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package it.cnr.istc.pst.platinum.ai.deliberative.strategy; |
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import java.util.Map; |
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import it.cnr.istc.pst.platinum.ai.deliberative.solver.SearchSpaceNode; |
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import it.cnr.istc.pst.platinum.ai.framework.domain.component.DomainComponent; |
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/** |
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* |
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* @author alessandro |
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* |
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*/ |
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public class StandardDeviationMinimizationSearchStrategy extends SearchStrategy { |
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/** |
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* |
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*/ |
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protected StandardDeviationMinimizationSearchStrategy() { |
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super("TimelineSDMinimizationSearchStrategy"); |
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} |
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/** |
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* |
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*/ |
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@Override |
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public void enqueue(SearchSpaceNode node) { |
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// compute a pessimistic estimation of flaw resolution cost |
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Map<DomainComponent, Double[]> h = this.computeHeuristicCost(node); |
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// set heuristic estimation |
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node.setHeuristicCost(h); |
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// add the node to the priority queue |
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this.fringe.offer(node); |
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} |
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/** |
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* |
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*/ |
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@Override |
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public int compare(SearchSpaceNode o1, SearchSpaceNode o2) { |
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// get plan horizon |
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long horizon = this.pdb.getHorizon(); |
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double sd1 = this.standardDevitation(horizon, o1); |
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double sd2 = this.standardDevitation(horizon, o2); |
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// greedy search checking standard deviation to the horizon |
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return o1.getDepth() > o2.getDepth() ? -1 : o1.getDepth() < o2.getDepth() ? 1 : |
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// check standard deviations |
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sd1 < sd2 ? -1 : sd1 > sd2 ? 1 : |
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0; |
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} |
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/** |
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* |
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* @param horizon |
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* @param node |
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* @return |
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*/ |
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public double standardDevitation(long horizon, SearchSpaceNode node) { |
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// initialize standard deviation with horizon |
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double sd = horizon; |
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// get estimated makespan of the plan |
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Map<DomainComponent, Double[]> mk = node.getEstimatedMakespan(); |
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double N = mk.keySet().size(); |
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double quadSum = 0; |
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for (DomainComponent comp : mk.keySet()) { |
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quadSum += Math.pow((mk.get(comp)[0] - horizon), 2.0); |
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} |
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// check data |
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if (N > 0 && quadSum > 0) { |
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// compute standard deviation |
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sd = Math.sqrt(quadSum / N); |
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} |
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// return standard deviation |
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return sd; |
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} |
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} |
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pstlab /
PLATINUm
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