source: anac2020/AzarAgent/src/main/java/geniusweb/exampleparties/simpleshaop/GravityEs.java@ 1

Last change on this file since 1 was 1, checked in by wouter, 4 years ago

#1910 added anac2020 parties
File size: 11.1 KB
Line 
1package geniusweb.exampleparties.simpleshaop;
2
3import java.util.ArrayList;
4import java.util.HashMap;
5import java.util.List;
6import java.util.Map;
7import java.util.Set;
8import geniusweb.issuevalue.Bid;
9import geniusweb.issuevalue.DiscreteValue;
10import geniusweb.issuevalue.Domain;
11import geniusweb.issuevalue.Value;
12
13
14
15public class GravityEs {
16
17 private static final double HALF_OF_TIME = 0.5;
18 Bid sampleBid;
19 private List<Bid> orderedBidListInAscendingOrder;
20 private List<String> listOfIssues;
21 private int numberOfIssues;
22
23 Map<Integer, double[][]> eachIssueAnItsIndexingValuesDoubleArrayMap;
24 Map<Integer, double[]> eachIssueAndItsValuesUtilityMap;
25
26 double[] sumOfSquaredErrorsOfIssueValues;
27 Map<Integer, Double> eachIssueAndItsUtilityMap;
28
29 // opponent modeling
30 Bid lastReceivedBid;
31 Map<Integer, double[]> eachIssueAndItsValuesFrequencyArrayMap;
32 Map<Integer, double[]> opponentEachIssueAndItsValuesUtilityMap;
33 double[] opponentSumOfSquaredErrosOfEachIssueMatrix;
34 Map<Integer, Double> opponentEachIssueAndItsUtilityMap;
35
36 double lastOfferingTime;
37 Bid lastOfferedBid;
38 Bid nextBid;
39
40 private List<Bid> bidOrder;
41 private Set<String> issList;
42 private Domain domain;
43
44 HashMap<String,List<Value>> issueValindex = new HashMap<String, List<Value>>();
45
46 public GravityEs(Domain domain, List<Bid> bidOrder) {
47
48 this.bidOrder = bidOrder;
49 this.domain = domain;
50 issList = domain.getIssues();
51
52
53 for ( String iss : issList ) {
54 List<Value> vaList = new ArrayList<Value>();
55 for ( Value val : domain.getValues(iss) ) {
56 vaList.add(val);
57 }
58 issueValindex.put(iss, vaList);
59 }
60
61 initAgentVariables();
62 initOpponentVars();
63
64
65
66
67
68 }
69
70 // uncertain preferences
71 private void initAgentVariables() {
72 initUncertainPrefVariables();
73 createCopelandMatrices();
74 fillAgentVars();
75 }
76
77 private void fillAgentVars() {
78 fillCopelandMatrices();
79 setValueUtilities();
80 fillMatriceOfNoChangeOfEachIssue();
81 setIssueUtilitiesWithNormalization();
82 }
83
84 // opponent modeling
85 private void initOpponentVars() {
86 initOpponentModelingVars();
87 createFrequencyArrays();
88 }
89
90 private void initUncertainPrefVariables() {
91
92 //this.outcomeSpace = new SortedOutcomeSpace(utilitySpace);
93
94 this.orderedBidListInAscendingOrder = bidOrder;
95 this.sampleBid = orderedBidListInAscendingOrder.get(0);
96 //this.listOfIssues = space.getDomain().getIssues();
97 this.numberOfIssues = issList.size();
98
99 this.eachIssueAnItsIndexingValuesDoubleArrayMap = new HashMap<Integer, double[][]>();
100 this.eachIssueAndItsValuesUtilityMap = new HashMap<Integer, double[]>();
101
102 this.sumOfSquaredErrorsOfIssueValues = new double[numberOfIssues];
103 this.eachIssueAndItsUtilityMap = new HashMap<Integer, Double>();
104 }
105
106 private void initOpponentModelingVars() {
107 this.eachIssueAndItsValuesFrequencyArrayMap = new HashMap<Integer, double[]>();
108 this.opponentEachIssueAndItsValuesUtilityMap = new HashMap<Integer, double[]>();
109 this.opponentSumOfSquaredErrosOfEachIssueMatrix = new double[numberOfIssues];
110 this.opponentEachIssueAndItsUtilityMap = new HashMap<Integer, Double>();
111 }
112
113 private void createCopelandMatrices() {
114
115 int i = 0;
116 for ( String iss : issList ) {
117 int valueSize = domain.getValues(iss).size().intValue() ;
118 eachIssueAnItsIndexingValuesDoubleArrayMap.put(i, new double[valueSize][valueSize]);
119 i++;
120 }
121
122 }
123
124 private void createFrequencyArrays() {
125
126 int i = 0;
127 for ( String issue : issList ) {
128 int valueSize = domain.getValues(issue).size().intValue();
129 eachIssueAndItsValuesFrequencyArrayMap.put(i, new double[valueSize]);
130 i++;
131 }
132
133
134 }
135
136 /*
137 * Gets the bigger bid from the sorted list . Gets the smaller bids from the
138 * sorted list. Does pairwise Copeland comparison with one bigger bid and
139 * smaller bids. depending on the bigger bid > all smaller bids
140 */
141 private void fillCopelandMatrices() {
142 for (int i = orderedBidListInAscendingOrder.size() - 1; i > 0; i--) {
143 Bid biggerBid = orderedBidListInAscendingOrder.get(i);
144 for (int j = i - 1; j >= 0; j--) {
145 Bid smallerBid = orderedBidListInAscendingOrder.get(j);
146 fillCopelandMatriceWithBiggerAndSmaller(biggerBid, smallerBid, i, j);
147 }
148 //System.out.println(biggerBid);
149 }
150 //printIntDoubleDoubleArrMap(eachIssueAnItsIndexingValuesDoubleArrayMap);
151 }
152
153 /*
154 * pairwise Copeland comparison in each issue value, update the frequency
155 * matrices for different issue values depending on the similarity.
156 */
157 private void fillCopelandMatriceWithBiggerAndSmaller(Bid biggerBid, Bid smallerBid, int biggerIndex,
158 int smallerIndex) {
159 int i = 0;
160 for (String issue : domain.getIssues()) {
161 DiscreteValue biggerBidValue = (DiscreteValue) ( biggerBid.getValue(issue) );
162 int biggerBidValueIndex = getIndexOfValueInIssue(biggerBid, issue, biggerBidValue);
163 DiscreteValue smallerValue = (DiscreteValue) (smallerBid.getValue(issue) );
164 int smallerBidValueIndex = getIndexOfValueInIssue(smallerBid, issue, smallerValue);
165 int numberOfSimilarities = countEqualValues(smallerBid, biggerBid);
166 if (numberOfSimilarities > 0) {
167 eachIssueAnItsIndexingValuesDoubleArrayMap.get(i)[biggerBidValueIndex][smallerBidValueIndex] += (1d
168 / (biggerIndex - smallerIndex)) * numberOfSimilarities;
169 }
170 i++;
171 }
172 }
173
174 private int getIndexOfValueInIssue(Bid bid, String issue, Value value) {
175
176 List<Value> valList = issueValindex.get(issue);
177
178 for ( int i = 0; i < valList.size(); i++ )
179 if ( valList.get(i).equals(value) )
180 return i;
181
182 return -1;
183
184 }
185
186 private void setValueUtilities() {
187
188 int i = 0;
189 for ( String issueDiscrete : issList ) {
190
191 int valueSize = domain.getValues(issueDiscrete).size().intValue();
192 double[] valuesBeingBigInfoArray = new double[valueSize];
193 double[][] matrix = eachIssueAnItsIndexingValuesDoubleArrayMap.get(i);
194 for (int j = 0; j < valueSize; j++) {
195 double sumOfRowInMatrix = getSumOfRowInMatrix(matrix, j);
196 double sumOfColInMatrix = getSumOfColInMatrix(matrix, j);
197 double total = sumOfColInMatrix + sumOfRowInMatrix;
198 if (total == 0) {
199 valuesBeingBigInfoArray[j] = 0;
200 } else {
201 double beingBigPercentage = (sumOfRowInMatrix) / total;
202 valuesBeingBigInfoArray[j] = beingBigPercentage;
203 }
204 }
205 normalize(i, valueSize, valuesBeingBigInfoArray);
206 i++;
207 }
208
209 //System.out.println("------------AGENT------------");
210 //printIntDoubleArrMap(eachIssueAndItsValuesUtilityMap);
211
212 }
213
214 private void normalize(int i, int valueSize, double[] valuesBeingBigInfoArray) {
215 double[] utilityArr = new double[valueSize];
216 double totalSum = getSumOfRowInOneDimensionalMatrix(valuesBeingBigInfoArray);
217 for (int j = 0; j < valueSize; j++) {
218 if (totalSum == 0) {
219 utilityArr[j] = 0;
220 } else {
221 utilityArr[j] = valuesBeingBigInfoArray[j] / totalSum;
222 }
223 }
224 eachIssueAndItsValuesUtilityMap.put(i, utilityArr);
225 }
226
227 private void fillMatriceOfNoChangeOfEachIssue() {
228 for (int i = 0; i < numberOfIssues; i++) {
229 double[][] matrix = eachIssueAnItsIndexingValuesDoubleArrayMap.get(i);
230 double sumOfMatrix = 0;
231 for (int j = 0; j < matrix.length; j++) {
232 sumOfMatrix += getSumOfRowInMatrix(matrix, j);
233 }
234 //System.out.println("Sum of matrix: " + sumOfMatrix);
235 double average = sumOfMatrix / (matrix.length * matrix.length);
236 //System.out.println("average of matrix: " + average);
237 double sumOfSquaredErrors = 0;
238 for (int j = 0; j < matrix.length; j++) {
239 for (int k = 0; k < matrix.length; k++) {
240 sumOfSquaredErrors += Math.pow(matrix[j][k] - average, 2);
241 }
242 }
243 sumOfSquaredErrorsOfIssueValues[i] = Math.sqrt(sumOfSquaredErrors / (matrix.length * matrix.length));
244 }
245 }
246
247 private int getSumOfRowInMatrix(double[][] matrix, int row) {
248 int rowSum = 0;
249 for (int col = 0; col < matrix[row].length; col++) {
250 rowSum += matrix[row][col];
251 }
252 return rowSum;
253 }
254
255 private int getSumOfColInMatrix(double[][] matrix, int col) {
256 int colSum = 0;
257 for (int row = 0; row < matrix.length; row++) {
258 colSum += matrix[row][col];
259 }
260 return colSum;
261 }
262
263 private void setIssueUtilitiesWithNormalization() {
264 double totalOfsumOfSquares = 0;
265 for (int i = 0; i < numberOfIssues; i++) {
266 totalOfsumOfSquares += sumOfSquaredErrorsOfIssueValues[i];
267 }
268 for (int i = 0; i < numberOfIssues; i++) {
269 eachIssueAndItsUtilityMap.put(i, sumOfSquaredErrorsOfIssueValues[i] / totalOfsumOfSquares);
270 }
271
272 //System.out.println("----------------------AGENT------------------");
273 //printIntDoubleMap(eachIssueAndItsUtilityMap);
274 }
275
276
277
278 /*
279 private void printIntDoubleDoubleArrMap(Map<Integer, double[][]> eachIssueAnItsValuesDoubleArrayMap) {
280 System.out.println("EACH ISSUE AND ITS VALUES");
281 for (Entry<Integer, double[][]> entry : eachIssueAnItsValuesDoubleArrayMap.entrySet()) {
282 System.out.println(entry.getKey() + " ");
283 double[][] values = entry.getValue();
284 for (int j = 0; j < values.length; j++) {
285 for (int k = 0; k < values.length; k++) {
286 System.out.print(values[j][k] + " ");
287 }
288 System.out.println();
289 }
290 System.out.println();
291 }
292 }
293
294 private void printIntDoubleArrMap(Map<Integer, double[]> map) {
295 System.out.println("EACH ISSUE AND ITS VALUES UTILITIES");
296 for (Entry<Integer, double[]> entry : map.entrySet()) {
297 System.out.print(entry.getKey() + " ");
298 double[] values = entry.getValue();
299 for (int j = 0; j < values.length; j++) {
300 System.out.print(values[j] + " ");
301 }
302 System.out.println();
303 }
304 System.out.println();
305 }
306
307 private void printIntDoubleMap(Map<Integer, Double> map) {
308 System.out.println("----------------------EACH ISSUE AND ITS UTILITIES------------------");
309 for (Entry<Integer, Double> entry : map.entrySet()) {
310 System.out.println(entry.getKey() + " " + entry.getValue());
311 }
312 System.out.println();
313 }
314 */
315
316
317
318
319 private double getSumOfRowInOneDimensionalMatrix(double[] matrix) {
320 double rowSum = 0;
321 for (int i = 0; i < matrix.length; i++) {
322 rowSum += matrix[i];
323 }
324 return rowSum;
325 }
326
327
328
329 /**
330 * Counts the number of equal values with another bid (assuming they are defined on the same domain)
331 */
332 public int countEqualValues(Bid b,Bid bOwn)
333 {
334 int count = 0;
335 for ( String iss : bOwn.getIssueValues().keySet() )
336 if ( bOwn.getValue(iss).equals(b.getValue(iss)) )
337 count++;
338 return count;
339 }
340
341
342
343
344
345 public double getUtilityForBid(Bid bid) {
346
347 double totalUtility = 0;
348 if (bid != null) {
349 int i = 0;
350 for (String issue : issList) {
351 Double utilityOfIssue = eachIssueAndItsUtilityMap.get(i);
352 DiscreteValue biggerBidValue = (DiscreteValue) (bid.getValue(issue));
353 int indexOfValue = getIndexOfValueInIssue(bid, issue, biggerBidValue );
354 double[] valueUtilities = eachIssueAndItsValuesUtilityMap.get(i);
355 double utilityOfValue = valueUtilities[indexOfValue];
356
357 totalUtility += utilityOfIssue * utilityOfValue;
358 i++;
359 }
360 return totalUtility;
361 }
362 return totalUtility;
363
364 }
365
366
367
368 public GravityEs updateGravityModel( List<Bid> bidOrder2 ) {
369 return new GravityEs(this.domain, bidOrder2);
370 }
371
372
373}
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