Examples with LRTAStarAgent aima.core.search.online.LRTAStarAgent used on opensource projects

Example 1 with LRTAStarAgent

use of aima.core.search.online.LRTAStarAgent in project aima-java by aimacode.

the class LRTAStarAgentTest method testNormalSearch.

@Test
public void testNormalSearch() {
    MapEnvironment me = new MapEnvironment(aMap);
    OnlineSearchProblem<String, MoveToAction> problem = new GeneralProblem<>(null, MapFunctions.createActionsFunction(aMap), null, GoalTest.isEqual("F"), MapFunctions.createDistanceStepCostFunction(aMap));
    LRTAStarAgent<String, MoveToAction> agent = new LRTAStarAgent<>(problem, MapFunctions.createPerceptToStateFunction(), h);
    me.addAgent(agent, "A");
    me.addEnvironmentView(new TestEnvironmentView());
    me.stepUntilDone();
    Assert.assertEquals("Action[name==moveTo, location==B]->Action[name==moveTo, location==A]->Action[name==moveTo, location==B]->Action[name==moveTo, location==C]->Action[name==moveTo, location==B]->Action[name==moveTo, location==C]->Action[name==moveTo, location==D]->Action[name==moveTo, location==C]->Action[name==moveTo, location==D]->Action[name==moveTo, location==E]->Action[name==moveTo, location==D]->Action[name==moveTo, location==E]->Action[name==moveTo, location==F]->Action[name==NoOp]->", envChanges.toString());
}

Example 2 with LRTAStarAgent

use of aima.core.search.online.LRTAStarAgent in project aima-java by aimacode.

the class LRTAStarAgentTest method testNoPath.

@Test
public void testNoPath() {
    MapEnvironment me = new MapEnvironment(aMap);
    OnlineSearchProblem<String, MoveToAction> problem = new GeneralProblem<>(null, MapFunctions.createActionsFunction(aMap), null, GoalTest.isEqual("G"), MapFunctions.createDistanceStepCostFunction(aMap));
    LRTAStarAgent<String, MoveToAction> agent = new LRTAStarAgent<>(problem, MapFunctions.createPerceptToStateFunction(), h);
    me.addAgent(agent, "A");
    me.addEnvironmentView(new TestEnvironmentView());
    // Note: Will search forever if no path is possible,
    // Therefore restrict the number of steps to something
    // reasonablbe, against which to test.
    me.step(14);
    Assert.assertEquals("Action[name==moveTo, location==B]->Action[name==moveTo, location==A]->Action[name==moveTo, location==B]->Action[name==moveTo, location==C]->Action[name==moveTo, location==B]->Action[name==moveTo, location==C]->Action[name==moveTo, location==D]->Action[name==moveTo, location==C]->Action[name==moveTo, location==D]->Action[name==moveTo, location==E]->Action[name==moveTo, location==D]->Action[name==moveTo, location==E]->Action[name==moveTo, location==F]->Action[name==moveTo, location==E]->", envChanges.toString());
}

Example 3 with LRTAStarAgent

use of aima.core.search.online.LRTAStarAgent in project aima-java by aimacode.

the class LRTAStarAgentTest method testAlreadyAtGoal.

@Test
public void testAlreadyAtGoal() {
    MapEnvironment me = new MapEnvironment(aMap);
    OnlineSearchProblem<String, MoveToAction> problem = new GeneralProblem<>(null, MapFunctions.createActionsFunction(aMap), null, GoalTest.isEqual("A"), MapFunctions.createDistanceStepCostFunction(aMap));
    LRTAStarAgent<String, MoveToAction> agent = new LRTAStarAgent<>(problem, MapFunctions.createPerceptToStateFunction(), h);
    me.addAgent(agent, "A");
    me.addEnvironmentView(new TestEnvironmentView());
    me.stepUntilDone();
    Assert.assertEquals("Action[name==NoOp]->", envChanges.toString());
}

Example 4 with LRTAStarAgent

use of aima.core.search.online.LRTAStarAgent in project aima-java by aimacode.

the class OsmAgentController method initAgents.

/** Creates new agents and adds them to the current environment. */
protected void initAgents(MessageLogger logger) {
    List<MapNode> markers = map.getOsmMap().getMarkers();
    if (markers.size() < 2) {
        logger.log("Error: Please set two markers with mouse-left.");
        return;
    }
    String[] locs = new String[markers.size()];
    for (int i = 0; i < markers.size(); i++) {
        MapNode node = markers.get(i);
        Point2D pt = new Point2D(node.getLon(), node.getLat());
        locs[i] = map.getNearestLocation(pt);
    }
    MapAgentFrame.SelectionState state = frame.getSelection();
    heuristic = createHeuristic(state.getIndex(MapAgentFrame.HEURISTIC_SEL), locs[1]);
    search = SearchFactory.getInstance().createSearch(state.getIndex(MapAgentFrame.SEARCH_SEL), state.getIndex(MapAgentFrame.Q_SEARCH_IMPL_SEL), heuristic);
    Agent agent = null;
    switch(state.getIndex(MapAgentFrame.AGENT_SEL)) {
        case 0:
            agent = new SimpleMapAgent(map, env, search, new String[] { locs[1] });
            break;
        case 1:
            Problem<String, MoveToAction> p = new BidirectionalMapProblem(map, null, locs[1]);
            OnlineSearchProblem<String, MoveToAction> osp = new GeneralProblem<>(null, p::getActions, null, p::testGoal, p::getStepCosts);
            agent = new LRTAStarAgent<>(osp, MapFunctions.createPerceptToStateFunction(), s -> heuristic.applyAsDouble(new Node<>(s)));
            break;
    }
    env.addAgent(agent, locs[0]);
}