[127] | 1 | package negotiator.boaframework.opponentmodel;
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| 2 |
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| 3 | import java.util.HashMap;
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| 4 | import java.util.List;
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| 5 | import java.util.Map;
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| 6 |
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| 7 | import genius.core.Bid;
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| 8 | import genius.core.boaframework.NegotiationSession;
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| 9 | import genius.core.boaframework.OpponentModel;
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| 10 | import genius.core.issue.Issue;
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| 11 | import genius.core.issue.Value;
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| 12 | import genius.core.utility.AdditiveUtilitySpace;
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| 13 | import negotiator.boaframework.opponentmodel.agentlg.BidStatistic;
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| 14 | import negotiator.boaframework.opponentmodel.tools.UtilitySpaceAdapter;
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| 15 |
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| 16 | /**
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| 17 | * Adaptation of the opponent model used by AgentLG in the ANAC2012 to be
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| 18 | * compatible with the BOA framework.
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| 19 | *
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| 20 | * Note that originally, the model sums up all value scores which entails that
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| 21 | * the total preference of a bid can be as high as offered bids * issues. The
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| 22 | * value score was equal to the amount of times the value was offered divided by
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| 23 | * total amount of bids offered by the opponent.
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| 24 | *
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| 25 | * In my implementation I normalize each value score by the highest value,
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| 26 | * similar to the implementation of preference profiles in Genius. Finally I sum
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| 27 | * all results and divide by the amount of issues. This is identical to assuming
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| 28 | * that the issue weights are uniform.
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| 29 | *
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| 30 | * Tim Baarslag, Koen Hindriks, Mark Hendrikx, Alex Dirkzwager and Catholijn M.
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| 31 | * Jonker. Decoupling Negotiating Agents to Explore the Space of Negotiation
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| 32 | * Strategies
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| 33 | *
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| 34 | * @author Mark Hendrikx
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| 35 | */
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| 36 | public class AgentLGModel extends OpponentModel {
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| 37 |
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| 38 | /**
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| 39 | * Creates objects to keep track of how many times each value has been
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| 40 | * offered for an issue
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| 41 | */
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| 42 | private HashMap<Issue, BidStatistic> statistic = new HashMap<Issue, BidStatistic>();
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| 43 | /** Cache the issues of the domain for performanc reasons */
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| 44 | private List<Issue> issues;
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| 45 |
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| 46 | /**
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| 47 | * Initialize the opponent model by creating an object to keep track of the
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| 48 | * values for each issue.
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| 49 | */
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| 50 | @Override
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| 51 | public void init(NegotiationSession negotiationSession, Map<String, Double> parameters) {
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| 52 | this.negotiationSession = negotiationSession;
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| 53 | issues = negotiationSession.getUtilitySpace().getDomain().getIssues();
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| 54 | for (Issue issue : issues) {
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| 55 | statistic.put(issue, new BidStatistic(issue));
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| 56 | }
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| 57 | }
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| 58 |
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| 59 | /**
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| 60 | * Update the opponent model by updating the value score for each issue.
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| 61 | *
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| 62 | * @param opponentBid
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| 63 | * @param time
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| 64 | * of offering
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| 65 | */
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| 66 | @Override
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| 67 | public void updateModel(Bid opponentBid, double time) {
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| 68 | try {
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| 69 | // updates statistics
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| 70 | for (Issue issue : statistic.keySet()) {
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| 71 | Value v = opponentBid.getValue(issue.getNumber());
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| 72 | statistic.get(issue).add(v);
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| 73 | }
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| 74 | } catch (Exception e) {
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| 75 | e.printStackTrace();
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| 76 | }
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| 77 | }
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| 78 |
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| 79 | /**
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| 80 | * @return utility of the opponent's bid
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| 81 | */
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| 82 | public double getBidEvaluation(Bid bid) {
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| 83 | double ret = 0;
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| 84 | for (Issue issue : issues) {
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| 85 | try {
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| 86 | ret += statistic.get(issue).getValueUtility(bid.getValue(issue.getNumber()));
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| 87 | } catch (Exception e) {
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| 88 | e.printStackTrace();
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| 89 | }
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| 90 | }
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| 91 | return (ret / issues.size());
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| 92 | }
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| 93 |
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| 94 | /**
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| 95 | * @return the uniform issue weight
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| 96 | */
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| 97 | public double getWeight(Issue issue) {
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| 98 | return (1.0 / issues.size());
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| 99 | }
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| 100 |
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| 101 | /**
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| 102 | * @return utilityspace created by using the opponent model adapter.
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| 103 | */
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| 104 | @Override
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| 105 | public AdditiveUtilitySpace getOpponentUtilitySpace() {
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| 106 | return new UtilitySpaceAdapter(this, negotiationSession.getUtilitySpace().getDomain());
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| 107 | }
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| 108 |
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| 109 | public void cleanUp() {
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| 110 | super.cleanUp();
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| 111 | }
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| 112 |
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| 113 | @Override
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| 114 | public String getName() {
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| 115 | return "AgentLG Model";
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| 116 | }
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[1] | 117 | } |
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