aimacode · norvig · Mar 18, 2017 · Mar 15, 2017
diff --git a/search.ipynb b/search.ipynb
@@ -28,24 +28,24 @@
    "source": [
     "## Review\n",
     "\n",
-    "Here, we learn about problem solving. Building goal-based agents that can plan ahead to solve problems, in particular navigation problem / route finding problem. First, we will start the problem solving by precisely defining **problems** and their **solutions**. We will look at several general-purpose search algorithms. Broadly, search algorithms are classified into two types:\n",
+    "Here, we learn about problem solving. Building goal-based agents that can plan ahead to solve problems, in particular, navigation problem/route finding problem. First, we will start the problem solving by precisely defining **problems** and their **solutions**. We will look at several general-purpose search algorithms. Broadly, search algorithms are classified into two types:\n",
     "\n",
-    "* **Uninformed search algorithms**: Search algorithms which explores the search space without having any information about the problem other than its definition.\n",
+    "* **Uninformed search algorithms**: Search algorithms which explore the search space without having any information about the problem other than its definition.\n",
     "* Examples:\n",
     "    1. Breadth First Search\n",
     "    2. Depth First Search\n",
     "    3. Depth Limited Search\n",
     "    4. Iterative Deepening Search\n",
     "\n",
     "\n",
-    "* **Informed search algorithms**: These type of algorithms leverage any information (hueristics, path cost) on the problem to search through the search space to find the solution efficiently.\n",
+    "* **Informed search algorithms**: These type of algorithms leverage any information (heuristics, path cost) on the problem to search through the search space to find the solution efficiently.\n",
     "* Examples:\n",
     "    1. Best First Search\n",
     "    2. Uniform Cost Search\n",
     "    3. A\\* Search\n",
     "    4. Recursive Best First Search\n",
     "\n",
-    "*Don't miss the visualisations of these algorithms solving route-finding problem defined on romania map at the end of this notebook.*"
+    "*Don't miss the visualisations of these algorithms solving the route-finding problem defined on Romania map at the end of this notebook.*"
    ]
   },
   {
@@ -74,7 +74,7 @@
    "source": [
     "The `Problem` class has six methods.\n",
     "\n",
-    "* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of class. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins its task of exploration to find the goal state(s) which is given in the `goal` parameter.\n",
+    "* `__init__(self, initial, goal)` : This is what is called a `constructor` and is the first method called when you create an instance of the class. `initial` specifies the initial state of our search problem. It represents the start state from where our agent begins its task of exploration to find the goal state(s) which is given in the `goal` parameter.\n",
     "\n",
     "\n",
     "* `actions(self, state)` : This method returns all the possible actions agent can execute in the given state `state`.\n",
@@ -89,7 +89,7 @@
     "* `path_cost(self, c, state1, action, state2)` : Return the cost of the path that arrives at `state2` as a result of taking `action` from `state1`, assuming total cost of `c` to get up to `state1`.\n",
     "\n",
     "\n",
-    "* `value(self, state)` : This acts as a bit of extra information in problems where we try to optimize a value when we cannot do a goal test."
+    "* `value(self, state)` : This acts as a bit of extra information in problems where we try to optimise a value when we cannot do a goal test."
    ]
   },
   {
@@ -215,7 +215,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's start the visualisations by importing necessary modules. We use networkx and matplotlib to show the map in notebook and we use ipywidgets to interact with the map to see how the searching algorithm works."
+    "Let's start the visualisations by importing necessary modules. We use networkx and matplotlib to show the map in the notebook and we use ipywidgets to interact with the map to see how the searching algorithm works."
    ]
   },
   {
@@ -574,7 +574,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we use ipywidgets to display a slider, a button and our romania map. By sliding the slider we can have a look at all the intermediate steps of a particular search algorithm. By pressing the button **Visualize**, you can see all the steps without interacting with the slider. These two helper functions are the callback function which are called when we interact with slider and the button.\n",
+    "Now, we use ipywidgets to display a slider, a button and our romania map. By sliding the slider we can have a look at all the intermediate steps of a particular search algorithm. By pressing the button **Visualize**, you can see all the steps without interacting with the slider. These two helper functions are the callback functions which are called when we interact with the slider and the button.\n",
     "\n"
    ]
   },