Fix typo in N Queens Problem Part I

articles/2015-02-20-Overview-of-Common-AI-Algorithm-and-Their-Application-to-the-N-Queens-Problem-Part-I.md

@@ -17,7 +17,7 @@ Today I'll like us to work on some classic, general-purpose and well-known Artif
 
 You may know them, you may have used them, you might have heard of them, and very often, that'll be just that: _you have heard of them_. And I was the same: I _knew_ all of them, I knew the basic idea behind them (which is fine, for most things, it is important to have a basic understanding, it makes it all the more easier when you decide to learn it in a bit more thorough way) but I had never **played/worked with them**.  
 And this is what we are going to do here today!  
-Sounds coold, doesn't it?
+Sounds cool, doesn't it?
 
 For each of these algorithms, we will understand what the main idea about them is. Then we will try to pursue a little further by trying to pinpoint the specifics: enumerate the steps it is composed of, their order and their importance.  
 Because we understand better that way we will then code an actual implementation, in Haskell (as always, you know I love it, can't help it!), so that we'll be able to **actually** play with them, and boost our understanding.  
@@ -28,10 +28,10 @@ What happens is that based on dozens of articles, books and papers on A.I. that
 I will be **delighted** to hear comments (good and bad) about my approach, my implementation or my explanations!
 
 ### Why The Hell Do I Do That?
-Well, as I have just mentionned, I wanted to give anybody the opportunity to _easily_ experiment with A.I. and in particular with these algorithms. You have to know that _there is nothing new here_ for the A.I. field. I haven't invented these algorithms: they are all well-known by any A.I. student. But this is it: _by the A.I. students_.  
+Well, as I have just mentioned, I wanted to give anybody the opportunity to _easily_ experiment with A.I. and in particular with these algorithms. You have to know that _there is nothing new here_ for the A.I. field. I haven't invented these algorithms: they are all well-known by any A.I. student. But this is it: _by the A.I. students_.  
 It is amazing what a little A.I. might do in almost any application, and I just want everybody to know that some low-level A.I. is achievable, not boring and accessible.
 
-I'd like for you to think about this series of articles as a go-to ressource for when you are decided to take on an A.I. project and you feel like getting an introduction or a quick refresher course.
+I'd like for you to think about this series of articles as a go-to resource for when you are decided to take on an A.I. project and you feel like getting an introduction or a quick refresher course.
 
 ### Where Doest That Come From?
 
@@ -68,7 +68,7 @@ Now that was just the idea, we will have plenty of time to cover that more thoro
 
 How will our algorithms, or agents actually _make decisions_? One of the ways is to use a _cost function_. Quickly said, a cost function gives a price, or a cost: an estimation of _how bad_ a chosen path is.  
 Okay that was not so clear after all. Let me say it again: a cost function will tell you how much it will cost you to go down the path you're currently examining. The goal is, obviously to minimize the overall cost. (Now, if you think it's weird to reason in terms of "how bad" your solution will be, you can make the cost function become an _evaluation function_ which will tell you how good your solution is; the goal would be to pick the highest. Actually make yourselv familiar with that as I will endlessly switch between cost functions and objective functions, so sometimes we will try to minimize a function, sometimes maximize it. But we are always talking about the same thing. Be ready for that).  
-To make an analogy (again and ever: analogies are the keys to understanding concepts), imagine we are building a GPS application, you want to go from town A to town B. There are several ways. You cost function could be the distance, if your goal is to find the shortest path. It might be the time, if your goal is to minimize the time you spend on the road. Or it might be a combinaison of both: `cost(dist,time) = a * dist + b * time`, adjusting the coefficients. And a great many things depend on finding the _good_ cost function.
+To make an analogy (again and ever: analogies are the keys to understanding concepts), imagine we are building a GPS application, you want to go from town A to town B. There are several ways. You cost function could be the distance, if your goal is to find the shortest path. It might be the time, if your goal is to minimize the time you spend on the road. Or it might be a combination of both: `cost(dist,time) = a * dist + b * time`, adjusting the coefficients. And a great many things depend on finding the _good_ cost function.
 
 Okay, boring part over, we-are-ready!
 
@@ -92,7 +92,7 @@ Before I answer this, let me say something:
 
 > When you are wondering what something means or implies in Artificial Intelligence (for your Agent); just ask what it would mean **for you**.
 
-Even though Artificial Intelligence is not necessarily trying to mimick the human mind and the human intelligence, it still provides a strong reference point.  
+Even though Artificial Intelligence is not necessarily trying to mimic the human mind and the human intelligence, it still provides a strong reference point.  
 Okay so, what would "additional information" mean for us compared to the problem definition? Otherwise, what is likely to _help_ you with respect to the problem?
 
 Imagine your problem is the one we talked about before: going from town A to B, minimizing, say the time. Bham, that's the definition of the problem. Good luck with that.