| 
28 | 28 |    "source": [  | 
29 | 29 |     "## Review\n",  | 
30 | 30 |     "\n",  | 
31 |  | -    "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 precicly defining **problems** and their **solutions**. We will look at several general-purpose search algorithms. Broadly, search algorithms are classified into two types:\n",  | 
 | 31 | +    "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",  | 
32 | 32 |     "\n",  | 
33 |  | -    "* **Uninformed search algorithms**: Search algorithms which explores the search space without having any information aboout the problem other than its definition.\n",  | 
 | 33 | +    "* **Uninformed search algorithms**: Search algorithms which explores the search space without having any information about the problem other than its definition.\n",  | 
34 | 34 |     "* Examples:\n",  | 
35 | 35 |     "    1. Breadth First Search\n",  | 
36 | 36 |     "    2. Depth First Search\n",  | 
 | 
96 | 96 |    "cell_type": "markdown",  | 
97 | 97 |    "metadata": {},  | 
98 | 98 |    "source": [  | 
99 |  | -    "We will use the abstract class `Problem` to define out real **problem** named `GraphProblem`. You can see how we defing `GraphProblem` by running the next cell."  | 
 | 99 | +    "We will use the abstract class `Problem` to define our real **problem** named `GraphProblem`. You can see how we defing `GraphProblem` by running the next cell."  | 
100 | 100 |    ]  | 
101 | 101 |   },  | 
102 | 102 |   {  | 
 | 
156 | 156 |     "collapsed": true  | 
157 | 157 |    },  | 
158 | 158 |    "source": [  | 
159 |  | -    "It is pretty straight forward to understand this `romania_map`. The first node **Arad** has three neighbours named **Zerind**, **Sibiu**, **Timisoara**. Each of these nodes are 75, 140, 118 units apart from **Arad** respectively. And the same goes with other nodes.\n",  | 
 | 159 | +    "It is pretty straightforward to understand this `romania_map`. The first node **Arad** has three neighbours named **Zerind**, **Sibiu**, **Timisoara**. Each of these nodes are 75, 140, 118 units apart from **Arad** respectively. And the same goes with other nodes.\n",  | 
160 | 160 |     "\n",  | 
161 | 161 |     "And `romania_map.locations` contains the positions of each of the nodes. We will use the straight line distance (which is different from the one provided in `romania_map`) between two cities in algorithms like A\\*-search and Recursive Best First Search.\n",  | 
162 | 162 |     "\n",  | 
 | 
392 | 392 |     "* Currently exploring node - <font color='red'>red</font>\n",  | 
393 | 393 |     "* Already explored nodes - <font color='gray'>gray</font>\n",  | 
394 | 394 |     "\n",  | 
395 |  | -    "Now, we will define some helper methods to display interactive buttons ans sliders when visualising search algorithms."  | 
 | 395 | +    "Now, we will define some helper methods to display interactive buttons and sliders when visualising search algorithms."  | 
396 | 396 |    ]  | 
397 | 397 |   },  | 
398 | 398 |   {  | 
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