) {
320 for (int l = 0; l < allIssues.get(k).size(); l++) {
321 boolean cget = true;
322 if (bidOrder.get(i).get(k) == null)
323 oneHotEncoded[i][count] = 0.5;
324 else if (allIssues.get(k).get(l) instanceof DiscreteValue) {
325 if (bidOrder.get(i).get(k).equals(((DiscreteValue) allIssues.get(k).get(l)).getValue())) {
326 oneHotEncoded[i][count] = 1.0;
327 cget = false;
328 }
329 } else if (allIssues.get(i).get(k) instanceof NumberValue) {
330 if (bidOrder.get(i).get(k)
331 .equals(((NumberValue) allIssues.get(k).get(l)).getValue().toString())) {
332 oneHotEncoded[i][count] = 1.0;
333 cget = false;
334 }
335 }
336 if (cget) {
337 oneHotEncoded[i][count] = 0.0;
338 }
339 count++;
340 }
341 }
342 count = 0;
343 }
344 }
345 return oneHotEncoded;
346 }
347
348 public static double predict(OLSMultipleLinearRegression regression, double[] x) {
349 if (regression == null) {
350 throw new IllegalArgumentException("Null Object");
351 }
352 double[] params = regression.estimateRegressionParameters();
353 double prediction = params[0];
354 for (int i = 1; i < params.length; i++) {
355 prediction += params[i] * x[i - 1];
356 }
357 return prediction;
358 }
359
360}
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