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Merged
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Mar 14, 2018
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Rewrote parts of search.ipynb #809

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961feba
Rewrote parts of search.ipynb
bakerwho Mar 6, 2018
999d253
Fixed typo and cleared cell output
bakerwho Mar 6, 2018
e540ff4
Merge branch 'master' into search-changes
bakerwho Mar 13, 2018
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3 changes: 2 additions & 1 deletion search-4e.ipynb
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Expand Up @@ -1929,6 +1929,7 @@
"execution_count": 52,
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.1"
},
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"state": {},
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48 changes: 28 additions & 20 deletions search.ipynb
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Expand Up @@ -54,22 +54,24 @@
"source": [
"## OVERVIEW\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 a specific kind of problem solving - building goal-based agents that can plan ahead to solve problems. In particular, we examine navigation problem/route finding problem. We must begin by precisely defining **problems** and their **solutions**. We will look at several general-purpose search algorithms.\n",
"\n",
"Search algorithms can be classified into two types:\n",
"\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",
" * 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 (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",
" * 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 the route-finding problem defined on Romania map at the end of this notebook.*"
]
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"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 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",
"* `__init__(self, initial, goal)` : This is what is called a `constructor`. It is the first method called when you create an instance of the class as `Problem(initial, goal)`. The variable `initial` specifies the initial state $s_0$ of the search problem. It represents the beginning state. From here, 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",
Expand All @@ -133,7 +135,7 @@
"* `result(self, state, action)` : This returns the resulting state if action `action` is taken in the state `state`. This `Problem` class only deals with deterministic outcomes. So we know for sure what every action in a state would result to.\n",
"\n",
"\n",
"* `goal_test(self, state)` : Given a graph state, it checks if it is a terminal state. If the state is indeed a goal state, value of `True` is returned. Else, of course, `False` is returned.\n",
"* `goal_test(self, state)` : Return a boolean for a given state - `True` if it is a goal state, else `False`.\n",
"\n",
"\n",
"* `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",
Expand Down Expand Up @@ -164,13 +166,11 @@
"cell_type": "markdown",
"metadata": {},
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"The `Node` class has nine methods.\n",
"The `Node` class has nine methods. The first is the `__init__` method.\n",
"\n",
"* `__init__(self, state, parent, action, path_cost)` : This method creates a node. `parent` represents the node that this is a successor of and `action` is the action required to get from the parent node to this node. `path_cost` is the cost to reach current node from parent node.\n",
"\n",
"* `__repr__(self)` : This returns the state of this node.\n",
"\n",
"* `__lt__(self, node)` : Given a `node`, this method returns `True` if the state of current node is less than the state of the `node`. Otherwise it returns `False`.\n",
"The next 4 methods are specific `Node`-related functions.\n",
"\n",
"* `expand(self, problem)` : This method lists all the neighbouring(reachable in one step) nodes of current node. \n",
"\n",
Expand All @@ -180,6 +180,12 @@
"\n",
"* `path(self)` : This returns a list of all the nodes that lies in the path from the root to this node.\n",
"\n",
"The remaining 4 methods override standards Python functionality for representing an object as a string, the less-than ($<$) operator, the equal-to ($=$) operator, and the `hash` function.\n",
"\n",
"* `__repr__(self)` : This returns the state of this node.\n",
"\n",
"* `__lt__(self, node)` : Given a `node`, this method returns `True` if the state of current node is less than the state of the `node`. Otherwise it returns `False`.\n",
"\n",
"* `__eq__(self, other)` : This method returns `True` if the state of current node is equal to the other node. Else it returns `False`.\n",
"\n",
"* `__hash__(self)` : This returns the hash of the state of current node."
Expand All @@ -205,7 +211,7 @@
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"Now it's time to define our problem. We will define it by passing `initial`, `goal`, `graph` to `GraphProblem`. So, our problem is to find the goal state starting from the given initial state on the provided graph. Have a look at our romania_map, which is an Undirected Graph containing a dict of nodes as keys and neighbours as values."
"Have a look at our romania_map, which is an Undirected Graph containing a dict of nodes as keys and neighbours as values."
]
},
{
Expand Down Expand Up @@ -252,7 +258,9 @@
"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",
"\n",
"**Define a problem:**\n",
"Hmm... say we want to start exploring from **Arad** and try to find **Bucharest** in our romania_map. So, this is how we do it."
"Now it's time to define our problem. We will define it by passing `initial`, `goal`, `graph` to `GraphProblem`. So, our problem is to find the goal state starting from the given initial state on the provided graph. \n",
"\n",
"Say we want to start exploring from **Arad** and try to find **Bucharest** in our romania_map. So, this is how we do it."
]
},
{
Expand Down Expand Up @@ -377,7 +385,7 @@
"source": [
"The SimpleProblemSolvingAgentProgram class has six methods: \n",
"\n",
"* `__init__(self, intial_state=None)`: This is the `contructor` of the class and is the first method to be called when the class is instantiated. It takes in a keyword argument, `initial_state` which is initially `None`. The argument `intial_state` represents the state from which the agent starts.\n",
"* `__init__(self, intial_state=None)`: This is the `contructor` of the class and is the first method to be called when the class is instantiated. It takes in a keyword argument, `initial_state` which is initially `None`. The argument `initial_state` represents the state from which the agent starts.\n",
"\n",
"* `__call__(self, percept)`: This method updates the `state` of the agent based on its `percept` using the `update_state` method. It then formulates a `goal` with the help of `formulate_goal` method and a `problem` using the `formulate_problem` method and returns a sequence of actions to solve it (using the `search` method).\n",
"\n",
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