public void testMultiGoalProblem()
{
Map romaniaMap = new SimplifiedRoadMapOfPartOfRomania();
Problem problem = new Problem(SimplifiedRoadMapOfPartOfRomania.ARAD,
MapFunctionFactory.getActionsFunction(romaniaMap),
MapFunctionFactory.getResultFunction(), new DualMapGoalTest(
SimplifiedRoadMapOfPartOfRomania.BUCHAREST,
SimplifiedRoadMapOfPartOfRomania.HIRSOVA),
new MapStepCostFunction(romaniaMap));
Search search = new BreadthFirstSearch(new GraphSearch());
SearchAgent agent = new SearchAgent(problem, search);
Assert
.Equals(
"[Action[name==moveTo, location==Sibiu], Action[name==moveTo, location==Fagaras], Action[name==moveTo, location==Bucharest], Action[name==moveTo, location==Urziceni], Action[name==moveTo, location==Hirsova]]",
agent.getActions().ToString());
Assert.Equals(5, agent.getActions().Count);
Assert.Equals("14", agent.getInstrumentation()[
"nodesExpanded"]);
Assert.Equals("1", agent.getInstrumentation()[
"queueSize"]);
Assert.Equals("5", agent.getInstrumentation()[
"maxQueueSize"]);
}
static void RunAllLevels()
{
string[] levels = new string[] {
//"Levels/LA 1.xml","Levels/LA 2.xml","Levels/LA 3.xml","Levels/LA 4.xml",
//"Levels/LA 6.xml","Levels/LA 7.xml",
//"Levels/LA 8.xml","Levels/LoZ 1.xml",
//"Levels/LoZ 2.xml","Levels/LoZ 3.xml","Levels/LoZ 4.xml","Levels/LoZ 5.xml",
//"Levels/LoZ 7.xml","Levels/LoZ 8.xml","Levels/LoZ 9.xml","Levels/LoZ2 1.xml",
//"Levels/LoZ2 2.xml","Levels/LoZ2 4.xml","Levels/LoZ2 5.xml","Levels/LoZ2 6.xml",
//"Levels/LoZ2 7.xml","Levels/LoZ2 8.xml",
//"Levels/LoZ2 9.xml",
"Levels/LttP 1.xml",
//"Levels/LttP 10.xml",
//"Levels/LttP 11.xml",
//"Levels/LttP 2.xml","Levels/LttP 3.xml",
//"Levels/LttP 4.xml","Levels/LttP 5.xml","Levels/LttP 6.xml","Levels/LttP 7.xml",
//"Levels/LttP 8.xml","Levels/LttP 9.xml",
};
foreach (var level in levels)
{
Console.WriteLine(level);
Dungeon dungeon = new Dungeon(level);
SearchAgent path = dungeon.getOptimalPath(level.Contains("LttP"));
Console.WriteLine(path.pathToString());
dungeon.UpdateRooms(path);
string output = level;
output = Regex.Replace(output, @"Levels", "Summaries");
output = Regex.Replace(output, " ", "");
dungeon.WriteStats(output, path);
}
}
public void testAStarSearch()
{
// added to narrow down bug report filed by L.N.Sudarshan of
// Thoughtworks and Xin Lu of UCI
// EightPuzzleBoard extreme = new EightPuzzleBoard(new int[]
// {2,0,5,6,4,8,3,7,1});
// EightPuzzleBoard extreme = new EightPuzzleBoard(new int[]
// {0,8,7,6,5,4,3,2,1});
EightPuzzleBoard board = new EightPuzzleBoard(new int[]
{ 7, 1, 8, 0, 4, 6, 2, 3, 5 });
//EightPuzzleBoard board = new EightPuzzleBoard(new int[]
//{ 1, 0, 2, 3, 4, 5, 6, 7, 8 });
IProblem <EightPuzzleBoard, IAction> problem = new BidirectionalEightPuzzleProblem(board);
ISearchForActions <EightPuzzleBoard, IAction> search
= new AStarSearch <EightPuzzleBoard, IAction>(
new GraphSearch <EightPuzzleBoard, IAction>(),
EightPuzzleFunctions.createManhattanHeuristicFunction());
SearchAgent <EightPuzzleBoard, IAction> agent
= new SearchAgent <EightPuzzleBoard, IAction>(problem, search);
Assert.AreEqual(23, agent.getActions().Size());
Assert.AreEqual("1133", // "926" GraphSearchReduced Frontier
agent.getInstrumentation().getProperty("nodesExpanded"));
Assert.AreEqual("676", // "534" GraphSearchReduced Frontier
agent.getInstrumentation().getProperty("queueSize"));
Assert.AreEqual("677", // "535" GraphSearchReduced Frontier
agent.getInstrumentation().getProperty("maxQueueSize"));
}
public void Resolve()
{
rubickCube.Apply();
Problem problem = new Problem(rubickCube,
new RubickSuccessorFunction(),
new RubickGoalTest(),
new RubickHeuristicFunction());
Search search = new IterativeDeepeningSearch();
//Search search = new AStarSearch(new GraphSearch());
SearchAgent agent = new SearchAgent(problem, search);
int iterations = -1;
ArrayList actions = agent.getActions();
Hashtable info = agent.getInstrumentation();
for (int i = 0; i < actions.Count; i++)
{
RubickMovementTypes move = (RubickMovementTypes)Enum.Parse(typeof(RubickMovementTypes), (string)actions[i]);
rubickCube.Transform(move);
}
//aStarSearchTree.Search(rubickCube);
//if (aStarSearchTree.result != null)
//{
// rubickCube = aStarSearchTree.result;
// iterations = aStarSearchTree.iterations;
//}
DispatchEvent(new RubickCubeResolvedOutputEvent((RubickColorMatrix)rubickCube.Clone(), iterations));
}
public void testAIMA3eFigure3_15()
{
Map romaniaMap = new SimplifiedRoadMapOfPartOfRomania();
IProblem <string, MoveToAction> problem = new GeneralProblem <string, MoveToAction>(
SimplifiedRoadMapOfPartOfRomania.SIBIU,
MapFunctions.createActionsFunction(romaniaMap),
MapFunctions.createResultFunction(),
SimplifiedRoadMapOfPartOfRomania.BUCHAREST.Equals,
MapFunctions.createDistanceStepCostFunction(romaniaMap));
ISearchForActions <string, MoveToAction> search
= new AStarSearch <string, MoveToAction>(
new GraphSearch <string, MoveToAction>(),
MapFunctions.createSLDHeuristicFunction(
SimplifiedRoadMapOfPartOfRomania.BUCHAREST,
romaniaMap));
SearchAgent <string, MoveToAction> agent = new SearchAgent <string, MoveToAction>(problem, search);
ICollection <MoveToAction> actions = agent.getActions();
Assert.AreEqual(
"[Action[name==moveTo, location==RimnicuVilcea], Action[name==moveTo, location==Pitesti], Action[name==moveTo, location==Bucharest]]",
actions.ToString());
Assert.AreEqual("278",
search.getMetrics().get(QueueSearch <string, MoveToAction> .METRIC_PATH_COST));
}
static void Main(string[] args)
{
var environment = new PuzzleEnvironment <int>(GetEightPuzzleBoard());
var bfs = new BreadthFirstStrategy <ProblemState <int>, ProblemAction>();
var ucs = new UniformCostStrategy <ProblemState <int>, ProblemAction>();
var ucs2 = new UniformCostStrategy2 <ProblemState <int>, ProblemAction>();
var dfs = new DepthFirstStrategy <ProblemState <int>, ProblemAction>();
var dls = new DepthLimitedStrategy <ProblemState <int>, ProblemAction>(10);
var ids = new IterativeDeepeningStrategy <ProblemState <int>, ProblemAction>();
var greedy = new GreedyBestFirstStrategy <ProblemState <int>, ProblemAction>(
HeuristicFunctions.StraightLineDistance);
var astar = new AStarStrategy <ProblemState <int>, ProblemAction>(
HeuristicFunctions.StraightLineDistance);
var agent = new SearchAgent <int>(astar);
RunAgent(environment, agent);
}
public void testGreedyBestFirstSearch() {
try {
// EightPuzzleBoard extreme = new EightPuzzleBoard(new int[]
// {2,0,5,6,4,8,3,7,1});
// EightPuzzleBoard extreme = new EightPuzzleBoard(new int[]
// {0,8,7,6,5,4,3,2,1});
EightPuzzleBoard board = new EightPuzzleBoard(new int[] { 7, 1, 8,
0, 4, 6, 2, 3, 5 });
Problem problem = new Problem(board, EightPuzzleFunctionFactory
.getActionsFunction(), EightPuzzleFunctionFactory
.getResultFunction(), new EightPuzzleGoalTest());
Search search = new GreedyBestFirstSearch(new GraphSearch(),
new ManhattanHeuristicFunction());
SearchAgent agent = new SearchAgent(problem, search);
Assert.assertEquals(49, agent.getActions().size());
Assert.assertEquals("197", agent.getInstrumentation().getProperty(
"nodesExpanded"));
Assert.assertEquals("140", agent.getInstrumentation().getProperty(
"queueSize"));
Assert.assertEquals("141", agent.getInstrumentation().getProperty(
"maxQueueSize"));
} catch (Exception e) {
e.printStackTrace();
Assert.fail("Exception thrown.");
}
}
//幅優先探索 basePosition:始点座標 judgePosition:通行可能条件 dMOFunction:各座標に対する処理
public void DetureMatrixOperate(Vector2Int basePosition, JudgePosition judgePosition, DMOFunction dMOFunction)
{
int[,] searchMatrix = new int[mapRange.x, mapRange.y];
Queue <SearchAgent> searchAgent = new Queue <SearchAgent>();
searchAgent.Enqueue(new SearchAgent()
{
position = basePosition, distance = 1
});
searchMatrix[basePosition.x, basePosition.y] = 1;
while (0 < searchAgent.Count)
{
SearchAgent current = searchAgent.Dequeue();
NextPoint(current.position, (x, y) =>
{
if (searchMatrix[x, y] == 0 && judgePosition(new Vector2Int(x, y)) == true)
{
searchAgent.Enqueue(new SearchAgent()
{
position = new Vector2Int(x, y), distance = current.distance + 1
});
searchMatrix[x, y] = current.distance + 1;
}
});
}
MatrixOperate((xcount, ycount) =>
{
dMOFunction(xcount, ycount, searchMatrix[xcount, ycount]);
});
}
static void nQueensHillClimbingSearch()
{
System.Console.WriteLine("\nNQueensDemo HillClimbing -->");
try
{
IProblem <NQueensBoard, QueenAction>
problem = NQueensFunctions.createCompleteStateFormulationProblem(
boardSize,
NQueensBoard.Config.QUEENS_IN_FIRST_ROW);
HillClimbingSearch <NQueensBoard, QueenAction>
search = new HillClimbingSearch <NQueensBoard, QueenAction>(
NQueensFunctions.createAttackingPairsHeuristicFunction());
SearchAgent <NQueensBoard, QueenAction>
agent = new SearchAgent <NQueensBoard, QueenAction>(problem, search);
System.Console.WriteLine();
printActions(agent.getActions());
System.Console.WriteLine("Search Outcome=" + search.getOutcome());
System.Console.WriteLine("Final State=\n" + search.getLastSearchState());
printInstrumentation(agent.getInstrumentation());
}
catch (Exception e)
{
throw e;
}
}
public void testAStarSearch() {
// added to narrow down bug report filed by L.N.Sudarshan of
// Thoughtworks and Xin Lu of UCI
try {
// EightPuzzleBoard extreme = new EightPuzzleBoard(new int[]
// {2,0,5,6,4,8,3,7,1});
// EightPuzzleBoard extreme = new EightPuzzleBoard(new int[]
// {0,8,7,6,5,4,3,2,1});
EightPuzzleBoard board = new EightPuzzleBoard(new int[] { 7, 1, 8,
0, 4, 6, 2, 3, 5 });
Problem problem = new Problem(board, EightPuzzleFunctionFactory
.getActionsFunction(), EightPuzzleFunctionFactory
.getResultFunction(), new EightPuzzleGoalTest());
Search search = new AStarSearch(new GraphSearch(),
new ManhattanHeuristicFunction());
SearchAgent agent = new SearchAgent(problem, search);
Assert.assertEquals(23, agent.getActions().size());
Assert.assertEquals("926", agent.getInstrumentation().getProperty(
"nodesExpanded"));
Assert.assertEquals("534", agent.getInstrumentation().getProperty(
"queueSize"));
Assert.assertEquals("535", agent.getInstrumentation().getProperty(
"maxQueueSize"));
} catch (Exception e) {
e.printStackTrace();
Assert.fail("Exception thrown");
}
}
public void testAIMA3eFigure3_24()
{
Map romaniaMap = new SimplifiedRoadMapOfPartOfRomania();
IProblem <string, MoveToAction> problem = new GeneralProblem <string, MoveToAction>(
SimplifiedRoadMapOfPartOfRomania.ARAD,
MapFunctions.createActionsFunction(romaniaMap),
MapFunctions.createResultFunction(),
SimplifiedRoadMapOfPartOfRomania.BUCHAREST.Equals,
MapFunctions.createDistanceStepCostFunction(romaniaMap));
ISearchForActions <string, MoveToAction> search = new AStarSearch <string, MoveToAction>(new TreeSearch <string, MoveToAction>(),
MapFunctions.createSLDHeuristicFunction(SimplifiedRoadMapOfPartOfRomania.BUCHAREST, romaniaMap));
SearchAgent <string, MoveToAction> agent = new SearchAgent <string, MoveToAction>(problem, search);
Assert.AreEqual(
"[Action[name==moveTo, location==Sibiu], Action[name==moveTo, location==RimnicuVilcea], Action[name==moveTo, location==Pitesti], Action[name==moveTo, location==Bucharest]]",
agent.getActions().ToString());
Assert.AreEqual(4, agent.getActions().Size());
Assert.AreEqual("5",
agent.getInstrumentation().getProperty("nodesExpanded"));
Assert.AreEqual("10",
agent.getInstrumentation().getProperty("queueSize"));
Assert.AreEqual("11",
agent.getInstrumentation().getProperty("maxQueueSize"));
}
static void Main(string[] args)
{
JiraApi.Instance.JiraAuthorize();
using (SearchAgent agent = new SearchAgent(JiraApi.Instance))
{
var data = agent.Search("project = WEB AND issuetype = Bug AND status = \"To Do\" AND \"Epic Link\" = WEB-3826 ORDER BY priority DESC");
}
}
private void btnBreadthFirst_Click(object sender, System.EventArgs e)
{
this.textBox1.Text = "NQueensDemo BFS -->" + System.Environment.NewLine;
Problem problem = new Problem(new NQueensBoard(8), new NQueensSuccessorFunction(), new NQueensGoalTest());
Search search = new BreadthFirstSearch(new TreeSearch());
SearchAgent agent2 = new SearchAgent(problem, search);
printActions(agent2.getActions());
printInstrumentation(agent2.getInstrumentation());
}
private void btnRecursiveDLS_Click(object sender, System.EventArgs e)
{
this.textBox1.Text = "NQueensDemo recursive DLS -->" + System.Environment.NewLine;
Problem problem = new Problem(new NQueensBoard(8), new NQueensSuccessorFunction(), new NQueensGoalTest());
Search search = new DepthLimitedSearch(8);
SearchAgent agent = new SearchAgent(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
public List <string> PrintActions(SearchAgent agent)
{
var toReturn = new List <String>();
var actions = agent.getActions().toArray();
foreach (aima.core.agent.Action action in actions)
{
toReturn.Add(action.ToString());
}
return(toReturn);
}
private void btnHillClimbing_Click(object sender, System.EventArgs e)
{
this.textBox1.Text = "NQueensDemo HillClimbing -->" + System.Environment.NewLine;
Problem problem = new Problem(new NQueensBoard(8), new NQueensSuccessorFunction(), new NQueensGoalTest(), new QueensToBePlacedHeuristic());
Search search = new HillClimbingSearch();
SearchAgent agent = new SearchAgent(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
private void btnGreedyBestFirstMan_Click(object sender, System.EventArgs e)
{
this.textBox1.Text = "EightPuzzleDemo Greedy Best First Search (ManhattanHeursitic)-->" + System.Environment.NewLine;
Problem problem = new Problem(boardWithThreeMoveSolution,
new EightPuzzleSuccessorFunction(),
new EightPuzzleGoalTest(), new ManhattanHeuristicFunction());
Search search = new GreedyBestFirstSearch(new GraphSearch());
SearchAgent agent = new SearchAgent(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
private void btnSimulatedAnnealing_Click(object sender, System.EventArgs e)
{
this.textBox1.Text = "EightPuzzleDemo Simulated Annealing Search -->" + System.Environment.NewLine;
Problem problem = new Problem(random1,
new EightPuzzleSuccessorFunction(),
new EightPuzzleGoalTest(), new ManhattanHeuristicFunction());
Search search = new SimulatedAnnealingSearch();
SearchAgent agent = new SearchAgent(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
private void btnIterativeDeeping_Click(object sender, System.EventArgs e)
{
this.textBox1.Text = "NQueensDemo Iterative DS -->" + System.Environment.NewLine;
Problem problem = new Problem(new NQueensBoard(8), new NQueensSuccessorFunction(), new NQueensGoalTest());
Search search = new IterativeDeepeningSearch();
SearchAgent agent = new SearchAgent(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
private void btnIDLS_Click(object sender, System.EventArgs e)
{
this.textBox1.Text = "EightPuzzleDemo Iterative DLS-->" + System.Environment.NewLine;
Problem problem = new Problem(random1,
new EightPuzzleSuccessorFunction(),
new EightPuzzleGoalTest());
Search search = new IterativeDeepeningSearch();
SearchAgent agent = new SearchAgent(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
private void btnAStar_Click(object sender, System.EventArgs e)
{
this.textBox1.Text = "EightPuzzleDemo AStar Search (MisplacedTileHeursitic)-->" + System.Environment.NewLine;
Problem problem = new Problem(random1,
new EightPuzzleSuccessorFunction(),
new EightPuzzleGoalTest(),
new MisplacedTilleHeuristicFunction());
Search search = new AStarSearch(new GraphSearch());
SearchAgent agent = new SearchAgent(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
public Dictionary <string, string> PrintInstrumentation(SearchAgent agent)
{
var toReturn = new Dictionary <String, String>();
var instrumentation = agent.getInstrumentation();
Iterator terator = instrumentation.keySet().iterator();
while (terator.hasNext())
{
string key = (string)terator.next();
string property = instrumentation.getProperty(key);
toReturn[key] = property;
}
return(toReturn);
}
private void OnTriggerEnter(Collider other)
{
SearchAgent searchAgent = other.GetComponent <SearchAgent>();
if (searchAgent && (searchAgent.waypoint == this || searchAgent.waypoint == null))
{
searchAgent.waypoint = m_nextWaypoint;
}
StateAgent stateAgent = other.GetComponent <StateAgent>();
if (stateAgent && (stateAgent.waypoint == this || stateAgent.waypoint == null))
{
stateAgent.waypoint = m_nextWaypoint;
}
}
static void eightPuzzleIDLSDemo()
{
System.Console.WriteLine("\nEightPuzzleDemo Iterative DLS -->");
try
{
IProblem <EightPuzzleBoard, IAction> problem = new BidirectionalEightPuzzleProblem(random1);
ISearchForActions <EightPuzzleBoard, IAction> search = new IterativeDeepeningSearch <EightPuzzleBoard, IAction>();
SearchAgent <EightPuzzleBoard, IAction> agent = new SearchAgent <EightPuzzleBoard, IAction>(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
catch (Exception e)
{
throw e;
}
}
static void eightPuzzleDLSDemo()
{
System.Console.WriteLine("\nEightPuzzleDemo recursive DLS (9) -->");
try
{
IProblem <EightPuzzleBoard, IAction> problem = new BidirectionalEightPuzzleProblem(boardWithThreeMoveSolution);
ISearchForActions <EightPuzzleBoard, IAction> search = new DepthLimitedSearch <EightPuzzleBoard, IAction>(9);
SearchAgent <EightPuzzleBoard, IAction> agent = new SearchAgent <EightPuzzleBoard, IAction>(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
catch (Exception e)
{
throw e;
}
}
static void eightPuzzleAStarDemo()
{
System.Console.WriteLine("\nEightPuzzleDemo AStar Search (MisplacedTileHeursitic)-->");
try
{
IProblem <EightPuzzleBoard, IAction> problem = new BidirectionalEightPuzzleProblem(random1);
ISearchForActions <EightPuzzleBoard, IAction>
search = new AStarSearch <EightPuzzleBoard, IAction>(
new GraphSearch <EightPuzzleBoard, IAction>(), EightPuzzleFunctions.createMisplacedTileHeuristicFunction());
SearchAgent <EightPuzzleBoard, IAction> agent = new SearchAgent <EightPuzzleBoard, IAction>(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
catch (Exception e)
{
throw e;
}
}
public void testIterativeDeepeningSearch() {
try {
Problem problem = new Problem(new NQueensBoard(8),
NQueensFunctionFactory.getIActionsFunction(),
NQueensFunctionFactory.getResultFunction(),
new NQueensGoalTest());
Search search = new IterativeDeepeningSearch();
SearchAgent agent = new SearchAgent(problem, search);
List<Action> actions = agent.getActions();
assertCorrectPlacement(actions);
Assert.assertEquals("3656", agent.getInstrumentation().getProperty(
"nodesExpanded"));
} catch (Exception e) {
e.printStackTrace();
Assert.fail("Exception should not occur");
}
}
static void nQueensWithRecursiveDLS()
{
System.Console.WriteLine("\nNQueensDemo recursive DLS -->");
try
{
IProblem<NQueensBoard, QueenAction> problem =
NQueensFunctions.createIncrementalFormulationProblem(boardSize);
ISearchForActions<NQueensBoard, QueenAction>
search = new DepthLimitedSearch<NQueensBoard, QueenAction>(boardSize);
SearchAgent<NQueensBoard, QueenAction>
agent = new SearchAgent<NQueensBoard, QueenAction>(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
catch (Exception e)
{
throw e;
}
}
static void eightPuzzleGreedyBestFirstManhattanDemo()
{
System.Console.WriteLine("\nEightPuzzleDemo Greedy Best First Search (ManhattanHeursitic)-->");
try
{
IProblem <EightPuzzleBoard, IAction> problem = new BidirectionalEightPuzzleProblem(boardWithThreeMoveSolution);
ISearchForActions <EightPuzzleBoard, IAction>
search = new GreedyBestFirstSearch <EightPuzzleBoard, IAction>(
new GraphSearch <EightPuzzleBoard, IAction>(),
EightPuzzleFunctions.createManhattanHeuristicFunction());
SearchAgent <EightPuzzleBoard, IAction> agent = new SearchAgent <EightPuzzleBoard, IAction>(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
catch (Exception e)
{
throw e;
}
}
public void testGreedyBestFirstSearchReducedFrontier()
{
// EightPuzzleBoard extreme = new EightPuzzleBoard(new int[]
// {2,0,5,6,4,8,3,7,1});
// EightPuzzleBoard extreme = new EightPuzzleBoard(new int[]
// {0,8,7,6,5,4,3,2,1});
EightPuzzleBoard board = new EightPuzzleBoard(new int[] { 7, 1, 8, 0, 4, 6, 2, 3, 5 });
IProblem <EightPuzzleBoard, IAction> problem = new BidirectionalEightPuzzleProblem(board);
QueueBasedSearch <EightPuzzleBoard, IAction> search = new GreedyBestFirstSearch <EightPuzzleBoard, IAction>
(new GraphSearchReducedFrontier <EightPuzzleBoard, IAction>(), EightPuzzleFunctions.createManhattanHeuristicFunction());
SearchAgent <EightPuzzleBoard, IAction> agent = new SearchAgent <EightPuzzleBoard, IAction>(problem, search);
Assert.AreEqual(49, agent.getActions().Size());
Assert.AreEqual("197", agent.getInstrumentation().getProperty("nodesExpanded"));
Assert.AreEqual("140", agent.getInstrumentation().getProperty("queueSize"));
Assert.AreEqual("141", agent.getInstrumentation().getProperty("maxQueueSize"));
}
static void nQueensWithBreadthFirstSearch()
{
try
{
System.Console.WriteLine("\nNQueensDemo BFS -->");
IProblem <NQueensBoard, QueenAction> problem =
NQueensFunctions.createIncrementalFormulationProblem(boardSize);
ISearchForActions <NQueensBoard, QueenAction>
search = new BreadthFirstSearch <NQueensBoard, QueenAction>(new TreeSearch <NQueensBoard, QueenAction>());
SearchAgent <NQueensBoard, QueenAction>
agent = new SearchAgent <NQueensBoard, QueenAction>(problem, search);
printActions(agent.getActions());
printInstrumentation(agent.getInstrumentation());
}
catch (Exception e)
{
throw e;
}
}
static void eightPuzzleSimulatedAnnealingDemo()
{
System.Console.WriteLine("\nEightPuzzleDemo Simulated Annealing Search -->");
try
{
IProblem <EightPuzzleBoard, IAction> problem = new BidirectionalEightPuzzleProblem(random1);
SimulatedAnnealingSearch <EightPuzzleBoard, IAction>
search = new SimulatedAnnealingSearch <EightPuzzleBoard, IAction>(
EightPuzzleFunctions.createManhattanHeuristicFunction());
SearchAgent <EightPuzzleBoard, IAction> agent = new SearchAgent <EightPuzzleBoard, IAction>(problem, search);
printActions(agent.getActions());
System.Console.WriteLine("Search Outcome=" + search.getOutcome());
System.Console.WriteLine("Final State=\n" + search.getLastSearchState());
printInstrumentation(agent.getInstrumentation());
}
catch (Exception e)
{
throw e;
}
}
private List<Vector2> GetRouteTo(Vector3 target)
{
List<Vector2> ret = new List<Vector2>();
//Create the problem and the agent
Problem prob = new Problem(heroList.First().Position, target, matrix, guiManagerInstance.GetComponent<GuiManager>());
SearchAgent agent = new SearchAgent(prob);
//Get the selected
Node goalNode = null;
switch (alg)
{
case AlgorithmsEnum.DFS:
goalNode = agent.DFGS();
break;
case AlgorithmsEnum.BFS:
goalNode = agent.BFGS();
break;
case AlgorithmsEnum.UCS:
goalNode = agent.UCGS();
break;
case AlgorithmsEnum.AStar:
goalNode = agent.AstarGS();
break;
default:
goalNode = agent.DFGS();
break;
}
var nodeRoute = goalNode.Path();
nodeRoute.Reverse();
foreach (var node in nodeRoute)
{
if (node.Action.HasValue)
{
ret.Add(node.Action.Value);
var pos = node.State.GetPosition();
guiManagerInstance.GetComponent<GuiManager>().MarkMoveCell(new Vector3(pos.x, pos.y));
}
}
return ret;
}
/**
* Returns a sequence of actions using A* Search.
*
* @param current
* the agent's current position
* @param goals
* a set of squares; try to plan a route to one of them
* @param allowed
* a set of squares that can form part of the route
*
* @return the best sequence of actions that the agent have to do to reach a
* goal from the current position.
*/
public ICollection <IAction> planRoute(AgentPosition current, ISet <Room> goals, ISet <Room> allowed)
{
// Every square represent 4 possible positions for the agent, it could
// be in different orientations. For every square in allowed and goals
// sets we add 4 squares.
ISet <AgentPosition> allowedPositions = CollectionFactory.CreateSet <AgentPosition>();
foreach (Room allowedRoom in allowed)
{
int x = allowedRoom.getX();
int y = allowedRoom.getY();
allowedPositions.Add(new AgentPosition(x, y, AgentPosition.Orientation.FACING_WEST));
allowedPositions.Add(new AgentPosition(x, y, AgentPosition.Orientation.FACING_EAST));
allowedPositions.Add(new AgentPosition(x, y, AgentPosition.Orientation.FACING_NORTH));
allowedPositions.Add(new AgentPosition(x, y, AgentPosition.Orientation.FACING_SOUTH));
}
ISet <AgentPosition> goalPositions = CollectionFactory.CreateSet <AgentPosition>();
foreach (Room goalRoom in goals)
{
int x = goalRoom.getX();
int y = goalRoom.getY();
goalPositions.Add(new AgentPosition(x, y, AgentPosition.Orientation.FACING_WEST));
goalPositions.Add(new AgentPosition(x, y, AgentPosition.Orientation.FACING_EAST));
goalPositions.Add(new AgentPosition(x, y, AgentPosition.Orientation.FACING_NORTH));
goalPositions.Add(new AgentPosition(x, y, AgentPosition.Orientation.FACING_SOUTH));
}
WumpusCave cave = new WumpusCave(kb.getCaveXDimension(), kb.getCaveYDimension(), allowedPositions);
GoalTest <AgentPosition> goalTest = goalPositions.Contains;
IProblem <AgentPosition, IAction> problem = new GeneralProblem <AgentPosition, IAction>(current,
WumpusFunctionFunctions.createActionsFunction(cave),
WumpusFunctionFunctions.createResultFunction(), goalTest);
IToDoubleFunction <Node <AgentPosition, IAction> > h = new ManhattanHeuristicFunction(goals);
ISearchForActions <AgentPosition, IAction> search = new AStarSearch <AgentPosition, IAction>(
new GraphSearch <AgentPosition, IAction>(), h);
SearchAgent <AgentPosition, IAction> agent;
ICollection <IAction> actions = null;
try
{
agent = new SearchAgent <AgentPosition, IAction>(problem, search);
actions = agent.getActions();
// Search agent can return a NoOp if already at goal,
// in the context of this agent we will just return
// no actions.
if (actions.Size() == 1 && actions.Get(0).IsNoOp())
{
actions = CollectionFactory.CreateQueue <IAction>();
}
}
catch (Exception e)
{
throw e;
}
return(actions);
}
