Examples of aima.core.search.local.HillClimbingSearch

aima.core.search.local.HillClimbingSearch
Artificial Intelligence A Modern Approach (3rd Edition): Figure 4.2, page 122.
```
 function HILL-CLIMBING(problem) returns a state that is a local maximum current <- MAKE-NODE(problem.INITIAL-STATE) loop do neighbor <- a highest-valued successor of current if neighbor.VALUE <= current.VALUE then return current.STATE current <- neighbor 
```
Figure 4.2 The hill-climbing search algorithm, which is the most basic local search technique. At each step the current node is replaced by the best neighbor; in this version, that means the neighbor with the highest VALUE, but if a heuristic cost estimate h is used, we would find the neighbor with the lowest h. @author Ravi Mohan @author Mike Stampone

    case RBF_SEARCH:
      result = new RecursiveBestFirstSearch(new AStarEvaluationFunction(
          hf));
      break;
    case HILL_SEARCH:
      result = new HillClimbingSearch(hf);
      break;
    }
    return result;
  }

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    try {
      Problem problem = new Problem(new NQueensBoard(8),
          NQueensFunctionFactory.getIActionsFunction(),
          NQueensFunctionFactory.getResultFunction(),
          new NQueensGoalTest());
      HillClimbingSearch search = new HillClimbingSearch(
          new AttackingPairsHeuristic());
      SearchAgent agent = new SearchAgent(problem, search);


      System.out.println();
      printActions(agent.getActions());
      System.out.println("Search Outcome=" + search.getOutcome());
      System.out.println("Final State=\n" + search.getLastSearchState());
      printInstrumentation(agent.getInstrumentation());
    } catch (Exception e) {
      e.printStackTrace();
    }
  }

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Related Classes of aima.core.search.local.HillClimbingSearch

aima.core.search.framework.Node

aima.gui.applications.search.map.SearchFactory

aima.gui.demo.search.NQueensDemo

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