[1] | 1 | package agents.anac.y2011.IAMhaggler2011;
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| 2 |
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| 3 | import java.util.ArrayList;
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| 4 |
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| 5 | import agents.Jama.Matrix;
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| 6 | import agents.org.apache.commons.math.MathException;
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| 7 | import agents.org.apache.commons.math.MaxIterationsExceededException;
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| 8 | import agents.org.apache.commons.math.special.Erf;
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| 9 | import agents.uk.ac.soton.ecs.gp4j.bmc.BasicPrior;
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| 10 | import agents.uk.ac.soton.ecs.gp4j.bmc.GaussianProcessMixture;
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| 11 | import agents.uk.ac.soton.ecs.gp4j.bmc.GaussianProcessMixturePrediction;
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| 12 | import agents.uk.ac.soton.ecs.gp4j.bmc.GaussianProcessRegressionBMC;
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| 13 | import agents.uk.ac.soton.ecs.gp4j.gp.covariancefunctions.CovarianceFunction;
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| 14 | import agents.uk.ac.soton.ecs.gp4j.gp.covariancefunctions.Matern3CovarianceFunction;
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| 15 | import agents.uk.ac.soton.ecs.gp4j.gp.covariancefunctions.NoiseCovarianceFunction;
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| 16 | import agents.uk.ac.soton.ecs.gp4j.gp.covariancefunctions.SumCovarianceFunction;
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| 17 | import genius.core.Agent;
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| 18 | import genius.core.Bid;
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| 19 | import genius.core.SupportedNegotiationSetting;
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| 20 | import genius.core.actions.Accept;
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| 21 | import genius.core.actions.Action;
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| 22 | import genius.core.actions.EndNegotiation;
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| 23 | import genius.core.actions.Offer;
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| 24 | import genius.core.utility.AdditiveUtilitySpace;
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| 25 |
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| 26 | /**
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| 27 | * @author Colin Williams
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| 28 | *
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| 29 | * The IAMhaggler Agent, created for ANAC 2011. Designed by C. R.
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| 30 | * Williams, V. Robu, E. H. Gerding and N. R. Jennings.
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| 31 | *
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| 32 | */
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| 33 | public class IAMhaggler2011 extends Agent {
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| 34 |
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| 35 | protected double RISK_PARAMETER = 3.0;
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| 36 | private Matrix utilitySamples;
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| 37 | private Matrix timeSamples;
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| 38 | private Matrix utility;
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| 39 | private GaussianProcessRegressionBMC regression;
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| 40 | private double lastRegressionTime = 0;
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| 41 | private double lastRegressionUtility = 1;
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| 42 | private ArrayList<Double> opponentTimes = new ArrayList<Double>();
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| 43 | private ArrayList<Double> opponentUtilities = new ArrayList<Double>();
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| 44 | private double maxUtilityInTimeSlot;
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| 45 | private int lastTimeSlot = -1;
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| 46 | private Matrix means;
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| 47 | private Matrix variances;
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| 48 | private double maxUtility;
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| 49 | private Bid bestReceivedBid;
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| 50 | private double previousTargetUtility;
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| 51 | protected BidCreator bidCreator;
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| 52 |
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| 53 | private static enum ActionType {
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| 54 | ACCEPT, BREAKOFF, OFFER, START;
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| 55 | }
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| 56 |
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| 57 | protected double MAXIMUM_ASPIRATION = 0.9;
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| 58 | private Action messageOpponent;
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| 59 | protected Action myLastAction = null;
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| 60 | protected Bid myLastBid = null;
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| 61 | protected double acceptMultiplier = 1.02;
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| 62 | private ArrayList<Bid> opponentBids;
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| 63 |
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| 64 | public IAMhaggler2011() {
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| 65 | }
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| 66 |
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| 67 | /*
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| 68 | * (non-Javadoc)
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| 69 | *
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| 70 | * @see agents.southampton.SouthamptonAgent#init()
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| 71 | */
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| 72 | @Override
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| 73 | public void init() {
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| 74 | myLastBid = null;
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| 75 | myLastAction = null;
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| 76 |
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| 77 | opponentBids = new ArrayList<Bid>();
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| 78 |
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| 79 | double discountingFactor = 0.5;
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| 80 | try {
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| 81 | discountingFactor = utilitySpace.getDiscountFactor();
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| 82 | } catch (Exception ex) {
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| 83 | ex.printStackTrace();
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| 84 | }
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| 85 | if (discountingFactor == 0)
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| 86 | discountingFactor = 1;
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| 87 | makeUtilitySamples(100);
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| 88 | makeTimeSamples(100);
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| 89 | Matrix discounting = generateDiscountingFunction(discountingFactor);
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| 90 | Matrix risk = generateRiskFunction(RISK_PARAMETER);
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| 91 | utility = risk.arrayTimes(discounting);
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| 92 |
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| 93 | BasicPrior[] bps = { new BasicPrior(11, 0.252, 0.5),
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| 94 | new BasicPrior(11, 0.166, 0.5), new BasicPrior(1, .01, 1.0) };
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| 95 | CovarianceFunction cf = new SumCovarianceFunction(
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| 96 | Matern3CovarianceFunction.getInstance(),
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| 97 | NoiseCovarianceFunction.getInstance());
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| 98 |
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| 99 | regression = new GaussianProcessRegressionBMC();
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| 100 | regression.setCovarianceFunction(cf);
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| 101 | regression.setPriors(bps);
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| 102 |
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| 103 | maxUtility = 0;
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| 104 | previousTargetUtility = 1;
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| 105 |
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| 106 | bidCreator = new RandomBidCreator();
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| 107 | }
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| 108 |
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| 109 | @Override
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| 110 | public String getName() {
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| 111 | return "IAMhaggler2011";
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| 112 | }
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| 113 |
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| 114 | /**
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| 115 | * Create an m-by-1 matrix of utility samples.
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| 116 | *
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| 117 | * @param m
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| 118 | * The sample size.
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| 119 | */
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| 120 | private void makeUtilitySamples(int m) {
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| 121 | double[] utilitySamplesArray = new double[m];
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| 122 | {
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| 123 | for (int i = 0; i < utilitySamplesArray.length; i++) {
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| 124 | utilitySamplesArray[i] = 1.0 - (i + 0.5) / (m + 1.0);
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| 125 | }
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| 126 | }
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| 127 | utilitySamples = new Matrix(utilitySamplesArray,
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| 128 | utilitySamplesArray.length);
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| 129 | }
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| 130 |
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| 131 | /**
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| 132 | * Create a 1-by-n matrix of time samples.
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| 133 | *
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| 134 | * @param n
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| 135 | * The sample size.
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| 136 | */
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| 137 | private void makeTimeSamples(int n) {
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| 138 | double[] timeSamplesArray = new double[n + 1];
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| 139 | {
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| 140 | for (int i = 0; i < timeSamplesArray.length; i++) {
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| 141 | timeSamplesArray[i] = ((double) i) / ((double) n);
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| 142 | }
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| 143 | }
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| 144 | timeSamples = new Matrix(timeSamplesArray, 1);
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| 145 | }
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| 146 |
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| 147 | /*
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| 148 | * (non-Javadoc)
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| 149 | *
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| 150 | * @see agents.southampton.SouthamptonAgent#proposeInitialBid()
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| 151 | */
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| 152 | protected Bid proposeInitialBid() throws Exception {
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| 153 | return utilitySpace.getMaxUtilityBid();
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| 154 | }
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| 155 |
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| 156 | /*
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| 157 | * (non-Javadoc)
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| 158 | *
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| 159 | * @see agents.southampton.SouthamptonAgent#proposeNextBid(negotiator.Bid)
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| 160 | */
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| 161 | protected Bid proposeNextBid(Bid opponentBid) throws Exception {
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| 162 | double opponentUtility = utilitySpace.getUtility(opponentBid);
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| 163 |
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| 164 | if (opponentUtility > maxUtility) {
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| 165 | bestReceivedBid = opponentBid;
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| 166 | maxUtility = opponentUtility;
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| 167 | }
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| 168 |
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| 169 | double targetUtility = getTarget(opponentUtility, timeline.getTime());
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| 170 |
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| 171 | if (targetUtility <= maxUtility && previousTargetUtility > maxUtility)
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| 172 | return bestReceivedBid;
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| 173 | previousTargetUtility = targetUtility;
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| 174 | // Now get a random bid in the range targetUtility � 0.025
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| 175 | return bidCreator.getBid((AdditiveUtilitySpace) utilitySpace,
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| 176 | targetUtility - 0.025, targetUtility + 0.025);
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| 177 | }
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| 178 |
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| 179 | /**
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| 180 | * Get the target at a given time, recording the opponent's utility.
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| 181 | *
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| 182 | * @param opponentUtility
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| 183 | * The utility of the most recent offer made by the opponent.
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| 184 | * @param time
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| 185 | * The current time.
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| 186 | * @return the target.
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| 187 | */
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| 188 | protected double getTarget(double opponentUtility, double time) {
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| 189 | // Calculate the current time slot
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| 190 | int timeSlot = (int) Math.floor(time * 36);
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| 191 |
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| 192 | boolean regressionUpdateRequired = false;
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| 193 | if (lastTimeSlot == -1) {
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| 194 | regressionUpdateRequired = true;
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| 195 | }
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| 196 |
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| 197 | // If the time slot has changed
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| 198 | if (timeSlot != lastTimeSlot) {
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| 199 | if (lastTimeSlot != -1) {
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| 200 | // Store the data from the time slot
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| 201 | opponentTimes.add((lastTimeSlot + 0.5) / 36.0);
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| 202 | opponentUtilities.add(maxUtilityInTimeSlot);
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| 203 | // Flag regression receiveMessage required
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| 204 | regressionUpdateRequired = true;
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| 205 | }
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| 206 | // Update the time slot
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| 207 | lastTimeSlot = timeSlot;
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| 208 | // Reset the max utility
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| 209 | maxUtilityInTimeSlot = 0;
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| 210 | }
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| 211 |
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| 212 | // Calculate the maximum utility observed in the current time slot
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| 213 | maxUtilityInTimeSlot = Math.max(maxUtilityInTimeSlot, opponentUtility);
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| 214 |
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| 215 | if (timeSlot == 0) {
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| 216 | return 1.0 - time / 2.0;
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| 217 | }
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| 218 |
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| 219 | if (regressionUpdateRequired) {
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| 220 | double[] x = new double[opponentTimes.size()];
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| 221 | double[] xAdjust = new double[opponentTimes.size()];
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| 222 | double[] y = new double[opponentUtilities.size()];
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| 223 | double[] timeSamplesAdjust = new double[timeSamples
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| 224 | .getColumnDimension()];
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| 225 |
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| 226 | int i;
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| 227 | i = 0;
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| 228 | for (double d : opponentTimes) {
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| 229 | x[i++] = d;
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| 230 | }
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| 231 | i = 0;
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| 232 | double intercept = opponentUtilities.get(0);
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| 233 | double gradient = 0.9 - intercept;
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| 234 | for (double d : opponentTimes) {
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| 235 | xAdjust[i++] = intercept + (gradient * d);
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| 236 | }
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| 237 | i = 0;
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| 238 | for (double d : timeSamples.getRowPackedCopy()) {
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| 239 | timeSamplesAdjust[i++] = intercept + (gradient * d);
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| 240 | }
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| 241 | i = 0;
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| 242 | for (double d : opponentUtilities) {
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| 243 | y[i++] = d;
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| 244 | }
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| 245 |
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| 246 | Matrix matrixX = new Matrix(x, x.length);
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| 247 | Matrix matrixXAdjust = new Matrix(xAdjust, xAdjust.length);
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| 248 | Matrix matrixY = new Matrix(y, y.length);
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| 249 | Matrix matrixTimeSamplesAdjust = new Matrix(timeSamplesAdjust,
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| 250 | timeSamplesAdjust.length);
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| 251 |
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| 252 | matrixY.minusEquals(matrixXAdjust);
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| 253 |
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| 254 | GaussianProcessMixture predictor = regression
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| 255 | .calculateRegression(matrixX, matrixY);
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| 256 |
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| 257 | GaussianProcessMixturePrediction prediction = predictor
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| 258 | .calculatePrediction(timeSamples.transpose());
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| 259 |
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| 260 | // Store the means and variances
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| 261 | means = prediction.getMean().plus(matrixTimeSamplesAdjust);
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| 262 | variances = prediction.getVariance();
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| 263 | }
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| 264 |
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| 265 | Pair<Matrix, Matrix> acceptMatrices = generateProbabilityAccept(means,
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| 266 | variances, time);
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| 267 | Matrix probabilityAccept = acceptMatrices.fst;
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| 268 | Matrix cumulativeAccept = acceptMatrices.snd;
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| 269 |
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| 270 | Matrix probabilityExpectedUtility = probabilityAccept
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| 271 | .arrayTimes(utility);
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| 272 | Matrix cumulativeExpectedUtility = cumulativeAccept.arrayTimes(utility);
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| 273 |
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| 274 | Pair<Double, Double> bestAgreement = getExpectedBestAgreement(
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| 275 | probabilityExpectedUtility, cumulativeExpectedUtility, time);
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| 276 | double bestTime = bestAgreement.fst;
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| 277 | double bestUtility = bestAgreement.snd;
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| 278 |
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| 279 | double targetUtility = lastRegressionUtility
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| 280 | + ((time - lastRegressionTime)
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| 281 | * (bestUtility - lastRegressionUtility)
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| 282 | / (bestTime - lastRegressionTime));
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| 283 |
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| 284 | // Store the target utility and time
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| 285 | lastRegressionUtility = targetUtility;
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| 286 | lastRegressionTime = time;
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| 287 |
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| 288 | return targetUtility;
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| 289 | }
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| 290 |
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| 291 | /**
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| 292 | * Generate an n-by-m matrix representing the effect of the discounting
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| 293 | * factor for a given utility-time combination. The combinations are given
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| 294 | * by the time and utility samples stored in timeSamples and utilitySamples
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| 295 | * respectively.
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| 296 | *
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| 297 | * @param discountingFactor
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| 298 | * The discounting factor, in the range (0, 1].
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| 299 | * @return An n-by-m matrix representing the discounted utilities.
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| 300 | */
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| 301 | private Matrix generateDiscountingFunction(double discountingFactor) {
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| 302 | double[] discountingSamples = timeSamples.getRowPackedCopy();
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| 303 | double[][] m = new double[utilitySamples.getRowDimension()][timeSamples
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| 304 | .getColumnDimension()];
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| 305 | for (int i = 0; i < m.length; i++) {
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| 306 | for (int j = 0; j < m[i].length; j++) {
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| 307 | m[i][j] = Math.pow(discountingFactor, discountingSamples[j]);
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| 308 | }
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| 309 | }
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| 310 | return new Matrix(m);
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| 311 | }
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| 312 |
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| 313 | /**
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| 314 | * Generate an (n-1)-by-m matrix representing the probability of acceptance
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| 315 | * for a given utility-time combination. The combinations are given by the
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| 316 | * time and utility samples stored in timeSamples and utilitySamples
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| 317 | * respectively.
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| 318 | *
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| 319 | * @param mean
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| 320 | * The means, at each of the sample time points.
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| 321 | * @param variance
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| 322 | * The variances, at each of the sample time points.
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| 323 | * @param time
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| 324 | * The current time, in the range [0, 1].
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| 325 | * @return An (n-1)-by-m matrix representing the probability of acceptance.
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| 326 | */
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| 327 | private Pair<Matrix, Matrix> generateProbabilityAccept(Matrix mean,
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| 328 | Matrix variance, double time) {
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| 329 | int i = 0;
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| 330 | for (; i < timeSamples.getColumnDimension(); i++) {
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| 331 | if (timeSamples.get(0, i) > time)
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| 332 | break;
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| 333 | }
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| 334 | Matrix cumulativeAccept = new Matrix(utilitySamples.getRowDimension(),
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| 335 | timeSamples.getColumnDimension(), 0);
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| 336 | Matrix probabilityAccept = new Matrix(utilitySamples.getRowDimension(),
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| 337 | timeSamples.getColumnDimension(), 0);
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| 338 |
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| 339 | double interval = 1.0 / utilitySamples.getRowDimension();
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| 340 |
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| 341 | for (; i < timeSamples.getColumnDimension(); i++) {
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| 342 | double s = Math.sqrt(2 * variance.get(i, 0));
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| 343 | double m = mean.get(i, 0);
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| 344 |
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| 345 | double minp = (1.0 - (0.5 * (1 + erf(
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| 346 | (utilitySamples.get(0, 0) + (interval / 2.0) - m) / s))));
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| 347 | double maxp = (1.0 - (0.5 * (1 + erf(
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| 348 | (utilitySamples.get(utilitySamples.getRowDimension() - 1, 0)
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| 349 | - (interval / 2.0) - m) / s))));
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| 350 |
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| 351 | for (int j = 0; j < utilitySamples.getRowDimension(); j++) {
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| 352 | double utility = utilitySamples.get(j, 0);
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| 353 | double p = (1.0 - (0.5 * (1 + erf((utility - m) / s))));
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| 354 | double p1 = (1.0 - (0.5
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| 355 | * (1 + erf((utility - (interval / 2.0) - m) / s))));
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| 356 | double p2 = (1.0 - (0.5
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| 357 | * (1 + erf((utility + (interval / 2.0) - m) / s))));
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| 358 |
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| 359 | cumulativeAccept.set(j, i, (p - minp) / (maxp - minp));
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| 360 | probabilityAccept.set(j, i, (p1 - p2) / (maxp - minp));
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| 361 | }
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| 362 | }
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| 363 | return new Pair<Matrix, Matrix>(probabilityAccept, cumulativeAccept);
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| 364 | }
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| 365 |
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| 366 | /**
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| 367 | * Wrapper for the erf function.
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| 368 | *
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| 369 | * @param x
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| 370 | * @return
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| 371 | */
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| 372 | private double erf(double x) {
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| 373 | if (x > 6)
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| 374 | return 1;
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| 375 | if (x < -6)
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| 376 | return -1;
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| 377 | try {
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| 378 | double d = Erf.erf(x);
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| 379 | if (d > 1)
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| 380 | return 1;
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| 381 | if (d < -1)
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| 382 | return -1;
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| 383 | return d;
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| 384 | } catch (MaxIterationsExceededException e) {
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| 385 | if (x > 0)
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| 386 | return 1;
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| 387 | else
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| 388 | return -1;
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| 389 | } catch (MathException e) {
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| 390 | e.printStackTrace();
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| 391 | return 0;
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| 392 | }
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| 393 | }
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| 394 |
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| 395 | /**
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| 396 | * Generate an n-by-m matrix representing the risk based utility for a given
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| 397 | * utility-time combination. The combinations are given by the time and
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| 398 | * utility samples stored in timeSamples and utilitySamples
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| 399 | *
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| 400 | * @param riskParameter
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| 401 | * The risk parameter.
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| 402 | * @return an n-by-m matrix representing the risk based utility.
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| 403 | */
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| 404 | protected Matrix generateRiskFunction(double riskParameter) {
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| 405 | double mmin = generateRiskFunction(riskParameter, 0.0);
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| 406 | double mmax = generateRiskFunction(riskParameter, 1.0);
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| 407 | double range = mmax - mmin;
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| 408 |
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| 409 | double[] riskSamples = utilitySamples.getColumnPackedCopy();
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| 410 | double[][] m = new double[utilitySamples.getRowDimension()][timeSamples
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| 411 | .getColumnDimension()];
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| 412 | for (int i = 0; i < m.length; i++) {
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| 413 | double val;
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| 414 | if (range == 0) {
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| 415 | val = riskSamples[i];
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| 416 | } else {
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| 417 | val = (generateRiskFunction(riskParameter, riskSamples[i])
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| 418 | - mmin) / range;
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| 419 | }
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| 420 | for (int j = 0; j < m[i].length; j++) {
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| 421 | m[i][j] = val;
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| 422 | }
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| 423 | }
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| 424 | return new Matrix(m);
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| 425 | }
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| 426 |
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| 427 | /**
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| 428 | * Generate the risk based utility for a given actual utility.
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| 429 | *
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| 430 | * @param riskParameter
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| 431 | * The risk parameter.
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| 432 | * @param utility
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| 433 | * The actual utility to calculate the risk based utility from.
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| 434 | * @return the risk based utility.
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| 435 | */
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| 436 | protected double generateRiskFunction(double riskParameter,
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| 437 | double utility) {
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| 438 | return Math.pow(utility, riskParameter);
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| 439 | }
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| 440 |
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| 441 | /**
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| 442 | * Get a pair representing the time and utility value of the expected best
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| 443 | * agreement.
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| 444 | *
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| 445 | * @param expectedValues
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| 446 | * A matrix of expected utility values at the sampled time and
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| 447 | * utilities given by timeSamples and utilitySamples
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| 448 | * respectively.
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| 449 | * @param time
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| 450 | * The current time.
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| 451 | * @return a pair representing the time and utility value of the expected
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| 452 | * best agreement.
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| 453 | */
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| 454 | private Pair<Double, Double> getExpectedBestAgreement(
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| 455 | Matrix probabilityExpectedValues, Matrix cumulativeExpectedValues,
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| 456 | double time) {
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| 457 | Matrix probabilityFutureExpectedValues = getFutureExpectedValues(
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| 458 | probabilityExpectedValues, time);
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| 459 | Matrix cumulativeFutureExpectedValues = getFutureExpectedValues(
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| 460 | cumulativeExpectedValues, time);
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| 461 |
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| 462 | double[][] probabilityFutureExpectedValuesArray = probabilityFutureExpectedValues
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| 463 | .getArray();
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| 464 | double[][] cumulativeFutureExpectedValuesArray = cumulativeFutureExpectedValues
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| 465 | .getArray();
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| 466 |
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| 467 | Double bestX = null;
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| 468 | Double bestY = null;
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| 469 |
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| 470 | double[] colSums = new double[probabilityFutureExpectedValuesArray[0].length];
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| 471 | double bestColSum = 0;
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| 472 | int bestCol = 0;
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| 473 |
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| 474 | for (int x = 0; x < probabilityFutureExpectedValuesArray[0].length; x++) {
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| 475 | colSums[x] = 0;
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| 476 | for (int y = 0; y < probabilityFutureExpectedValuesArray.length; y++) {
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| 477 | colSums[x] += probabilityFutureExpectedValuesArray[y][x];
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| 478 | }
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| 479 |
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| 480 | if (colSums[x] >= bestColSum) {
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| 481 | bestColSum = colSums[x];
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| 482 | bestCol = x;
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| 483 | }
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| 484 | }
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| 485 |
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| 486 | int bestRow = 0;
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| 487 | double bestRowValue = 0;
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| 488 |
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| 489 | for (int y = 0; y < cumulativeFutureExpectedValuesArray.length; y++) {
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| 490 | double expectedValue = cumulativeFutureExpectedValuesArray[y][bestCol];
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| 491 | if (expectedValue > bestRowValue) {
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| 492 | bestRowValue = expectedValue;
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| 493 | bestRow = y;
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| 494 | }
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| 495 | }
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| 496 |
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| 497 | bestX = timeSamples.get(0,
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| 498 | bestCol + probabilityExpectedValues.getColumnDimension()
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| 499 | - probabilityFutureExpectedValues.getColumnDimension());
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| 500 | bestY = utilitySamples.get(bestRow, 0);
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| 501 |
|
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| 502 | return new Pair<Double, Double>(bestX, bestY);
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| 503 | }
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| 504 |
|
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| 505 | /**
|
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| 506 | * Get a matrix of expected utility values at the sampled time and utilities
|
---|
| 507 | * given by timeSamples and utilitySamples, for times in the future.
|
---|
| 508 | *
|
---|
| 509 | * @param expectedValues
|
---|
| 510 | * A matrix of expected utility values at the sampled time and
|
---|
| 511 | * utilities given by timeSamples and utilitySamples
|
---|
| 512 | * respectively.
|
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| 513 | * @param time
|
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| 514 | * The current time.
|
---|
| 515 | * @return a matrix of expected utility values for future time.
|
---|
| 516 | */
|
---|
| 517 | private Matrix getFutureExpectedValues(Matrix expectedValues, double time) {
|
---|
| 518 | int i = 0;
|
---|
| 519 | for (; i < timeSamples.getColumnDimension(); i++) {
|
---|
| 520 | if (timeSamples.get(0, i) > time)
|
---|
| 521 | break;
|
---|
| 522 | }
|
---|
| 523 | return expectedValues.getMatrix(0, expectedValues.getRowDimension() - 1,
|
---|
| 524 | i, expectedValues.getColumnDimension() - 1);
|
---|
| 525 | }
|
---|
| 526 |
|
---|
| 527 | /*
|
---|
| 528 | * (non-Javadoc)
|
---|
| 529 | *
|
---|
| 530 | * @see negotiator.Agent#ReceiveMessage(negotiator.actions.Action)
|
---|
| 531 | */
|
---|
| 532 | @Override
|
---|
| 533 | public final void ReceiveMessage(Action opponentAction) {
|
---|
| 534 | // Store the received opponentAction
|
---|
| 535 | messageOpponent = opponentAction;
|
---|
| 536 | }
|
---|
| 537 |
|
---|
| 538 | /**
|
---|
| 539 | * Handle an opponent's offer.
|
---|
| 540 | *
|
---|
| 541 | * @param opponentBid
|
---|
| 542 | * The bid made by the opponent.
|
---|
| 543 | * @return the action that we should take in response to the opponent's
|
---|
| 544 | * offer.
|
---|
| 545 | * @throws Exception
|
---|
| 546 | */
|
---|
| 547 | private Action handleOffer(Bid opponentBid) throws Exception {
|
---|
| 548 | Action chosenAction = null;
|
---|
| 549 |
|
---|
| 550 | if (myLastAction == null) {
|
---|
| 551 | // Special case to handle first action
|
---|
| 552 | Bid b = proposeInitialBid();
|
---|
| 553 | myLastBid = b;
|
---|
| 554 | chosenAction = new Offer(this.getAgentID(), b);
|
---|
| 555 | } else if (utilitySpace.getUtility(opponentBid)
|
---|
| 556 | * acceptMultiplier >= utilitySpace.getUtility(myLastBid)) {
|
---|
| 557 | // Accept opponent's bid based on my previous bid.
|
---|
| 558 | chosenAction = new Accept(this.getAgentID(), opponentBid);
|
---|
| 559 | opponentBids.add(opponentBid);
|
---|
| 560 | } else if (utilitySpace.getUtility(opponentBid)
|
---|
| 561 | * acceptMultiplier >= MAXIMUM_ASPIRATION) {
|
---|
| 562 | // Accept opponent's bid based on my previous bid.
|
---|
| 563 | chosenAction = new Accept(this.getAgentID(), opponentBid);
|
---|
| 564 | opponentBids.add(opponentBid);
|
---|
| 565 | } else {
|
---|
| 566 | Bid plannedBid = proposeNextBid(opponentBid);
|
---|
| 567 | chosenAction = new Offer(this.getAgentID(), plannedBid);
|
---|
| 568 |
|
---|
| 569 | if (utilitySpace.getUtility(opponentBid)
|
---|
| 570 | * acceptMultiplier >= utilitySpace.getUtility(plannedBid)) {
|
---|
| 571 | // Accept opponent's bid based on my planned bid.
|
---|
| 572 | chosenAction = new Accept(this.getAgentID(), opponentBid);
|
---|
| 573 | }
|
---|
| 574 | opponentBids.add(opponentBid);
|
---|
| 575 | }
|
---|
| 576 |
|
---|
| 577 | return chosenAction;
|
---|
| 578 | }
|
---|
| 579 |
|
---|
| 580 | /**
|
---|
| 581 | * Gets the version number.
|
---|
| 582 | *
|
---|
| 583 | * @return the version number.
|
---|
| 584 | */
|
---|
| 585 | @Override
|
---|
| 586 | public String getVersion() {
|
---|
| 587 | return "2.0";
|
---|
| 588 | }
|
---|
| 589 |
|
---|
| 590 | /*
|
---|
| 591 | * (non-Javadoc)
|
---|
| 592 | *
|
---|
| 593 | * @see negotiator.Agent#chooseAction()
|
---|
| 594 | */
|
---|
| 595 | @Override
|
---|
| 596 | public final Action chooseAction() {
|
---|
| 597 | Action chosenAction = null;
|
---|
| 598 | Bid opponentBid = null;
|
---|
| 599 |
|
---|
| 600 | try {
|
---|
| 601 | switch (getActionType(this.messageOpponent)) {
|
---|
| 602 | case OFFER:
|
---|
| 603 | opponentBid = ((Offer) this.messageOpponent).getBid();
|
---|
| 604 | chosenAction = handleOffer(opponentBid);
|
---|
| 605 | break;
|
---|
| 606 | case ACCEPT:
|
---|
| 607 | case BREAKOFF:
|
---|
| 608 | break;
|
---|
| 609 | default:
|
---|
| 610 | if (this.myLastAction == null) {
|
---|
| 611 | chosenAction = new Offer(getAgentID(), proposeInitialBid());
|
---|
| 612 | } else {
|
---|
| 613 | chosenAction = this.myLastAction;
|
---|
| 614 | }
|
---|
| 615 | break;
|
---|
| 616 | }
|
---|
| 617 |
|
---|
| 618 | } catch (Exception e) {
|
---|
| 619 | e.printStackTrace();
|
---|
| 620 | chosenAction = new Offer(this.getAgentID(), myLastBid);
|
---|
| 621 | }
|
---|
| 622 | myLastAction = chosenAction;
|
---|
| 623 | if (myLastAction instanceof Offer) {
|
---|
| 624 | Bid b = ((Offer) myLastAction).getBid();
|
---|
| 625 | myLastBid = b;
|
---|
| 626 | }
|
---|
| 627 |
|
---|
| 628 | return chosenAction;
|
---|
| 629 | }
|
---|
| 630 |
|
---|
| 631 | /**
|
---|
| 632 | * Get the action type of a given action.
|
---|
| 633 | *
|
---|
| 634 | * @param action
|
---|
| 635 | * The action.
|
---|
| 636 | * @return The action type of the action.
|
---|
| 637 | */
|
---|
| 638 | private ActionType getActionType(Action action) {
|
---|
| 639 | ActionType actionType = ActionType.START;
|
---|
| 640 | if (action instanceof Offer)
|
---|
| 641 | actionType = ActionType.OFFER;
|
---|
| 642 | else if (action instanceof Accept)
|
---|
| 643 | actionType = ActionType.ACCEPT;
|
---|
| 644 | else if (action instanceof EndNegotiation)
|
---|
| 645 | actionType = ActionType.BREAKOFF;
|
---|
| 646 | return actionType;
|
---|
| 647 | }
|
---|
| 648 |
|
---|
| 649 | @Override
|
---|
| 650 | public SupportedNegotiationSetting getSupportedNegotiationSetting() {
|
---|
| 651 | return SupportedNegotiationSetting.getLinearUtilitySpaceInstance();
|
---|
| 652 | }
|
---|
| 653 |
|
---|
| 654 | @Override
|
---|
| 655 | public String getDescription() {
|
---|
| 656 | return "ANAC2011";
|
---|
| 657 | }
|
---|
| 658 | } |
---|