source: src/test/java/boaexample/HardHeadedFrequencyModel.java@ 37

package boaexample;

import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Map.Entry;

import genius.core.Bid;
import genius.core.bidding.BidDetails;
import genius.core.boaframework.BOAparameter;
import genius.core.boaframework.NegotiationSession;
import genius.core.boaframework.OpponentModel;
import genius.core.issue.Issue;
import genius.core.issue.IssueDiscrete;
import genius.core.issue.Objective;
import genius.core.issue.ValueDiscrete;
import genius.core.utility.AdditiveUtilitySpace;
import genius.core.utility.Evaluator;
import genius.core.utility.EvaluatorDiscrete;

import java.util.Set;

/**
 * BOA framework implementation of the HardHeaded Frequecy Model. My main
 * contribution to this model is that I fixed a bug in the mainbranch which
 * resulted in an equal preference of each bid in the ANAC 2011 competition.
 * Effectively, the corrupt model resulted in the offering of a random bid in
 * the ANAC 2011.
 * 
 * Default: learning coef l = 0.2; learnValueAddition v = 1.0
 * 
 * Adapted by Mark Hendrikx to be compatible with the BOA framework.
 * 
 * Tim Baarslag, Koen Hindriks, Mark Hendrikx, Alex Dirkzwager and Catholijn M.
 * Jonker. Decoupling Negotiating Agents to Explore the Space of Negotiation
 * Strategies
 * 
 */
public class HardHeadedFrequencyModel extends OpponentModel {

        // the learning coefficient is the weight that is added each turn to the
        // issue weights
        // which changed. It's a trade-off between concession speed and accuracy.
        private double learnCoef;
        // value which is added to a value if it is found. Determines how fast
        // the value weights converge.
        private int learnValueAddition;
        private int amountOfIssues;

        /**
         * Initializes the utility space of the opponent such that all value issue
         * weights are equal.
         */
        @Override
        public void init(NegotiationSession negotiationSession, Map<String, Double> parameters) {
                super.init(negotiationSession, parameters);
                this.negotiationSession = negotiationSession;
                if (parameters != null && parameters.get("l") != null) {
                        learnCoef = parameters.get("l");
                } else {
                        learnCoef = 0.2;
                }
                learnValueAddition = 1;
                initializeModel();
        }

        private void initializeModel() {
                opponentUtilitySpace = new AdditiveUtilitySpace(negotiationSession.getDomain());
                amountOfIssues = opponentUtilitySpace.getDomain().getIssues().size();
                double commonWeight = 1D / (double) amountOfIssues;

                // initialize the weights
                for (Entry<Objective, Evaluator> e : opponentUtilitySpace.getEvaluators()) {
                        // set the issue weights
                        opponentUtilitySpace.unlock(e.getKey());
                        e.getValue().setWeight(commonWeight);
                        try {
                                // set all value weights to one (they are normalized when
                                // calculating the utility)
                                for (ValueDiscrete vd : ((IssueDiscrete) e.getKey()).getValues())
                                        ((EvaluatorDiscrete) e.getValue()).setEvaluation(vd, 1);
                        } catch (Exception ex) {
                                ex.printStackTrace();
                        }
                }
        }

        /**
         * Determines the difference between bids. For each issue, it is determined
         * if the value changed. If this is the case, a 1 is stored in a hashmap for
         * that issue, else a 0.
         * 
         * @param a
         *            bid of the opponent
         * @param another
         *            bid
         * @return
         */
        private HashMap<Integer, Integer> determineDifference(BidDetails first, BidDetails second) {

                HashMap<Integer, Integer> diff = new HashMap<Integer, Integer>();
                try {
                        for (Issue i : opponentUtilitySpace.getDomain().getIssues()) {
                                diff.put(i.getNumber(), (((ValueDiscrete) first.getBid().getValue(i.getNumber()))
                                                .equals((ValueDiscrete) second.getBid().getValue(i.getNumber()))) ? 0 : 1);
                        }
                } catch (Exception ex) {
                        ex.printStackTrace();
                }

                return diff;
        }

        /**
         * Updates the opponent model given a bid.
         */
        @Override
        public void updateModel(Bid opponentBid, double time) {
                if (negotiationSession.getOpponentBidHistory().size() < 2) {
                        return;
                }
                int numberOfUnchanged = 0;
                BidDetails oppBid = negotiationSession.getOpponentBidHistory().getHistory()
                                .get(negotiationSession.getOpponentBidHistory().size() - 1);
                BidDetails prevOppBid = negotiationSession.getOpponentBidHistory().getHistory()
                                .get(negotiationSession.getOpponentBidHistory().size() - 2);
                HashMap<Integer, Integer> lastDiffSet = determineDifference(prevOppBid, oppBid);

                // count the number of changes in value
                for (Integer i : lastDiffSet.keySet()) {
                        if (lastDiffSet.get(i) == 0)
                                numberOfUnchanged++;
                }

                // This is the value to be added to weights of unchanged issues before
                // normalization.
                // Also the value that is taken as the minimum possible weight,
                // (therefore defining the maximum possible also).
                double goldenValue = learnCoef / (double) amountOfIssues;
                // The total sum of weights before normalization.
                double totalSum = 1D + goldenValue * (double) numberOfUnchanged;
                // The maximum possible weight
                double maximumWeight = 1D - ((double) amountOfIssues) * goldenValue / totalSum;

                // re-weighing issues while making sure that the sum remains 1
                for (Integer i : lastDiffSet.keySet()) {
                        if (lastDiffSet.get(i) == 0 && opponentUtilitySpace.getWeight(i) < maximumWeight)
                                opponentUtilitySpace.setWeight(opponentUtilitySpace.getDomain().getObjectives().get(i),
                                                (opponentUtilitySpace.getWeight(i) + goldenValue) / totalSum);
                        else
                                opponentUtilitySpace.setWeight(opponentUtilitySpace.getDomain().getObjectives().get(i),
                                                opponentUtilitySpace.getWeight(i) / totalSum);
                }

                // Then for each issue value that has been offered last time, a constant
                // value is added to its corresponding ValueDiscrete.
                try {
                        for (Entry<Objective, Evaluator> e : opponentUtilitySpace.getEvaluators()) {
                                // cast issue to discrete and retrieve value. Next, add constant
                                // learnValueAddition to the current preference of the value to
                                // make
                                // it more important
                                ((EvaluatorDiscrete) e.getValue()).setEvaluation(
                                                oppBid.getBid().getValue(((IssueDiscrete) e.getKey()).getNumber()),
                                                (learnValueAddition + ((EvaluatorDiscrete) e.getValue()).getEvaluationNotNormalized(
                                                                ((ValueDiscrete) oppBid.getBid().getValue(((IssueDiscrete) e.getKey()).getNumber())))));
                        }
                } catch (Exception ex) {
                        ex.printStackTrace();
                }
        }

        @Override
        public double getBidEvaluation(Bid bid) {
                double result = 0;
                try {
                        result = opponentUtilitySpace.getUtility(bid);
                } catch (Exception e) {
                        e.printStackTrace();
                }
                return result;
        }

        @Override
        public String getName() {
                return "HardHeaded Frequency Model example";
        }

        @Override
        public Set<BOAparameter> getParameterSpec() {
                Set<BOAparameter> set = new HashSet<BOAparameter>();
                set.add(new BOAparameter("l", 0.2,
                                "The learning coefficient determines how quickly the issue weights are learned"));
                return set;
        }
}