logic.ipynb 127 ko
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    "\n",
    "* There are no new clauses that can be added, in which case $\\text{KB} \\nvDash \\alpha$.\n",
    "* Two clauses resolve to yield the <em>empty clause</em>, in which case $\\text{KB} \\vDash \\alpha$.\n",
    "\n",
    "The <em>empty clause</em> is equivalent to <em>False</em> because it arises only from resolving two complementary\n",
    "unit clauses such as $P$ and $\\neg P$ which is a contradiction as both $P$ and $\\neg P$ can't be <em>True</em> at the same time."
   ]
  },
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  {
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    "There is  one catch however, the algorithm that implements proof by resolution cannot handle complex sentences. \n",
    "Implications and bi-implications have to be simplified into simpler clauses. \n",
    "We already know that *every sentence of a propositional logic is logically equivalent to a conjunction of clauses*.\n",
    "We will use this fact to our advantage and simplify the input sentence into the **conjunctive normal form** (CNF) which is a conjunction of disjunctions of literals.\n",
    "For eg:\n",
    "<br>\n",
    "$$(A\\lor B)\\land (\\neg B\\lor C\\lor\\neg D)\\land (D\\lor\\neg E)$$\n",
    "This is equivalent to the POS (Product of sums) form in digital electronics.\n",
    "<br>\n",
    "Here's an outline of how the conversion is done:\n",
    "1. Convert bi-implications to implications\n",
    "<br>\n",
    "$\\alpha\\iff\\beta$ can be written as $(\\alpha\\implies\\beta)\\land(\\beta\\implies\\alpha)$\n",
    "<br>\n",
    "This also applies to compound sentences\n",
    "<br>\n",
    "$\\alpha\\iff(\\beta\\lor\\gamma)$ can be written as $(\\alpha\\implies(\\beta\\lor\\gamma))\\land((\\beta\\lor\\gamma)\\implies\\alpha)$\n",
    "<br>\n",
    "2. Convert implications to their logical equivalents\n",
    "<br>\n",
    "$\\alpha\\implies\\beta$ can be written as $\\neg\\alpha\\lor\\beta$\n",
    "<br>\n",
    "3. Move negation inwards\n",
    "<br>\n",
    "CNF requires atomic literals. Hence, negation cannot appear on a compound statement.\n",
    "De Morgan's laws will be helpful here.\n",
    "<br>\n",
    "$\\neg(\\alpha\\land\\beta)\\equiv(\\neg\\alpha\\lor\\neg\\beta)$\n",
    "<br>\n",
    "$\\neg(\\alpha\\lor\\beta)\\equiv(\\neg\\alpha\\land\\neg\\beta)$\n",
    "<br>\n",
    "4. Distribute disjunction over conjunction\n",
    "<br>\n",
    "Disjunction and conjunction are distributive over each other.\n",
    "Now that we only have conjunctions, disjunctions and negations in our expression, \n",
    "we will distribute disjunctions over conjunctions wherever possible as this will give us a sentence which is a conjunction of simpler clauses, \n",
    "which is what we wanted in the first place.\n",
    "<br>\n",
    "We need a term of the form\n",
    "<br>\n",
    "$(\\alpha_{1}\\lor\\alpha_{2}\\lor\\alpha_{3}...)\\land(\\beta_{1}\\lor\\beta_{2}\\lor\\beta_{3}...)\\land(\\gamma_{1}\\lor\\gamma_{2}\\lor\\gamma_{3}...)\\land...$\n",
    "<br>\n",
    "<br>\n",
    "The `to_cnf` function executes this conversion using helper subroutines."
   ]
  },
  {
   "cell_type": "code",
Aman Deep Singh's avatar
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   "execution_count": 29,
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       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">to_cnf</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;Convert a propositional logical sentence to conjunctive normal form.</span>\n",
       "<span class=\"sd\">    That is, to the form ((A | ~B | ...) &amp; (B | C | ...) &amp; ...) [p. 253]</span>\n",
       "<span class=\"sd\">    &gt;&gt;&gt; to_cnf(&#39;~(B | C)&#39;)</span>\n",
       "<span class=\"sd\">    (~B &amp; ~C)</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">expr</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">,</span> <span class=\"nb\">str</span><span class=\"p\">):</span>\n",
       "        <span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">expr</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">eliminate_implications</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">)</span>  <span class=\"c1\"># Steps 1, 2 from p. 253</span>\n",
       "    <span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">move_not_inwards</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">)</span>  <span class=\"c1\"># Step 3</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">distribute_and_over_or</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">)</span>  <span class=\"c1\"># Step 4</span>\n",
       "</pre></div>\n",
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    "psource(to_cnf)"
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   "source": [
    "`to_cnf` calls three subroutines.\n",
    "<br>\n",
    "`eliminate_implications` converts bi-implications and implications to their logical equivalents.\n",
    "<br>\n",
    "`move_not_inwards` removes negations from compound statements and moves them inwards using De Morgan's laws.\n",
    "<br>\n",
    "`distribute_and_over_or` distributes disjunctions over conjunctions.\n",
    "<br>\n",
    "Run the cells below for implementation details.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource eliminate_implications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
Aman Deep Singh's avatar
Aman Deep Singh a validé
    "%psource move_not_inwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource distribute_and_over_or"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert some sentences to see how it works\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((A | ~B) & (B | ~A))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A, B, C, D = expr('A, B, C, D')\n",
    "to_cnf(A |'<=>'| B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((A | ~B | ~C) & (B | ~A) & (C | ~A))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_cnf(A |'<=>'| (B & C))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(A & (C | B) & (D | B))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_cnf(A & (B | (C & D)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((B | ~A | C | ~D) & (A | ~A | C | ~D) & (B | ~B | C | ~D) & (A | ~B | C | ~D))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_cnf((A |'<=>'| ~B) |'==>'| (C | ~D))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coming back to our resolution problem, we can see how the `to_cnf` function is utilized here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">pl_resolution</span><span class=\"p\">(</span><span class=\"n\">KB</span><span class=\"p\">,</span> <span class=\"n\">alpha</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;Propositional-logic resolution: say if alpha follows from KB. [Figure 7.12]&quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">clauses</span> <span class=\"o\">=</span> <span class=\"n\">KB</span><span class=\"o\">.</span><span class=\"n\">clauses</span> <span class=\"o\">+</span> <span class=\"n\">conjuncts</span><span class=\"p\">(</span><span class=\"n\">to_cnf</span><span class=\"p\">(</span><span class=\"o\">~</span><span class=\"n\">alpha</span><span class=\"p\">))</span>\n",
       "    <span class=\"n\">new</span> <span class=\"o\">=</span> <span class=\"nb\">set</span><span class=\"p\">()</span>\n",
       "    <span class=\"k\">while</span> <span class=\"bp\">True</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">clauses</span><span class=\"p\">)</span>\n",
       "        <span class=\"n\">pairs</span> <span class=\"o\">=</span> <span class=\"p\">[(</span><span class=\"n\">clauses</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">],</span> <span class=\"n\">clauses</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">])</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=\"n\">n</span><span class=\"p\">)</span> <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=\"n\">i</span><span class=\"o\">+</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">)]</span>\n",
       "        <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"n\">ci</span><span class=\"p\">,</span> <span class=\"n\">cj</span><span class=\"p\">)</span> <span class=\"ow\">in</span> <span class=\"n\">pairs</span><span class=\"p\">:</span>\n",
       "            <span class=\"n\">resolvents</span> <span class=\"o\">=</span> <span class=\"n\">pl_resolve</span><span class=\"p\">(</span><span class=\"n\">ci</span><span class=\"p\">,</span> <span class=\"n\">cj</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">if</span> <span class=\"bp\">False</span> <span class=\"ow\">in</span> <span class=\"n\">resolvents</span><span class=\"p\">:</span>\n",
       "                <span class=\"k\">return</span> <span class=\"bp\">True</span>\n",
       "            <span class=\"n\">new</span> <span class=\"o\">=</span> <span class=\"n\">new</span><span class=\"o\">.</span><span class=\"n\">union</span><span class=\"p\">(</span><span class=\"nb\">set</span><span class=\"p\">(</span><span class=\"n\">resolvents</span><span class=\"p\">))</span>\n",
       "        <span class=\"k\">if</span> <span class=\"n\">new</span><span class=\"o\">.</span><span class=\"n\">issubset</span><span class=\"p\">(</span><span class=\"nb\">set</span><span class=\"p\">(</span><span class=\"n\">clauses</span><span class=\"p\">)):</span>\n",
       "            <span class=\"k\">return</span> <span class=\"bp\">False</span>\n",
       "        <span class=\"k\">for</span> <span class=\"n\">c</span> <span class=\"ow\">in</span> <span class=\"n\">new</span><span class=\"p\">:</span>\n",
       "            <span class=\"k\">if</span> <span class=\"n\">c</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">clauses</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">clauses</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">c</span><span class=\"p\">)</span>\n",
       "</pre></div>\n",
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    "psource(pl_resolution)"
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       "(True, False)"
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    "pl_resolution(wumpus_kb, ~P11), pl_resolution(wumpus_kb, P11)"
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    {
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       "(False, False)"
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    "pl_resolution(wumpus_kb, ~P22), pl_resolution(wumpus_kb, P22)"
   ]
  },
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  {
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    "### Effective Propositional Model Checking\n",
    "\n",
    "The previous segments elucidate the algorithmic procedure for model checking. \n",
    "In this segment, we look at ways of making them computationally efficient.\n",
    "<br>\n",
    "The problem we are trying to solve is conventionally called the _propositional satisfiability problem_, abbreviated as the _SAT_ problem.\n",
    "In layman terms, if there exists a model that satisfies a given Boolean formula, the formula is called satisfiable.\n",
    "<br>\n",
    "The SAT problem was the first problem to be proven _NP-complete_.\n",
    "The main characteristics of an NP-complete problem are:\n",
    "- Given a solution to such a problem, it is easy to verify if the solution solves the problem.\n",
    "- The time required to actually solve the problem using any known algorithm increases exponentially with respect to the size of the problem.\n",
    "<br>\n",
    "<br>\n",
    "Due to these properties, heuristic and approximational methods are often applied to find solutions to these problems.\n",
    "<br>\n",
    "It is extremely important to be able to solve large scale SAT problems efficiently because \n",
    "many combinatorial problems in computer science can be conveniently reduced to checking the satisfiability of a propositional sentence under some constraints.\n",
    "<br>\n",
    "We will introduce two new algorithms that perform propositional model checking in a computationally effective way.\n",
    "<br>\n"
   ]
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   "cell_type": "markdown",
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    "### 1. DPLL (Davis-Putnam-Logeman-Loveland) algorithm\n",
    "This algorithm is very similar to Backtracking-Search.\n",
    "It recursively enumerates possible models in a depth-first fashion with the following improvements over algorithms like `tt_entails`:\n",
    "1. Early termination:\n",
    "<br>\n",
    "In certain cases, the algorithm can detect the truth value of a statement using just a partially completed model.\n",
    "For example, $(P\\lor Q)\\land(P\\lor R)$ is true if P is true, regardless of other variables.\n",
    "This reduces the search space significantly.\n",
    "2. Pure symbol heuristic:\n",
    "<br>\n",
    "A symbol that has the same sign (positive or negative) in all clauses is called a _pure symbol_.\n",
    "It isn't difficult to see that any satisfiable model will have the pure symbols assigned such that its parent clause becomes _true_.\n",
    "For example, $(P\\lor\\neg Q)\\land(\\neg Q\\lor\\neg R)\\land(R\\lor P)$ has P and Q as pure symbols\n",
    "and for the sentence to be true, P _has_ to be true and Q _has_ to be false.\n",
    "The pure symbol heuristic thus simplifies the problem a bit.\n",
    "3. Unit clause heuristic:\n",
    "<br>\n",
    "In the context of DPLL, clauses with just one literal and clauses with all but one _false_ literals are called unit clauses.\n",
    "If a clause is a unit clause, it can only be satisfied by assigning the necessary value to make the last literal true.\n",
    "We have no other choice.\n",
    "<br>\n",
    "Assigning one unit clause can create another unit clause.\n",
    "For example, when P is false, $(P\\lor Q)$ becomes a unit clause, causing _true_ to be assigned to Q.\n",
    "A series of forced assignments derived from previous unit clauses is called _unit propagation_.\n",
    "In this way, this heuristic simplifies the problem further.\n",
    "<br>\n",
    "The algorithm often employs other tricks to scale up to large problems.\n",
    "However, these tricks are currently out of the scope of this notebook. Refer to section 7.6 of the book for more details.\n",
    "<br>\n",
    "<br>\n",
    "Let's have a look at the algorithm."
   ]
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       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">dpll</span><span class=\"p\">(</span><span class=\"n\">clauses</span><span class=\"p\">,</span> <span class=\"n\">symbols</span><span class=\"p\">,</span> <span class=\"n\">model</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;See if the clauses are true in a partial model.&quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">unknown_clauses</span> <span class=\"o\">=</span> <span class=\"p\">[]</span>  <span class=\"c1\"># clauses with an unknown truth value</span>\n",
       "    <span class=\"k\">for</span> <span class=\"n\">c</span> <span class=\"ow\">in</span> <span class=\"n\">clauses</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">val</span> <span class=\"o\">=</span> <span class=\"n\">pl_true</span><span class=\"p\">(</span><span class=\"n\">c</span><span class=\"p\">,</span> <span class=\"n\">model</span><span class=\"p\">)</span>\n",
       "        <span class=\"k\">if</span> <span class=\"n\">val</span> <span class=\"ow\">is</span> <span class=\"bp\">False</span><span class=\"p\">:</span>\n",
       "            <span class=\"k\">return</span> <span class=\"bp\">False</span>\n",
       "        <span class=\"k\">if</span> <span class=\"n\">val</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"bp\">True</span><span class=\"p\">:</span>\n",
       "            <span class=\"n\">unknown_clauses</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">c</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">unknown_clauses</span><span class=\"p\">:</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">model</span>\n",
       "    <span class=\"n\">P</span><span class=\"p\">,</span> <span class=\"n\">value</span> <span class=\"o\">=</span> <span class=\"n\">find_pure_symbol</span><span class=\"p\">(</span><span class=\"n\">symbols</span><span class=\"p\">,</span> <span class=\"n\">unknown_clauses</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">if</span> <span class=\"n\">P</span><span class=\"p\">:</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">dpll</span><span class=\"p\">(</span><span class=\"n\">clauses</span><span class=\"p\">,</span> <span class=\"n\">removeall</span><span class=\"p\">(</span><span class=\"n\">P</span><span class=\"p\">,</span> <span class=\"n\">symbols</span><span class=\"p\">),</span> <span class=\"n\">extend</span><span class=\"p\">(</span><span class=\"n\">model</span><span class=\"p\">,</span> <span class=\"n\">P</span><span class=\"p\">,</span> <span class=\"n\">value</span><span class=\"p\">))</span>\n",
       "    <span class=\"n\">P</span><span class=\"p\">,</span> <span class=\"n\">value</span> <span class=\"o\">=</span> <span class=\"n\">find_unit_clause</span><span class=\"p\">(</span><span class=\"n\">clauses</span><span class=\"p\">,</span> <span class=\"n\">model</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">if</span> <span class=\"n\">P</span><span class=\"p\">:</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">dpll</span><span class=\"p\">(</span><span class=\"n\">clauses</span><span class=\"p\">,</span> <span class=\"n\">removeall</span><span class=\"p\">(</span><span class=\"n\">P</span><span class=\"p\">,</span> <span class=\"n\">symbols</span><span class=\"p\">),</span> <span class=\"n\">extend</span><span class=\"p\">(</span><span class=\"n\">model</span><span class=\"p\">,</span> <span class=\"n\">P</span><span class=\"p\">,</span> <span class=\"n\">value</span><span class=\"p\">))</span>\n",
       "    <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">symbols</span><span class=\"p\">:</span>\n",
       "        <span class=\"k\">raise</span> <span class=\"ne\">TypeError</span><span class=\"p\">(</span><span class=\"s2\">&quot;Argument should be of the type Expr.&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">P</span><span class=\"p\">,</span> <span class=\"n\">symbols</span> <span class=\"o\">=</span> <span class=\"n\">symbols</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"n\">symbols</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">:]</span>\n",
       "    <span class=\"k\">return</span> <span class=\"p\">(</span><span class=\"n\">dpll</span><span class=\"p\">(</span><span class=\"n\">clauses</span><span class=\"p\">,</span> <span class=\"n\">symbols</span><span class=\"p\">,</span> <span class=\"n\">extend</span><span class=\"p\">(</span><span class=\"n\">model</span><span class=\"p\">,</span> <span class=\"n\">P</span><span class=\"p\">,</span> <span class=\"bp\">True</span><span class=\"p\">))</span> <span class=\"ow\">or</span>\n",
       "            <span class=\"n\">dpll</span><span class=\"p\">(</span><span class=\"n\">clauses</span><span class=\"p\">,</span> <span class=\"n\">symbols</span><span class=\"p\">,</span> <span class=\"n\">extend</span><span class=\"p\">(</span><span class=\"n\">model</span><span class=\"p\">,</span> <span class=\"n\">P</span><span class=\"p\">,</span> <span class=\"bp\">False</span><span class=\"p\">)))</span>\n",
       "</pre></div>\n",
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    "The algorithm uses the ideas described above to check satisfiability of a sentence in propositional logic.\n",
    "It recursively calls itself, simplifying the problem at each step. It also uses helper functions `find_pure_symbol` and `find_unit_clause` to carry out steps 2 and 3 above.\n",
    "<br>\n",
    "The `dpll_satisfiable` helper function converts the input clauses to _conjunctive normal form_ and calls the `dpll` function with the correct parameters."
   ]
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       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">dpll_satisfiable</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;Check satisfiability of a propositional sentence.</span>\n",
       "<span class=\"sd\">    This differs from the book code in two ways: (1) it returns a model</span>\n",
       "<span class=\"sd\">    rather than True when it succeeds; this is more useful. (2) The</span>\n",
       "<span class=\"sd\">    function find_pure_symbol is passed a list of unknown clauses, rather</span>\n",
       "<span class=\"sd\">    than a list of all clauses and the model; this is more efficient.&quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">clauses</span> <span class=\"o\">=</span> <span class=\"n\">conjuncts</span><span class=\"p\">(</span><span class=\"n\">to_cnf</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">))</span>\n",
       "    <span class=\"n\">symbols</span> <span class=\"o\">=</span> <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"n\">prop_symbols</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">))</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">dpll</span><span class=\"p\">(</span><span class=\"n\">clauses</span><span class=\"p\">,</span> <span class=\"n\">symbols</span><span class=\"p\">,</span> <span class=\"p\">{})</span>\n",
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   "source": [
    "psource(dpll_satisfiable)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see a few examples of usage."
   ]
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   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "A, B, C, D = expr('A, B, C, D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{C: False, A: True, D: True, B: True}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpll_satisfiable(A & B & ~C & D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a simple case to highlight that the algorithm actually works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{C: True, D: False, B: True}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "dpll_satisfiable((A & B) | (C & ~A) | (B & ~D))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a particular symbol isn't present in the solution, \n",
    "it means that the solution is independent of the value of that symbol.\n",
    "In this case, the solution is independent of A."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{A: True, B: True}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpll_satisfiable(A |'<=>'| B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{C: True, A: True, B: False}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpll_satisfiable((A |'<=>'| B) |'==>'| (C & ~A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{C: True, A: True}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpll_satisfiable((A | (B & C)) |'<=>'| ((A | B) & (A | C)))"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. WalkSAT algorithm\n",
    "This algorithm is very similar to Hill climbing.\n",
    "On every iteration, the algorithm picks an unsatisfied clause and flips a symbol in the clause.\n",
    "This is similar to finding a neighboring state in the `hill_climbing` algorithm.\n",
    "<br>\n",
    "The symbol to be flipped is decided by an evaluation function that counts the number of unsatisfied clauses.\n",
    "Sometimes, symbols are also flipped randomly, to avoid local optima. A subtle balance between greediness and randomness is required. Alternatively, some versions of the algorithm restart with a completely new random assignment if no solution has been found for too long, as a way of getting out of local minima of numbers of unsatisfied clauses.\n",
    "<br>\n",
    "<br>\n",
    "Let's have a look at the algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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