Examples of aima.core.search.framework.Problem

aima.core.search.framework.Problem
Artificial Intelligence A Modern Approach (3rd Edition): page 66.

A problem can be defined formally by five components:
- The initial state that the agent starts in.
- A description of the possible actions available to the agent. Given a particular state s, ACTIONS(s) returns the set of actions that can be executed in s.
- A description of what each action does; the formal name for this is the transition model, specified by a function RESULT(s, a) that returns the state that results from doing action a in state s.
- The goal test, which determines whether a given state is a goal state.
- A path cost function that assigns a numeric cost to each path. The problem-solving agent chooses a cost function that reflects its own performance measure. The step cost of taking action a in state s to reach state s' is denoted by c(s,a,s')
@author Ravi Mohan @author Ciaran O'Reilly @author Mike Stampone

        MapFunctionFactory.getResultFunction(), new DefaultGoalTest(
            goalState), new MapStepCostFunction(map));


    this.map = map;


    reverseProblem = new Problem(goalState,
        MapFunctionFactory.getActionsFunction(map),
        MapFunctionFactory.getResultFunction(), new DefaultGoalTest(
            initialState), new MapStepCostFunction(map));
  }

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    searchOutcome = SearchOutcome.PATH_NOT_FOUND;


    clearInstrumentation();


    Problem op = ((BidirectionalProblem) p).getOriginalProblem();
    Problem rp = ((BidirectionalProblem) p).getReverseProblem();


    CachedStateQueue<Node> opFrontier = new CachedStateQueue<Node>();
    CachedStateQueue<Node> rpFrontier = new CachedStateQueue<Node>();


    GraphSearch ogs = new GraphSearch();
    GraphSearch rgs = new GraphSearch();
    // Ensure the instrumentation for these
    // are cleared down as their values
    // are used in calculating the overall
    // bidirectional metrics.
    ogs.clearInstrumentation();
    rgs.clearInstrumentation();


    Node opNode = new Node(op.getInitialState());
    Node rpNode = new Node(rp.getInitialState());
    opFrontier.insert(opNode);
    rpFrontier.insert(rpNode);


    setQueueSize(opFrontier.size() + rpFrontier.size());
    setNodesExpanded(ogs.getNodesExpanded() + rgs.getNodesExpanded());

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     */
    protected void addAgent() throws Exception {
      if (agent == null) {
        int pSel = frame.getSelection().getValue(
            EightPuzzleFrame.SEARCH_SEL);
        Problem problem = new Problem(env.getBoard(),
            EightPuzzleFunctionFactory.getActionsFunction(),
            EightPuzzleFunctionFactory.getResultFunction(),
            new EightPuzzleGoalTest());
        Search search = SEARCH_ALGOS.get(pSel);
        agent = new SearchAgent(problem, search);

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        ActionsFunction af;
        if (pSel == 0)
          af = NQueensFunctionFactory.getIActionsFunction();
        else
          af = NQueensFunctionFactory.getCActionsFunction();
        Problem problem = new Problem(env.getBoard(), af,
            NQueensFunctionFactory.getResultFunction(),
            new NQueensGoalTest());
        Search search = SEARCH_ALGOS.get(sSel);
        agent = new SearchAgent(problem, search);
        env.addAgent(agent);

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    switch (state.getValue(MapAgentFrame.AGENT_SEL)) {
    case 0:
      agent = new MapAgent(map, env, search, new String[] { locs[1] });
      break;
    case 1:
      Problem p = new BidirectionalMapProblem(map, null, locs[1]);
      OnlineSearchProblem osp = new OnlineSearchProblem
      (p.getActionsFunction(), p.getGoalTest(), p.getStepCostFunction());
      agent = new LRTAStarAgent
      (osp, MapFunctionFactory.getPerceptToStateFunction(), heuristic);
      break;
    }
    env.addAgent(agent, locs[0]);

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  }


  private static void nQueensWithRecursiveDLS() {
    System.out.println("\nNQueensDemo recursive DLS -->");
    try {
      Problem problem = new Problem(new NQueensBoard(8),
          NQueensFunctionFactory.getIActionsFunction(),
          NQueensFunctionFactory.getResultFunction(),
          new NQueensGoalTest());
      Search search = new DepthLimitedSearch(8);
      SearchAgent agent = new SearchAgent(problem, search);

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  }


  private static void nQueensWithBreadthFirstSearch() {
    try {
      System.out.println("\nNQueensDemo BFS -->");
      Problem problem = new Problem(new NQueensBoard(8),
          NQueensFunctionFactory.getIActionsFunction(),
          NQueensFunctionFactory.getResultFunction(),
          new NQueensGoalTest());
      Search search = new BreadthFirstSearch(new TreeSearch());
      SearchAgent agent2 = new SearchAgent(problem, search);

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  }


  private static void nQueensWithDepthFirstSearch() {
    System.out.println("\nNQueensDemo DFS -->");
    try {
      Problem problem = new Problem(new NQueensBoard(8),
          NQueensFunctionFactory.getIActionsFunction(),
          NQueensFunctionFactory.getResultFunction(),
          new NQueensGoalTest());
      Search search = new DepthFirstSearch(new GraphSearch());
      SearchAgent agent = new SearchAgent(problem, search);

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  }


  private static void nQueensWithIterativeDeepeningSearch() {
    System.out.println("\nNQueensDemo Iterative DS  -->");
    try {
      Problem problem = new Problem(new NQueensBoard(8),
          NQueensFunctionFactory.getIActionsFunction(),
          NQueensFunctionFactory.getResultFunction(),
          new NQueensGoalTest());
      Search search = new IterativeDeepeningSearch();
      SearchAgent agent = new SearchAgent(problem, search);

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  }


  private static void nQueensSimulatedAnnealingSearch() {
    System.out.println("\nNQueensDemo Simulated Annealing  -->");
    try {
      Problem problem = new Problem(new NQueensBoard(8),
          NQueensFunctionFactory.getIActionsFunction(),
          NQueensFunctionFactory.getResultFunction(),
          new NQueensGoalTest());
      SimulatedAnnealingSearch search = new SimulatedAnnealingSearch(
          new AttackingPairsHeuristic());

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Related Classes of aima.core.search.framework.Problem

aima.core.environment.map.BidirectionalMapProblem

aima.core.search.uninformed.BidirectionalSearch

aima.gui.applications.search.games.EightPuzzleApp$EightPuzzleController

aima.gui.applications.search.games.NQueensApp$NQueensController

aima.gui.demo.search.EightPuzzleDemo

aima.gui.demo.search.NQueensDemo

aima.test.core.unit.search.framework.SolutionCheckerTest

aima.test.core.unit.search.informed.AStarSearchTest

aima.test.core.unit.search.informed.GreedyBestFirstSearchTest

aima.test.core.unit.search.uninformed.BreadthFirstSearchTest

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