1 | package ai2020.group6;
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2 |
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3 | import java.math.BigDecimal;
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4 |
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5 | import geniusweb.inform.Settings;
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6 |
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7 | /**
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8 | * MAMirroredExponential accepts and generates bids above a threshold exponentially
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9 | * decreasing as a function of time.
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10 | * The threshold starts at the "upperThreshold" (defaulting to 1) and decreases
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11 | * to the "lowerThreshold" (defaulting to 0) by scaling with 1-t^(1/e), where t is
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12 | * the progress over time and e is the parameter "e" (defaulting to 1).
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13 | *
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14 | * This is the default behaviour of the exampleparties provided by the geniusweb
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15 | * framework;
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16 | * Hardliner: e = 0'
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17 | * Boulware: e = 0.2
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18 | * Linear: e = 1
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19 | * TimeDependentParty: e = 1.2
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20 | * Conceder: e = 2
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21 | *
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22 | * ' e = 0 would theoretically give the same result, but is not allowed for this agent,
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23 | * as this would leave this agent with a static threshold of "upperThreshold" and
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24 | * is therefore replaceable with the MAStatic agent.
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25 | *
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26 | * @author Group 6
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27 | */
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28 | public class MAMirroredExponential extends MADefaultParty {
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29 |
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30 | @Override
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31 | protected IAcceptanceStrategy getAccceptanceStrategy ( Settings settings ) {
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32 | Object val = settings.getParameters().get("minPower");
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33 | Integer minpower = (val instanceof Integer) ? (Integer) val : 2;
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34 | val = settings.getParameters().get("maxPower");
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35 | Integer maxpower = (val instanceof Integer) ? (Integer) val : Integer.MAX_VALUE;
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36 | val = settings.getParameters().get("upperThreshold");
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37 | Double upperThreshold = (val instanceof Double) ? (Double) val : 1.0;
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38 | val = settings.getParameters().get("lowerThreshold");
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39 | Double lowerThreshold = (val instanceof Double) ? (Double) val : 0.0;
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40 | val = settings.getParameters().get("e");
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41 | Double e = (val instanceof Double) ? (Double) val : 1.0;
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42 | return new UtilityBasedAcceptanceStrategy(minpower, maxpower) {
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43 | @Override
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44 | public BigDecimal getUtilityThreshold ( MAState state ) {
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45 | Double t = state.getProgressTime().doubleValue();
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46 | Double scale = 1-Math.pow(t, 1/e);
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47 | return BigDecimal.valueOf(lowerThreshold + scale * (upperThreshold - lowerThreshold));
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48 | }
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49 | };
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50 | }
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51 |
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52 | @Override
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53 | protected IBiddingStrategy getBiddingStrategy ( Settings settings ) {
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54 | Object val = settings.getParameters().get("minPower");
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55 | Integer minpower = (val instanceof Integer) ? (Integer) val : 2;
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56 | val = settings.getParameters().get("maxPower");
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57 | Integer maxpower = (val instanceof Integer) ? (Integer) val : Integer.MAX_VALUE;
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58 | val = settings.getParameters().get("upperThreshold");
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59 | Double upperThreshold = (val instanceof Double) ? (Double) val : 1.0;
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60 | val = settings.getParameters().get("lowerThreshold");
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61 | Double lowerThreshold = (val instanceof Double) ? (Double) val : 0.0;
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62 | val = settings.getParameters().get("e");
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63 | Double e = (val instanceof Double) ? (Double) val : 1.0;
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64 | return new UtilityBasedBiddingStrategy(minpower, maxpower) {
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65 | @Override
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66 | public BigDecimal getUpperUtilityThreshold ( MAState state ) {
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67 | return BigDecimal.valueOf(upperThreshold);
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68 | }
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69 | @Override
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70 | public BigDecimal getLowerUtilityThreshold ( MAState state ) {
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71 | Double t = state.getProgressTime().doubleValue();
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72 | Double scale = 1-Math.pow(t, 1/e);
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73 | return BigDecimal.valueOf(lowerThreshold + scale * (upperThreshold - lowerThreshold));
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74 | }
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75 | };
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76 | }
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77 |
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78 | @Override
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79 | protected IOptInStrategy getOptInStrategy ( Settings settings ) {
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80 | return new NoOptInStrategy();
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81 | }
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82 |
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83 | // @Override
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84 | // protected IOpponentModel initNewOpponentModel ( Settings settings ) {
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85 | // return new EmptyOpponentModel();
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86 | // }
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87 |
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88 | }
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