As DKNguyen mentioned, this mathpages site covers it. This mathpages site also covers the topic.
In the above links, you can find this picture:

So you can just pick out the right answer from there.
Voltage Spike also points out that you can set this up as a grid of PDEs. And doing so allows you to easily solve problems that seek to find the resistance between different placements of the probes, too. (In fact, the entire idea is very powerful and can be used to solve a wide array of problems from heat flow to antennas to charge distribution over complex structures.)
There are a number of numerical approximation techniques for this kind of problem. These include Jacobi and Gauss-Seidel. But another technique found in the literature is called the red-black successive over-relaxation method. I just call it the checkerboard method because that's what it looks like.
Let's say you set up a square checkerboard matrix like this:

But with a much larger grid than that if you want better precision. The INF around the perimeter just means I won't allow the solver to work those squares. Nothing more.
Now you can set up the PDEs. Based upon KCL, it's just \$V_{i,j}=\frac14\left(V_{i-1,j}+V_{i+1,j}+V_{i,j-1}+V_{i,j+1}\right)\$
You can look at this link for a run. The results are \$0.773001\$ when the exact answer is \$\frac4{\pi}-\frac12\approx 0.77324\$. Which is close enough.
You can stack checkerboards to solve 3D problems. Or turn any arbitrary, constrained shape (like a cup or some crazy-looking antenna) and all this still works fine if a sufficiently fine grain grid is chosen. And you can choose to tessellate using something other than squares, too, if that helps simplify the PDE or otherwise tickle your fancy.
And because of how this alternates between red and black square processing, it lends well to a high degree of trivial parallelization. Even 'transputer-like' distributed memory MIMD fits well. F# would also be a good language fit for this.