Newer
Older
"**Have(x):** Indicates that we possess the item **'x'**."
]
},
{
"cell_type": "code",
"shoppingProblem = shopping_problem()"
]
},
{
"cell_type": "markdown",
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"Let's first check whether the goal state Have(Milk), Have(Banana), Have(Drill) is reached or not."
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"name": "stdout",
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"False\n"
]
}
],
"source": [
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the possible actions\n",
"**Buy(x, store):** Buy an item **'x'** from a **'store'** given that the **'store'** sells **'x'**.\n",
"**Go(x, y):** Go to destination **'y'** starting from source **'x'**."
"We now define a valid solution that will help us reach the goal.\n",
"The sequence of actions will then be carried out onto the `shoppingProblem` PlanningProblem."
]
},
{
"cell_type": "code",
"solution = [expr('Go(Home, SM)'),\n",
" expr('Buy(Milk, SM)'),\n",
" expr('Buy(Banana, SM)'),\n",
" expr('Go(SM, HW)'),\n",
" expr('Buy(Drill, HW)')]\n",
"\n",
"for action in solution:\n",
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have taken the steps required to acquire all the stuff we need. \n",
"Let's see if we have reached our goal."
]
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"metadata": {},
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"data": {
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"True"
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"It has now successfully achieved the goal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
]
},
{
"cell_type": "markdown",
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"This is a simple problem of putting on a pair of socks and shoes.\n",
"The problem is defined in the module as given below."
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"outputs": [
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"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">socks_and_shoes</span><span class=\"p\">():</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\"> SOCKS-AND-SHOES-PROBLEM</span>\n",
"\n",
"<span class=\"sd\"> A task of wearing socks and shoes on both feet</span>\n",
"\n",
"<span class=\"sd\"> Example:</span>\n",
"<span class=\"sd\"> >>> from planning import *</span>\n",
"<span class=\"sd\"> >>> ss = socks_and_shoes()</span>\n",
"<span class=\"sd\"> >>> ss.goal_test()</span>\n",
"<span class=\"sd\"> False</span>\n",
"<span class=\"sd\"> >>> ss.act(expr('RightSock'))</span>\n",
"<span class=\"sd\"> >>> ss.act(expr('RightShoe'))</span>\n",
"<span class=\"sd\"> >>> ss.act(expr('LeftSock'))</span>\n",
"<span class=\"sd\"> >>> ss.goal_test()</span>\n",
"<span class=\"sd\"> False</span>\n",
"<span class=\"sd\"> >>> ss.act(expr('LeftShoe'))</span>\n",
"<span class=\"sd\"> >>> ss.goal_test()</span>\n",
"<span class=\"sd\"> True</span>\n",
"<span class=\"sd\"> >>></span>\n",
"<span class=\"sd\"> """</span>\n",
"\n",
" <span class=\"k\">return</span> <span class=\"n\">PlanningProblem</span><span class=\"p\">(</span><span class=\"n\">init</span><span class=\"o\">=</span><span class=\"s1\">''</span><span class=\"p\">,</span>\n",
" <span class=\"n\">goals</span><span class=\"o\">=</span><span class=\"s1\">'RightShoeOn & LeftShoeOn'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">actions</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"n\">Action</span><span class=\"p\">(</span><span class=\"s1\">'RightShoe'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">precond</span><span class=\"o\">=</span><span class=\"s1\">'RightSockOn'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">effect</span><span class=\"o\">=</span><span class=\"s1\">'RightShoeOn'</span><span class=\"p\">),</span>\n",
" <span class=\"n\">Action</span><span class=\"p\">(</span><span class=\"s1\">'RightSock'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">precond</span><span class=\"o\">=</span><span class=\"s1\">''</span><span class=\"p\">,</span>\n",
" <span class=\"n\">effect</span><span class=\"o\">=</span><span class=\"s1\">'RightSockOn'</span><span class=\"p\">),</span>\n",
" <span class=\"n\">Action</span><span class=\"p\">(</span><span class=\"s1\">'LeftShoe'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">precond</span><span class=\"o\">=</span><span class=\"s1\">'LeftSockOn'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">effect</span><span class=\"o\">=</span><span class=\"s1\">'LeftShoeOn'</span><span class=\"p\">),</span>\n",
" <span class=\"n\">Action</span><span class=\"p\">(</span><span class=\"s1\">'LeftSock'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">precond</span><span class=\"o\">=</span><span class=\"s1\">''</span><span class=\"p\">,</span>\n",
" <span class=\"n\">effect</span><span class=\"o\">=</span><span class=\"s1\">'LeftSockOn'</span><span class=\"p\">)])</span>\n",
"</pre></div>\n",
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{
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"**LeftSockOn:** Indicates that we have already put on the left sock.\n",
"\n",
"**RightSockOn:** Indicates that we have already put on the right sock.\n",
"\n",
"**LeftShoeOn:** Indicates that we have already put on the left shoe.\n",
"\n",
"**RightShoeOn:** Indicates that we have already put on the right shoe.\n"
]
},
{
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"source": [
"socksShoes = socks_and_shoes()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first check whether the goal state is reached or not."
]
},
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"cell_type": "code",
"metadata": {},
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{
"data": {
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"False"
]
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"socksShoes.goal_test()"
]
},
{
"cell_type": "markdown",
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"source": [
"As the goal state isn't reached, we will define a sequence of actions that might help us achieve the goal.\n",
"These actions will then be acted upon the `socksShoes` PlanningProblem to check if the goal state is reached."
]
},
{
"cell_type": "code",
"outputs": [],
"source": [
"solution = [expr('RightSock'),\n",
" expr('RightShoe'),\n",
" expr('LeftSock'),\n",
" expr('LeftShoe')]"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
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"metadata": {},
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}
],
"source": [
"for action in solution:\n",
" socksShoes.act(action)\n",
" \n",
"socksShoes.goal_test()"
]
},
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"cell_type": "markdown",
"metadata": {},
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"We have reached our goal."
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Cake Problem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This problem requires us to reach the state of having a cake and having eaten a cake simlutaneously, given a single cake.\n",
"Let's first take a look at the definition of the `have_cake_and_eat_cake_too` problem in the module."
]
},
{
"cell_type": "code",
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"<body>\n",
"<h2></h2>\n",
"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">have_cake_and_eat_cake_too</span><span class=\"p\">():</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\"> [Figure 10.7] CAKE-PROBLEM</span>\n",
"\n",
"<span class=\"sd\"> A problem where we begin with a cake and want to </span>\n",
"<span class=\"sd\"> reach the state of having a cake and having eaten a cake.</span>\n",
"<span class=\"sd\"> The possible actions include baking a cake and eating a cake.</span>\n",
"\n",
"<span class=\"sd\"> Example:</span>\n",
"<span class=\"sd\"> >>> from planning import *</span>\n",
"<span class=\"sd\"> >>> cp = have_cake_and_eat_cake_too()</span>\n",
"<span class=\"sd\"> >>> cp.goal_test()</span>\n",
"<span class=\"sd\"> False</span>\n",
"<span class=\"sd\"> >>> cp.act(expr('Eat(Cake)'))</span>\n",
"<span class=\"sd\"> >>> cp.goal_test()</span>\n",
"<span class=\"sd\"> False</span>\n",
"<span class=\"sd\"> >>> cp.act(expr('Bake(Cake)'))</span>\n",
"<span class=\"sd\"> >>> cp.goal_test()</span>\n",
"<span class=\"sd\"> True</span>\n",
"<span class=\"sd\"> >>></span>\n",
"<span class=\"sd\"> """</span>\n",
"\n",
" <span class=\"k\">return</span> <span class=\"n\">PlanningProblem</span><span class=\"p\">(</span><span class=\"n\">init</span><span class=\"o\">=</span><span class=\"s1\">'Have(Cake)'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">goals</span><span class=\"o\">=</span><span class=\"s1\">'Have(Cake) & Eaten(Cake)'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">actions</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"n\">Action</span><span class=\"p\">(</span><span class=\"s1\">'Eat(Cake)'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">precond</span><span class=\"o\">=</span><span class=\"s1\">'Have(Cake)'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">effect</span><span class=\"o\">=</span><span class=\"s1\">'Eaten(Cake) & ~Have(Cake)'</span><span class=\"p\">),</span>\n",
" <span class=\"n\">Action</span><span class=\"p\">(</span><span class=\"s1\">'Bake(Cake)'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">precond</span><span class=\"o\">=</span><span class=\"s1\">'~Have(Cake)'</span><span class=\"p\">,</span>\n",
" <span class=\"n\">effect</span><span class=\"o\">=</span><span class=\"s1\">'Have(Cake)'</span><span class=\"p\">)])</span>\n",
"</pre></div>\n",
"</body>\n",
"</html>\n"
],
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"<IPython.core.display.HTML object>"
]
},
"metadata": {},
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],
"source": [
"psource(have_cake_and_eat_cake_too)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since this problem doesn't involve variables, states can be considered similar to symbols in propositional logic.\n",
"\n",
"**Have(Cake):** Declares that we have a **'Cake'**.\n",
"\n",
"**~Have(Cake):** Declares that we _don't_ have a **'Cake'**."
]
},
{
"cell_type": "code",
"outputs": [],
"source": [
"cakeProblem = have_cake_and_eat_cake_too()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First let us check whether the goal state 'Have(Cake)' and 'Eaten(Cake)' are reached or not."
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(cakeProblem.goal_test())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us look at the possible actions.\n",
"\n",
"**Bake(x):** To bake **' x '**.\n",
"\n",
"**Eat(x):** To eat **' x '**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now define a valid solution that can help us reach the goal.\n",
"The sequence of actions will then be acted upon the `cakeProblem` PlanningProblem."
]
},
{
"cell_type": "code",
"outputs": [],
"source": [
"solution = [expr(\"Eat(Cake)\"),\n",
" expr(\"Bake(Cake)\")]\n",
"\n",
"for action in solution:\n",
" cakeProblem.act(action)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have made actions to bake the cake and eat the cake. Let us check if we have reached the goal."
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(cakeProblem.goal_test())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It has now successfully achieved its goal i.e, to have and eat the cake."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One might wonder if the order of the actions matters for this problem.\n",
"Let's see for ourselves."
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
"ename": "Exception",
"evalue": "Action 'Bake(Cake)' pre-conditions not satisfied",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-128-b340f831489f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0maction\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msolution\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mcakeProblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/aima-python/planning.py\u001b[0m in \u001b[0;36mact\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Action '{}' not found\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlist_action\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_precond\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Action '{}' pre-conditions not satisfied\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclauses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mException\u001b[0m: Action 'Bake(Cake)' pre-conditions not satisfied"
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]
}
],
"source": [
"cakeProblem = have_cake_and_eat_cake_too()\n",
"\n",
"solution = [expr('Bake(Cake)'),\n",
" expr('Eat(Cake)')]\n",
"\n",
"for action in solution:\n",
" cakeProblem.act(action)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It raises an exception.\n",
"Indeed, according to the problem, we cannot bake a cake if we already have one.\n",
"In planning terms, '~Have(Cake)' is a precondition to the action 'Bake(Cake)'.\n",
"Hence, this solution is invalid."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PLANNING IN THE REAL WORLD\n",
"---\n",
"## PROBLEM\n",
"The `Problem` class is a wrapper for `PlanningProblem` with some additional functionality and data-structures to handle real-world planning problems that involve time and resource constraints.\n",
"The `Problem` class includes everything that the `PlanningProblem` class includes.\n",
"Additionally, it also includes the following attributes essential to define a real-world planning problem:\n",
"- a list of `jobs` to be done\n",
"- a dictionary of `resources`\n",
"\n",
"It also overloads the `act` method to call the `do_action` method of the `HLA` class, \n",
"and also includes a new method `refinements` that finds refinements or primitive actions for high level actions.\n",
"`hierarchical_search` and `angelic_search` are also built into the `Problem` class to solve such planning problems."
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
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"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">class</span> <span class=\"nc\">Problem</span><span class=\"p\">(</span><span class=\"n\">PlanningProblem</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\"> Define real-world problems by aggregating resources as numerical quantities instead of</span>\n",
"<span class=\"sd\"> named entities.</span>\n",
"<span class=\"sd\"> This class is identical to PDLL, except that it overloads the act function to handle</span>\n",
"<span class=\"sd\"> resource and ordering conditions imposed by HLA as opposed to Action.</span>\n",
"<span class=\"sd\"> """</span>\n",
" <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">init</span><span class=\"p\">,</span> <span class=\"n\">goals</span><span class=\"p\">,</span> <span class=\"n\">actions</span><span class=\"p\">,</span> <span class=\"n\">jobs</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">resources</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">):</span>\n",
" <span class=\"nb\">super</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"n\">init</span><span class=\"p\">,</span> <span class=\"n\">goals</span><span class=\"p\">,</span> <span class=\"n\">actions</span><span class=\"p\">)</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">jobs</span> <span class=\"o\">=</span> <span class=\"n\">jobs</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">resources</span> <span class=\"o\">=</span> <span class=\"n\">resources</span> <span class=\"ow\">or</span> <span class=\"p\">{}</span>\n",
" <span class=\"k\">def</span> <span class=\"nf\">act</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">action</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\"> Performs the HLA given as argument.</span>\n",
"<span class=\"sd\"> Note that this is different from the superclass action - where the parameter was an</span>\n",
"<span class=\"sd\"> Expression. For real world problems, an Expr object isn't enough to capture all the</span>\n",
"<span class=\"sd\"> detail required for executing the action - resources, preconditions, etc need to be</span>\n",
"<span class=\"sd\"> checked for too.</span>\n",
"<span class=\"sd\"> """</span>\n",
" <span class=\"n\">args</span> <span class=\"o\">=</span> <span class=\"n\">action</span><span class=\"o\">.</span><span class=\"n\">args</span>\n",
" <span class=\"n\">list_action</span> <span class=\"o\">=</span> <span class=\"n\">first</span><span class=\"p\">(</span><span class=\"n\">a</span> <span class=\"k\">for</span> <span class=\"n\">a</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">actions</span> <span class=\"k\">if</span> <span class=\"n\">a</span><span class=\"o\">.</span><span class=\"n\">name</span> <span class=\"o\">==</span> <span class=\"n\">action</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">)</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">list_action</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span><span class=\"p\">:</span>\n",
" <span class=\"k\">raise</span> <span class=\"ne\">Exception</span><span class=\"p\">(</span><span class=\"s2\">"Action '{}' not found"</span><span class=\"o\">.</span><span class=\"n\">format</span><span class=\"p\">(</span><span class=\"n\">action</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">))</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">init</span> <span class=\"o\">=</span> <span class=\"n\">list_action</span><span class=\"o\">.</span><span class=\"n\">do_action</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">jobs</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">resources</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">init</span><span class=\"p\">,</span> <span class=\"n\">args</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">clauses</span>\n",
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" <span class=\"k\">def</span> <span class=\"nf\">refinements</span><span class=\"p\">(</span><span class=\"n\">hla</span><span class=\"p\">,</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">library</span><span class=\"p\">):</span> <span class=\"c1\"># refinements may be (multiple) HLA themselves ...</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\"> state is a Problem, containing the current state kb</span>\n",
"<span class=\"sd\"> library is a dictionary containing details for every possible refinement. eg:</span>\n",
"<span class=\"sd\"> {</span>\n",
"<span class=\"sd\"> 'HLA': [</span>\n",
"<span class=\"sd\"> 'Go(Home, SFO)',</span>\n",
"<span class=\"sd\"> 'Go(Home, SFO)',</span>\n",
"<span class=\"sd\"> 'Drive(Home, SFOLongTermParking)',</span>\n",
"<span class=\"sd\"> 'Shuttle(SFOLongTermParking, SFO)',</span>\n",
"<span class=\"sd\"> 'Taxi(Home, SFO)'</span>\n",
"<span class=\"sd\"> ],</span>\n",
"<span class=\"sd\"> 'steps': [</span>\n",
"<span class=\"sd\"> ['Drive(Home, SFOLongTermParking)', 'Shuttle(SFOLongTermParking, SFO)'],</span>\n",
"<span class=\"sd\"> ['Taxi(Home, SFO)'],</span>\n",
"<span class=\"sd\"> [],</span>\n",
"<span class=\"sd\"> [],</span>\n",
"<span class=\"sd\"> []</span>\n",
"<span class=\"sd\"> ],</span>\n",
"<span class=\"sd\"> # empty refinements indicate a primitive action</span>\n",
"<span class=\"sd\"> 'precond': [</span>\n",
"<span class=\"sd\"> ['At(Home) & Have(Car)'],</span>\n",
"<span class=\"sd\"> ['At(Home)'],</span>\n",
"<span class=\"sd\"> ['At(Home) & Have(Car)'],</span>\n",
"<span class=\"sd\"> ['At(SFOLongTermParking)'],</span>\n",
"<span class=\"sd\"> ['At(Home)']</span>\n",
"<span class=\"sd\"> ],</span>\n",
"<span class=\"sd\"> 'effect': [</span>\n",
"<span class=\"sd\"> ['At(SFO) & ~At(Home)'],</span>\n",
"<span class=\"sd\"> ['At(SFO) & ~At(Home)'],</span>\n",
"<span class=\"sd\"> ['At(SFOLongTermParking) & ~At(Home)'],</span>\n",
"<span class=\"sd\"> ['At(SFO) & ~At(SFOLongTermParking)'],</span>\n",
"<span class=\"sd\"> ['At(SFO) & ~At(Home)']</span>\n",
"<span class=\"sd\"> ]</span>\n",
"<span class=\"sd\"> }</span>\n",
"<span class=\"sd\"> """</span>\n",
" <span class=\"n\">e</span> <span class=\"o\">=</span> <span class=\"n\">Expr</span><span class=\"p\">(</span><span class=\"n\">hla</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">,</span> <span class=\"n\">hla</span><span class=\"o\">.</span><span class=\"n\">args</span><span class=\"p\">)</span>\n",
" <span class=\"n\">indices</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">i</span> <span class=\"k\">for</span> <span class=\"n\">i</span><span class=\"p\">,</span> <span class=\"n\">x</span> <span class=\"ow\">in</span> <span class=\"nb\">enumerate</span><span class=\"p\">(</span><span class=\"n\">library</span><span class=\"p\">[</span><span class=\"s1\">'HLA'</span><span class=\"p\">])</span> <span class=\"k\">if</span> <span class=\"n\">expr</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">op</span> <span class=\"o\">==</span> <span class=\"n\">hla</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">]</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"n\">indices</span><span class=\"p\">:</span>\n",
" <span class=\"n\">actions</span> <span class=\"o\">=</span> <span class=\"p\">[]</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">j</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">library</span><span class=\"p\">[</span><span class=\"s1\">'steps'</span><span class=\"p\">][</span><span class=\"n\">i</span><span class=\"p\">])):</span>\n",
" <span class=\"c1\"># find the index of the step [j] of the HLA </span>\n",
" <span class=\"n\">index_step</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">k</span> <span class=\"k\">for</span> <span class=\"n\">k</span><span class=\"p\">,</span><span class=\"n\">x</span> <span class=\"ow\">in</span> <span class=\"nb\">enumerate</span><span class=\"p\">(</span><span class=\"n\">library</span><span class=\"p\">[</span><span class=\"s1\">'HLA'</span><span class=\"p\">])</span> <span class=\"k\">if</span> <span class=\"n\">x</span> <span class=\"o\">==</span> <span class=\"n\">library</span><span class=\"p\">[</span><span class=\"s1\">'steps'</span><span class=\"p\">][</span><span class=\"n\">i</span><span class=\"p\">][</span><span class=\"n\">j</span><span class=\"p\">]][</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n",
" <span class=\"n\">precond</span> <span class=\"o\">=</span> <span class=\"n\">library</span><span class=\"p\">[</span><span class=\"s1\">'precond'</span><span class=\"p\">][</span><span class=\"n\">index_step</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"c1\"># preconditions of step [j]</span>\n",
" <span class=\"n\">effect</span> <span class=\"o\">=</span> <span class=\"n\">library</span><span class=\"p\">[</span><span class=\"s1\">'effect'</span><span class=\"p\">][</span><span class=\"n\">index_step</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"c1\"># effect of step [j]</span>\n",
" <span class=\"n\">actions</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">HLA</span><span class=\"p\">(</span><span class=\"n\">library</span><span class=\"p\">[</span><span class=\"s1\">'steps'</span><span class=\"p\">][</span><span class=\"n\">i</span><span class=\"p\">][</span><span class=\"n\">j</span><span class=\"p\">],</span> <span class=\"n\">precond</span><span class=\"p\">,</span> <span class=\"n\">effect</span><span class=\"p\">))</span>\n",
" <span class=\"k\">yield</span> <span class=\"n\">actions</span>\n",
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" <span class=\"k\">def</span> <span class=\"nf\">hierarchical_search</span><span class=\"p\">(</span><span class=\"n\">problem</span><span class=\"p\">,</span> <span class=\"n\">hierarchy</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\"> [Figure 11.5] 'Hierarchical Search, a Breadth First Search implementation of Hierarchical</span>\n",
"<span class=\"sd\"> Forward Planning Search'</span>\n",
"<span class=\"sd\"> The problem is a real-world problem defined by the problem class, and the hierarchy is</span>\n",
"<span class=\"sd\"> a dictionary of HLA - refinements (see refinements generator for details)</span>\n",
"<span class=\"sd\"> """</span>\n",
" <span class=\"n\">act</span> <span class=\"o\">=</span> <span class=\"n\">Node</span><span class=\"p\">(</span><span class=\"n\">problem</span><span class=\"o\">.</span><span class=\"n\">actions</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">])</span>\n",
" <span class=\"n\">frontier</span> <span class=\"o\">=</span> <span class=\"n\">deque</span><span class=\"p\">()</span>\n",
" <span class=\"n\">frontier</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">act</span><span class=\"p\">)</span>\n",
" <span class=\"k\">while</span> <span class=\"bp\">True</span><span class=\"p\">:</span>\n",
" <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">frontier</span><span class=\"p\">:</span>\n",
" <span class=\"k\">return</span> <span class=\"bp\">None</span>\n",
" <span class=\"n\">plan</span> <span class=\"o\">=</span> <span class=\"n\">frontier</span><span class=\"o\">.</span><span class=\"n\">popleft</span><span class=\"p\">()</span>\n",
" <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">state</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">)</span>\n",
" <span class=\"n\">hla</span> <span class=\"o\">=</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">state</span> <span class=\"c1\"># first_or_null(plan)</span>\n",
" <span class=\"n\">prefix</span> <span class=\"o\">=</span> <span class=\"bp\">None</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">parent</span><span class=\"p\">:</span>\n",
" <span class=\"n\">prefix</span> <span class=\"o\">=</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">parent</span><span class=\"o\">.</span><span class=\"n\">state</span><span class=\"o\">.</span><span class=\"n\">action</span> <span class=\"c1\"># prefix, suffix = subseq(plan.state, hla)</span>\n",
" <span class=\"n\">outcome</span> <span class=\"o\">=</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">result</span><span class=\"p\">(</span><span class=\"n\">problem</span><span class=\"p\">,</span> <span class=\"n\">prefix</span><span class=\"p\">)</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">hla</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span><span class=\"p\">:</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">outcome</span><span class=\"o\">.</span><span class=\"n\">goal_test</span><span class=\"p\">():</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">path</span><span class=\"p\">()</span>\n",
" <span class=\"k\">else</span><span class=\"p\">:</span>\n",
" <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"s2\">"else"</span><span class=\"p\">)</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">sequence</span> <span class=\"ow\">in</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">refinements</span><span class=\"p\">(</span><span class=\"n\">hla</span><span class=\"p\">,</span> <span class=\"n\">outcome</span><span class=\"p\">,</span> <span class=\"n\">hierarchy</span><span class=\"p\">):</span>\n",
" <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"s2\">"..."</span><span class=\"p\">)</span>\n",
" <span class=\"n\">frontier</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">Node</span><span class=\"p\">(</span><span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">parent</span><span class=\"p\">,</span> <span class=\"n\">sequence</span><span class=\"p\">))</span>\n",
" <span class=\"k\">def</span> <span class=\"nf\">result</span><span class=\"p\">(</span><span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">actions</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""The outcome of applying an action to the current problem"""</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">a</span> <span class=\"ow\">in</span> <span class=\"n\">actions</span><span class=\"p\">:</span> \n",
" <span class=\"k\">if</span> <span class=\"n\">a</span><span class=\"o\">.</span><span class=\"n\">check_precond</span><span class=\"p\">(</span><span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">a</span><span class=\"o\">.</span><span class=\"n\">args</span><span class=\"p\">):</span>\n",
" <span class=\"n\">state</span> <span class=\"o\">=</span> <span class=\"n\">a</span><span class=\"p\">(</span><span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">a</span><span class=\"o\">.</span><span class=\"n\">args</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">clauses</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">state</span>\n",
" \n",
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" <span class=\"k\">def</span> <span class=\"nf\">angelic_search</span><span class=\"p\">(</span><span class=\"n\">problem</span><span class=\"p\">,</span> <span class=\"n\">hierarchy</span><span class=\"p\">,</span> <span class=\"n\">initialPlan</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\">\t[Figure 11.8] A hierarchical planning algorithm that uses angelic semantics to identify and</span>\n",
"<span class=\"sd\">\tcommit to high-level plans that work while avoiding high-level plans that don’t. </span>\n",
"<span class=\"sd\">\tThe predicate MAKING-PROGRESS checks to make sure that we aren’t stuck in an infinite regression</span>\n",
"<span class=\"sd\">\tof refinements. </span>\n",
"<span class=\"sd\">\tAt top level, call ANGELIC -SEARCH with [Act ] as the initialPlan .</span>\n",
"\n",
"<span class=\"sd\"> initialPlan contains a sequence of HLA's with angelic semantics </span>\n",
"\n",
"<span class=\"sd\"> The possible effects of an angelic HLA in initialPlan are : </span>\n",
"<span class=\"sd\"> ~ : effect remove</span>\n",
"<span class=\"sd\"> $+: effect possibly add</span>\n",
"<span class=\"sd\"> $-: effect possibly remove</span>\n",
"<span class=\"sd\"> $$: possibly add or remove</span>\n",
"<span class=\"sd\">\t"""</span>\n",
" <span class=\"n\">frontier</span> <span class=\"o\">=</span> <span class=\"n\">deque</span><span class=\"p\">(</span><span class=\"n\">initialPlan</span><span class=\"p\">)</span>\n",
" <span class=\"k\">while</span> <span class=\"bp\">True</span><span class=\"p\">:</span> \n",
" <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">frontier</span><span class=\"p\">:</span>\n",
" <span class=\"k\">return</span> <span class=\"bp\">None</span>\n",
" <span class=\"n\">plan</span> <span class=\"o\">=</span> <span class=\"n\">frontier</span><span class=\"o\">.</span><span class=\"n\">popleft</span><span class=\"p\">()</span> <span class=\"c1\"># sequence of HLA/Angelic HLA's </span>\n",
" <span class=\"n\">opt_reachable_set</span> <span class=\"o\">=</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">reach_opt</span><span class=\"p\">(</span><span class=\"n\">problem</span><span class=\"o\">.</span><span class=\"n\">init</span><span class=\"p\">,</span> <span class=\"n\">plan</span><span class=\"p\">)</span>\n",
" <span class=\"n\">pes_reachable_set</span> <span class=\"o\">=</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">reach_pes</span><span class=\"p\">(</span><span class=\"n\">problem</span><span class=\"o\">.</span><span class=\"n\">init</span><span class=\"p\">,</span> <span class=\"n\">plan</span><span class=\"p\">)</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">problem</span><span class=\"o\">.</span><span class=\"n\">intersects_goal</span><span class=\"p\">(</span><span class=\"n\">opt_reachable_set</span><span class=\"p\">):</span> \n",
" <span class=\"k\">if</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">is_primitive</span><span class=\"p\">(</span> <span class=\"n\">plan</span><span class=\"p\">,</span> <span class=\"n\">hierarchy</span> <span class=\"p\">):</span> \n",
" <span class=\"k\">return</span> <span class=\"p\">([</span><span class=\"n\">x</span> <span class=\"k\">for</span> <span class=\"n\">x</span> <span class=\"ow\">in</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">action</span><span class=\"p\">])</span>\n",
" <span class=\"n\">guaranteed</span> <span class=\"o\">=</span> <span class=\"n\">problem</span><span class=\"o\">.</span><span class=\"n\">intersects_goal</span><span class=\"p\">(</span><span class=\"n\">pes_reachable_set</span><span class=\"p\">)</span> \n",
" <span class=\"k\">if</span> <span class=\"n\">guaranteed</span> <span class=\"ow\">and</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">making_progress</span><span class=\"p\">(</span><span class=\"n\">plan</span><span class=\"p\">,</span> <span class=\"n\">plan</span><span class=\"p\">):</span>\n",
" <span class=\"n\">final_state</span> <span class=\"o\">=</span> <span class=\"n\">guaranteed</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"c1\"># any element of guaranteed </span>\n",
" <span class=\"c1\">#print('decompose')</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">decompose</span><span class=\"p\">(</span><span class=\"n\">hierarchy</span><span class=\"p\">,</span> <span class=\"n\">problem</span><span class=\"p\">,</span> <span class=\"n\">plan</span><span class=\"p\">,</span> <span class=\"n\">final_state</span><span class=\"p\">,</span> <span class=\"n\">pes_reachable_set</span><span class=\"p\">)</span>\n",
" <span class=\"p\">(</span><span class=\"n\">hla</span><span class=\"p\">,</span> <span class=\"n\">index</span><span class=\"p\">)</span> <span class=\"o\">=</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">find_hla</span><span class=\"p\">(</span><span class=\"n\">plan</span><span class=\"p\">,</span> <span class=\"n\">hierarchy</span><span class=\"p\">)</span> <span class=\"c1\"># there should be at least one HLA/Angelic_HLA, otherwise plan would be primitive.</span>\n",
" <span class=\"n\">prefix</span> <span class=\"o\">=</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">action</span><span class=\"p\">[:</span><span class=\"n\">index</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span>\n",
" <span class=\"n\">suffix</span> <span class=\"o\">=</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">action</span><span class=\"p\">[</span><span class=\"n\">index</span><span class=\"o\">+</span><span class=\"mi\">1</span><span class=\"p\">:]</span>\n",
" <span class=\"n\">outcome</span> <span class=\"o\">=</span> <span class=\"n\">Problem</span><span class=\"p\">(</span><span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">result</span><span class=\"p\">(</span><span class=\"n\">problem</span><span class=\"o\">.</span><span class=\"n\">init</span><span class=\"p\">,</span> <span class=\"n\">prefix</span><span class=\"p\">),</span> <span class=\"n\">problem</span><span class=\"o\">.</span><span class=\"n\">goals</span> <span class=\"p\">,</span> <span class=\"n\">problem</span><span class=\"o\">.</span><span class=\"n\">actions</span> <span class=\"p\">)</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">sequence</span> <span class=\"ow\">in</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">refinements</span><span class=\"p\">(</span><span class=\"n\">hla</span><span class=\"p\">,</span> <span class=\"n\">outcome</span><span class=\"p\">,</span> <span class=\"n\">hierarchy</span><span class=\"p\">):</span> <span class=\"c1\"># find refinements</span>\n",
" <span class=\"n\">frontier</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">Angelic_Node</span><span class=\"p\">(</span><span class=\"n\">outcome</span><span class=\"o\">.</span><span class=\"n\">init</span><span class=\"p\">,</span> <span class=\"n\">plan</span><span class=\"p\">,</span> <span class=\"n\">prefix</span> <span class=\"o\">+</span> <span class=\"n\">sequence</span><span class=\"o\">+</span> <span class=\"n\">suffix</span><span class=\"p\">,</span> <span class=\"n\">prefix</span><span class=\"o\">+</span><span class=\"n\">sequence</span><span class=\"o\">+</span><span class=\"n\">suffix</span><span class=\"p\">))</span>\n",
"\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">intersects_goal</span><span class=\"p\">(</span><span class=\"n\">problem</span><span class=\"p\">,</span> <span class=\"n\">reachable_set</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\"> Find the intersection of the reachable states and the goal</span>\n",
"<span class=\"sd\"> """</span>\n",
" <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">y</span> <span class=\"k\">for</span> <span class=\"n\">x</span> <span class=\"ow\">in</span> <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"n\">reachable_set</span><span class=\"o\">.</span><span class=\"n\">keys</span><span class=\"p\">())</span> <span class=\"k\">for</span> <span class=\"n\">y</span> <span class=\"ow\">in</span> <span class=\"n\">reachable_set</span><span class=\"p\">[</span><span class=\"n\">x</span><span class=\"p\">]</span> <span class=\"k\">if</span> <span class=\"nb\">all</span><span class=\"p\">(</span><span class=\"n\">goal</span> <span class=\"ow\">in</span> <span class=\"n\">y</span> <span class=\"k\">for</span> <span class=\"n\">goal</span> <span class=\"ow\">in</span> <span class=\"n\">problem</span><span class=\"o\">.</span><span class=\"n\">goals</span><span class=\"p\">)]</span> \n",
" <span class=\"k\">def</span> <span class=\"nf\">is_primitive</span><span class=\"p\">(</span><span class=\"n\">plan</span><span class=\"p\">,</span> <span class=\"n\">library</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\"> checks if the hla is primitive action </span>\n",
"<span class=\"sd\"> """</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">hla</span> <span class=\"ow\">in</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">action</span><span class=\"p\">:</span> \n",
" <span class=\"n\">indices</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">i</span> <span class=\"k\">for</span> <span class=\"n\">i</span><span class=\"p\">,</span> <span class=\"n\">x</span> <span class=\"ow\">in</span> <span class=\"nb\">enumerate</span><span class=\"p\">(</span><span class=\"n\">library</span><span class=\"p\">[</span><span class=\"s1\">'HLA'</span><span class=\"p\">])</span> <span class=\"k\">if</span> <span class=\"n\">expr</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">op</span> <span class=\"o\">==</span> <span class=\"n\">hla</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">]</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"n\">indices</span><span class=\"p\">:</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">library</span><span class=\"p\">[</span><span class=\"s2\">"steps"</span><span class=\"p\">][</span><span class=\"n\">i</span><span class=\"p\">]:</span> \n",
" <span class=\"k\">return</span> <span class=\"bp\">False</span>\n",
" <span class=\"k\">return</span> <span class=\"bp\">True</span>\n",
" \n",
" <span class=\"k\">def</span> <span class=\"nf\">reach_opt</span><span class=\"p\">(</span><span class=\"n\">init</span><span class=\"p\">,</span> <span class=\"n\">plan</span><span class=\"p\">):</span> \n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\"> Finds the optimistic reachable set of the sequence of actions in plan </span>\n",
"<span class=\"sd\"> """</span>\n",
" <span class=\"n\">reachable_set</span> <span class=\"o\">=</span> <span class=\"p\">{</span><span class=\"mi\">0</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"n\">init</span><span class=\"p\">]}</span>\n",
" <span class=\"n\">optimistic_description</span> <span class=\"o\">=</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">action</span> <span class=\"c1\">#list of angelic actions with optimistic description</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">find_reachable_set</span><span class=\"p\">(</span><span class=\"n\">reachable_set</span><span class=\"p\">,</span> <span class=\"n\">optimistic_description</span><span class=\"p\">)</span>\n",
" \n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">reach_pes</span><span class=\"p\">(</span><span class=\"n\">init</span><span class=\"p\">,</span> <span class=\"n\">plan</span><span class=\"p\">):</span> \n",
" <span class=\"sd\">""" </span>\n",
"<span class=\"sd\"> Finds the pessimistic reachable set of the sequence of actions in plan</span>\n",
"<span class=\"sd\"> """</span>\n",
" <span class=\"n\">reachable_set</span> <span class=\"o\">=</span> <span class=\"p\">{</span><span class=\"mi\">0</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"n\">init</span><span class=\"p\">]}</span>\n",
" <span class=\"n\">pessimistic_description</span> <span class=\"o\">=</span> <span class=\"n\">plan</span><span class=\"o\">.</span><span class=\"n\">action_pes</span> <span class=\"c1\"># list of angelic actions with pessimistic description</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">Problem</span><span class=\"o\">.</span><span class=\"n\">find_reachable_set</span><span class=\"p\">(</span><span class=\"n\">reachable_set</span><span class=\"p\">,</span> <span class=\"n\">pessimistic_description</span><span class=\"p\">)</span>\n",
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
" <span class=\"k\">def</span> <span class=\"nf\">find_reachable_set</span><span class=\"p\">(</span><span class=\"n\">reachable_set</span><span class=\"p\">,</span> <span class=\"n\">action_description</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\">\tFinds the reachable states of the action_description when applied in each state of reachable set.</span>\n",
"<span class=\"sd\">\t"""</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">action_description</span><span class=\"p\">)):</span>\n",
" <span class=\"n\">reachable_set</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"o\">+</span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"o\">=</span><span class=\"p\">[]</span>\n",
" <span class=\"k\">if</span> <span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">action_description</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">])</span> <span class=\"ow\">is</span> <span class=\"n\">Angelic_HLA</span><span class=\"p\">:</span>\n",
" <span class=\"n\">possible_actions</span> <span class=\"o\">=</span> <span class=\"n\">action_description</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">angelic_action</span><span class=\"p\">()</span>\n",
" <span class=\"k\">else</span><span class=\"p\">:</span> \n",
" <span class=\"n\">possible_actions</span> <span class=\"o\">=</span> <span class=\"n\">action_description</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">action</span> <span class=\"ow\">in</span> <span class=\"n\">possible_actions</span><span class=\"p\">:</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">state</span> <span class=\"ow\">in</span> <span class=\"n\">reachable_set</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]:</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">action</span><span class=\"o\">.</span><span class=\"n\">check_precond</span><span class=\"p\">(</span><span class=\"n\">state</span> <span class=\"p\">,</span> <span class=\"n\">action</span><span class=\"o\">.</span><span class=\"n\">args</span><span class=\"p\">)</span> <span class=\"p\">:</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">action</span><span class=\"o\">.</span><span class=\"n\">effect</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"p\">:</span>\n",
" <span class=\"n\">new_state</span> <span class=\"o\">=</span> <span class=\"n\">action</span><span class=\"p\">(</span><span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">action</span><span class=\"o\">.</span><span class=\"n\">args</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">clauses</span>\n",
" <span class=\"n\">reachable_set</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"o\">+</span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">new_state</span><span class=\"p\">)</span>\n",
" <span class=\"k\">else</span><span class=\"p\">:</span> \n",
" <span class=\"n\">reachable_set</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"o\">+</span><span class=\"mi\">1</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">state</span><span class=\"p\">)</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">reachable_set</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">find_hla</span><span class=\"p\">(</span><span class=\"n\">plan</span><span class=\"p\">,</span> <span class=\"n\">hierarchy</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""</span>\n",
"<span class=\"sd\"> Finds the the first HLA action in plan.action, which is not primitive</span>\n",
"<span class=\"sd\"> and its corresponding index in plan.action</span>\n",