Algorithm to find solution to puzzle

I am trying to make a game where a player has to find his way from Start to End on the Game Board.

As you see this Game Board contains a bunch of red circular obstacles. To win the game the player has to remove a minimum amount of obstacles. So my question is, how do I programmatically find out the minimum amount of obstacles to remove, to free a path? A free path would be considered the space between, circles not overlapping and not touching.

So what I really need is the minimum amount of circles to remove, I don't need the actual path. Is there an easy way to do this?

And to supplement understanding of this game board, 开发者_如何学Cthe circles each have the same radius, and it is restricted by the black lines.

Also, it is not necessary to move in a straight line.

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It is a graph theory maximum flow problem.

Suppose that every circle is a node in the graph. Additionally introduce 2 special nodes: TOP and BOTTOM. Connect circles with these nodes if they intersect with TOP/BOTTOM side. Connect nodes corresponding to circles with each other if the circles intersect.

Now you need to find a minimum cut in this graph, having TOP as source and BOTTOM as sink or vice versa. You can use Max-flow_min-cut_theorem to solve it. What it states is that Minimum-cut problem is equivallent to Max-flow problem. You can find details on how to solve Max-Flow problem on TopCoder.

As we can go through each node only once, we should convert the nodes into a directed edge of capacity one with in-node and out-node for each circle. The max-flow algorithm will solve the problem on the resulting graph and take into account the fact that we are removing circles rather than connections between circles. It is always a better decision for this problem to remove a node in a graph rather than edge, as we can always remove any edge by removing a node. Removing a node additionally can result in removing more than one edge.

By the way, a similar problem can be found on Uva Online Judge. It a good idea to try solve this task on the judge, then you will be sure that your solution is correct.

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In trying to visualize what Leonid wrote, I made the following diagram.

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For a graph translation, something like this might work.

Make a wall(the blue lines) between two circles if they overlap. Don't forget to add in the top and bottom border as well. This creates a couple of regions. These will be all the nodes of the graph.

Next we have to find the edges, the cost of going from one node to another. Take two neighbour regions (sharing a wall.) Then by brute force, or what ever clever method you come up with, determine the cost of moving from this region to the other. If you remove a circle, that means, you remove all the walls that go to that circle. If this makes the two regions into one region, the cost of the edge is 1. If you need to remove two circles(all the walls they have) to combine the two regions, the cost is 2. Etc.

Some of the edges(green) are drawn. We have to go from the start region, to the end region. You now got a everyday weighted graph.

I think this can be improved a lot, but I leave that as an exercise =)

In this case minimum is 3.

Warning, picture is drawn by hand, I did forget a few walls, edges and regions. For illustration purposes only.

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Alright so I decided to make a visualization of this in pygame.

It turned out to be a lot more difficult than I expected.

The idea as in other suggestions is to use Max Flow. The bottleneck in the flow from source to sink is where the flow is most dense. If we cut the graph in half at this dense bottle neck, (i.e. at the min-cut) then we have the minimum number of circles. It so happens the maxflow = min-cut.

here are the steps I took:

1. Create pygame world were I can randomly generate circles

2. Make function to work out all collisions between circles:

This involved sorting the circles by x coordinate. Now to find all collisions of Circle[0] I keep moving along the array testing for collisions until I find a circle who's x value is more than 2*radius greater than circle[0]'s x value, then I can pop circle[0] and repeat the process..

3. Create source and sink nodes, work out which circles they need to connect to.

Steps 4-5 are carried out in the "findflow() function"

4. Create basic undirected networkX graph representing circles with nodes. Connecting nodes only if their corresponding circles collide.

5. And this is where it starts getting hard.. I create a new directed graph from my undirected graph. Because I needed to work out flow through circles (i.e. nodes) not edges, I need to split each node into two nodes with an edge between.

   Lets say I have node X connected to node Y (Y<->X) (in the original graph).

   I change X to Xa and Xb so that Xa connects to Xb (Xa->Xb) Also Y changes to (Ya->Yb).

   I also need to add (Yb->Xa) and (Xb->Ya) to represent the original connection between X and Y.

All edges in the undirected graph are given capacity=1 (e.g. you can only cross a circle once)

6. I now apply networkx.ford_fulkerson() algorithm on my new directed graph. This finds me the flowValue and the flowGraph.

the flowValue is an integer. It is the min-cut (or max flow from source to sink). This is the answer we have been looking for. It represents the minimum number of circles we are required to remove.

Bonus Assignment:

I thought.. why stop here, having the number of circles to remove is nice, but I want to know the exact circles I need to remove. To do this I need to find out where the min-cut actually occurs on the flowGraph. I managed to figure out how to do this, however my implementation has a bug somewhere, so it sometimes picks the cut slightly wrong and so gets the wrong circles.

To find the min-cut we will use the flowGraph produced in step6. The idea is that the bottleneck of this graph will be the min-cut. if we try flow from source to sink, we will get stuck at the bottle neck as all edges around the bottleneck will be at maximum capacity. So we simply use DFS (Depth first search) to flow down as far as we can. The DFS is only allowed to move along edges that aren't at maximum capacity in the flow graph. (e.g. their flow is 0 not 1). Using DFS from the source I kept note of all nodes I could see storing them in self.seen. Now after DFS, for all the nodes in seen, i check if the node has a maximum capacity edge to a node that wasn't seen in DFS. All such nodes are on the min-cut.

Here is a picture of one of the simulations I ran:

and with the circles removed, I filled it with paint (you may have to zoom in a bit to see that there is indeed a gap between the circles):

Learnings:

Speed is ok even in python, runs for 1000 circles ok.

It was harder than I thought, and I still have a bug in trying to use the DFS to find the original circles. (If anyone can help find the bug that would be great).

The code was elegant at first, although I kept adding hacks to change the visualization etc.

working code (apart from slight occasional bug in DFS):

__author__ = 'Robert'
import pygame
import networkx

class CirclesThing():
    def __init__(self,width,height,number_of_circles):
        self.removecircles = False #display removable circles as green.
        self.width = width
        self.height = height

        self.number_of_circles = number_of_circles
        self.radius = 40

        from random import randint
        self.circles = sorted(set((randint(self.radius,width-self.radius),randint(2*self.radius,height-2*self.radius)) for i in range(self.number_of_circles)))

        self.sink = (self.width/2, self.height-10)
        self.source = (self.width/2, 10)

        self.flowValue,self.flowGraph = self.find_flow()

        self.seen = set()
        self.seen.add(self.source)
        self.dfs(self.flowGraph,self.source)

        self.removable_circles = set()
        for node1 in self.flowGraph:
            if node1 not in self.seen or node1==self.source:
                continue
            for node2 in self.flowGraph[node1]:
                if self.flowGraph[node1][node2]==1:
                    if node2 not in self.seen:
                        self.removable_circles.add(node1[0])


    def find_flow(self):
        "finds the max flow from source to sink and returns the amount, along with the flow graph"
        G = networkx.Graph()
        for node1,node2 in self.get_connections_to_source_sink()+self.intersect_circles():
            G.add_edge(node1,node2,capacity=1)

        G2 = networkx.DiGraph()
        for node in G:
            if node not in (self.source,self.sink):
                G2.add_edge((node,'a'),(node,'b'),capacity=1) #each node is split into two new nodes. We add the edge between the two new nodes flowing from a to b.

        for edge in G.edges_iter():
            if self.source in edge or self.sink in edge:
                continue #add these edges later
            node1,node2 = edge
            G2.add_edge((node1,'b'),(node2,'a'),capacity=1) #if we flow through a circle (from node1a to node1b) we need to be able to flow from node1b to all node1's children
            G2.add_edge((node2,'b'),(node1,'a'),capactiy=1) #similarly for node2..

        for node in G[self.source]:
            G2.add_edge(self.source,(node,'a'))
        for node in G[self.sink]:
            G2.add_edge((node,'b'),self.sink)

        flowValue, flowGraph = networkx.ford_fulkerson(G2,self.source,self.sink)

        return flowValue, flowGraph


    def dfs(self,g,v):
        "depth first search from source of flowGraph. Don't explore any nodes that are at maximum capacity. (this means we can't explore past the min cut!)"
        for node in g[v]:
            if node not in self.seen:
                self.seen.add(node)
                if g[v][node]!=1 or v==self.source:
                    self.dfs(g,node)

    def display(self):
        self.draw_circles()
        self.draw_circles(circle_radius=5, circle_colour=(255,0,0))
        if not self.removecircles:
            lines = self.intersect_circles()
            self.draw_lines(lines)
        self.draw_source_sink()

    def draw_circles(self,circle_radius=None,circle_colour=(0,0,255),circles=None):
        if circle_radius is None:
            circle_radius = self.radius
        if circles is None:
            circles = self.circles

        circle_thickness = 2
        for pos in circles:
            cc = circle_colour if pos not in self.removable_circles else (100,200,0) #change colour of removable circles
            ct = circle_thickness if pos not in self.removable_circles else 4 #thicken removable circles
            if pos not in self.removable_circles or not self.removecircles:
                pygame.draw.circle(screen, cc, pos, circle_radius, ct)

    def intersect_circles(self):
        colliding_circles = []
        for i in range(len(self.circles)-1):
            for j in range(i+1,len(self.circles)):
                x1,y1 = self.circles[i]
                x2,y2 = self.circles[j]
                if x2-x1>2*self.radius+5: #add 5 to make a more obvious gap visually
                    break #can't collide anymore.
                if (x2-x1)**2 + (y2-y1)**2 <= (2*self.radius)**2+5:
                    colliding_circles.append(((x1,y1),(x2,y2)))
        return colliding_circles

    def draw_lines(self,lines,line_colour=(255, 0, 0)):
        for point_pair in lines:
            point1,point2 = point_pair
            try:
                tot = self.flowGraph[(point1,'b')][(point2,'a')] + self.flowGraph[(point2,'b')][(point1,'a')] #hack, does anything flow between the two circles?
            except KeyError:
                tot = 0
            thickness = 1 if tot==0 else 3
            lc = line_colour if tot==0 else (0,90,90)
            pygame.draw.line(screen, lc, point1, point2, thickness)

    def draw_source_sink(self):
        self.draw_circles(circles=(self.sink,self.source),circle_radius=15,circle_colour=(0,255,0))

        bottom_line = ((0,self.height-3*self.radius),(self.width,self.height-3*self.radius))
        top_line = ((0,3*self.radius),(self.width,3*self.radius))

        self.draw_lines([top_line, bottom_line],line_colour=(60,60,60))

        if not self.removecircles:
            self.draw_lines(self.get_connections_to_source_sink(),line_colour=(0,255,0))

    def get_connections_to_source_sink(self):
        connections = []
        for x,y in self.circles:
            if y<4*self.radius:
                connections.append((self.source,(x,y)))
            elif y>height-4*self.radius:
                connections.append((self.sink,(x,y)))
        return connections

    def get_caption(self):
        return "flow %s, circles removes %s" %(self.flowValue,len(self.removable_circles))



time_per_simulation = 5 #5 seconds
width, height = 1400, 600
background_colour = (255,255,255)
screen = pygame.display.set_mode((width, height))

screen.fill(background_colour)
from pygame.locals import USEREVENT
pygame.time.set_timer(USEREVENT+1,time_per_simulation*1000)

simulations = 0
simulations_max = 20

running = True
while running:
    for event in pygame.event.get():
        if event.type == pygame.QUIT:
            running = False
        if event.type == USEREVENT+1:
            if simulations % 2 ==0:
                world = CirclesThing(width,height,120) #new world
            else:
                world.removecircles = True #current world without green circles

            screen.fill(background_colour)
            world.display()
            pygame.display.set_caption(world.get_caption())
            pygame.display.flip()

            if simulations>=2*simulations_max:
                running = False
            simulations+=1

            if False:
                pygame.image.save(screen,'sim%s.bmp'%simulations)

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One option is to first remove those circles with the most numbers of overlap or touches. After each one you remove, check if its a solution, if not continue removing.

var circle;
circle = findMostOverlapCircle();
while(circle != null) {
    circle.remove();
    circle = findMostOverlapCircle();
}