From 3709b1e1348aeba254a385a9251236fecec03faf Mon Sep 17 00:00:00 2001
From: Claudio Scheer <claudioscheer@protonmail.com>
Date: Sun, 17 May 2020 14:54:55 -0300
Subject: [PATCH] Test whether goals are a subset of the state when planning

---
 AP-Jupyternotebook.ipynb | 179 +++++++++++++++++++--------------------
 paper/paper.pdf          | Bin 153323 -> 153371 bytes
 paper/paper.tex          |   4 +-
 3 files changed, 91 insertions(+), 92 deletions(-)

diff --git a/AP-Jupyternotebook.ipynb b/AP-Jupyternotebook.ipynb
index af23b26..2fc688e 100644
--- a/AP-Jupyternotebook.ipynb
+++ b/AP-Jupyternotebook.ipynb
@@ -123,11 +123,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-05-17T01:55:20.718217Z",
-     "start_time": "2020-05-17T01:55:20.707945Z"
+     "end_time": "2020-05-17T17:05:29.902684Z",
+     "start_time": "2020-05-17T17:05:29.899973Z"
     }
    },
    "outputs": [],
@@ -322,11 +322,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-05-16T18:04:16.102453Z",
-     "start_time": "2020-05-16T18:04:16.098534Z"
+     "end_time": "2020-05-17T17:04:38.756056Z",
+     "start_time": "2020-05-17T17:04:38.514619Z"
     },
     "deletable": false,
     "nbgrader": {
@@ -616,11 +616,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 76,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-05-16T00:40:57.167090Z",
-     "start_time": "2020-05-16T00:40:56.975360Z"
+     "end_time": "2020-05-17T04:19:41.932269Z",
+     "start_time": "2020-05-17T04:19:41.904592Z"
     },
     "deletable": false,
     "nbgrader": {
@@ -636,31 +636,14 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Expected 6, got: 6. Correct!\n",
-      "Expected 0, got: 0. Correct!\n",
-      "Expected 2, got: 2. Correct!\n",
-      "Expected 2, got: 2. Correct!\n",
-      "Expected 1, got: 1. Correct!\n",
-      "Expected 2, got: 2. Correct!\n",
-      ":constants is not recognized in domain\n",
-      "Expected 4, got: 4. Correct!\n",
-      ":constants is not recognized in domain\n",
-      "Expected 4, got: 4. Correct!\n",
-      "------------------------------\n",
-      "Expected 38, got: 38. Correct!\n",
-      "Expected 0, got: 0. Correct!\n",
-      "Expected 8, got: 8. Correct!\n",
-      "Expected 8, got: 8. Correct!\n",
-      "Expected 2, got: 2. Correct!\n",
-      "Expected 2, got: 2. Correct!\n",
-      ":constants is not recognized in domain\n",
-      "Expected 7, got: 7. Correct!\n",
-      ":constants is not recognized in domain\n",
-      "Expected 10, got: 10. Correct!\n",
-      "------------------------------\n"
+     "ename": "NameError",
+     "evalue": "name 'MaxHeuristic' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-76-e691caae032b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMaxHeuristic\u001b[0m\u001b[0;34m(\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     47\u001b[0m \u001b[0mtest_heuristic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdwr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpb1_dwr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0mtest_heuristic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdwr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpb2_dwr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'MaxHeuristic' is not defined"
      ]
     }
    ],
@@ -734,15 +717,15 @@
     "\n",
     "print(\"-\" * 30)\n",
     "\n",
-    "# h = FastForwardHeuristic()\n",
-    "# test_heuristic(dwr, pb1_dwr, h, 16)\n",
-    "# test_heuristic(dwr, pb2_dwr, h, 0)\n",
-    "# test_heuristic(tsp, pb1_tsp, h, 5)\n",
-    "# test_heuristic(tsp, pb2_tsp, h, 5)\n",
-    "# test_heuristic(dinner, pb1_dinner, h, 2)\n",
-    "# test_heuristic(dompteur, pb1_dompteur, h, 2)\n",
-    "# test_heuristic(logistics, pb1_logistics, h, 5)\n",
-    "# test_heuristic(logistics, pb2_logistics, h, 5)"
+    "h = FastForwardHeuristic()\n",
+    "test_heuristic(dwr, pb1_dwr, h, 16)\n",
+    "test_heuristic(dwr, pb2_dwr, h, 0)\n",
+    "test_heuristic(tsp, pb1_tsp, h, 5)\n",
+    "test_heuristic(tsp, pb2_tsp, h, 5)\n",
+    "test_heuristic(dinner, pb1_dinner, h, 2)\n",
+    "test_heuristic(dompteur, pb1_dompteur, h, 2)\n",
+    "test_heuristic(logistics, pb1_logistics, h, 5)\n",
+    "test_heuristic(logistics, pb2_logistics, h, 5)"
    ]
   },
   {
@@ -1352,7 +1335,7 @@
     "\n",
     "class CriticalPathHeuristic(Heuristic):\n",
     "    \"\"\"\n",
-    "        Haslum\"s H^m Heuristic\n",
+    "        Haslum's H^m Heuristic\n",
     "    \"\"\"\n",
     "\n",
     "    def __init__(self, m, stats=None):\n",
@@ -1441,11 +1424,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-05-17T03:39:02.794775Z",
-     "start_time": "2020-05-17T03:39:02.764628Z"
+     "end_time": "2020-05-17T17:05:47.819217Z",
+     "start_time": "2020-05-17T17:05:47.809965Z"
     },
     "deletable": false,
     "nbgrader": {
@@ -1503,9 +1486,9 @@
     "                    if applicable(\n",
     "                        state, (action.positive_preconditions, action.negative_preconditions)\n",
     "                    ):\n",
-    "                        state = apply(state, (action.add_effects, action.del_effects)).union(state)\n",
+    "                        state = apply(state, (action.add_effects, action.del_effects))\n",
     "                        break\n",
-    "\n",
+    "        \n",
     "        goals_reached = goals[0].intersection(state)\n",
     "        return goals_reached == goals[0]"
    ]
@@ -1532,11 +1515,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-05-17T03:39:04.126609Z",
-     "start_time": "2020-05-17T03:39:03.957513Z"
+     "end_time": "2020-05-17T17:16:56.846648Z",
+     "start_time": "2020-05-17T17:16:56.752994Z"
     },
     "deletable": false,
     "editable": false,
@@ -1741,11 +1724,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-05-16T18:04:38.160670Z",
-     "start_time": "2020-05-16T18:04:38.154065Z"
+     "end_time": "2020-05-17T17:42:25.097593Z",
+     "start_time": "2020-05-17T17:42:25.073069Z"
     },
     "deletable": false,
     "nbgrader": {
@@ -1779,43 +1762,51 @@
     "            goals: tuple with (positive predicates, negative predicates) of the goal\n",
     "        \"\"\"\n",
     "        frontier = queue.PriorityQueue()\n",
-    "        came_from = {}\n",
-    "        cost_so_far = {}\n",
+    "        \n",
+    "        parent_state = {}\n",
+    "        state_cost = {}\n",
+    "        action_applied = {}\n",
+    "        heuristic_at = {}\n",
+    "\n",
+    "        parent_state[state] = None\n",
+    "        state_cost[state] = 0\n",
+    "        action_applied[state] = None\n",
+    "        heuristic_at[state] = self.h(actions, state, goals)\n",
+    "\n",
     "        frontier.put(state, 0)\n",
-    "        came_from[state] = None\n",
-    "        cost_so_far[state] = 0\n",
-    "        goals_missing = goals[0]\n",
     "\n",
     "        while not frontier.empty():\n",
     "            current_state = frontier.get()\n",
-    "            goals_reached = goals_missing.intersection(current_state)\n",
-    "            if goals_reached:\n",
-    "                goals_missing = goals_missing.difference(goals_reached)\n",
-    "                if not goals_missing:\n",
-    "                    # Get the backward path.\n",
-    "                    path = []\n",
-    "                    while came_from[current_state]:\n",
-    "                        path.append(current_state)\n",
-    "                        current_state = came_from[current_state]\n",
-    "                    # path.append(current)\n",
-    "                    # path.reverse()\n",
-    "                    return path\n",
+    "\n",
+    "            # Test whether the goals have been reached.\n",
+    "            if goals[0].issubset(current_state):\n",
+    "                current = current_state\n",
+    "                # Get the backward path.\n",
+    "                path = []\n",
+    "                while current is not None:\n",
+    "                    path.append(current)\n",
+    "                    current = parent_state[current]\n",
+    "                path.reverse()\n",
+    "                return path\n",
     "\n",
     "            for action in actions:\n",
     "                # Get actions applicable to current state.\n",
     "                if applicable(\n",
-    "                    current_state, (action.positive_preconditions, action.negative_preconditions)\n",
+    "                    current_state, (action.positive_preconditions, action.negative_preconditions),\n",
     "                ):\n",
-    "                    new_state = apply(current_state, (action.add_effects, action.del_effects))\n",
+    "                    next_state = apply(current_state, (action.add_effects, action.del_effects))\n",
     "                    # +1 because this is the cost to move from one state to another.\n",
-    "                    # When not used, the result is the same.\n",
-    "                    cost = cost_so_far[current_state] + 1\n",
-    "                    if new_state not in cost_so_far or cost < cost_so_far[new_state]:\n",
-    "                        cost_so_far[new_state] = cost\n",
-    "                        priority = cost + self.h(actions, new_state, goals)\n",
-    "                        frontier.put(new_state, priority)\n",
-    "                        came_from[new_state] = current_state\n",
-    "\n",
+    "                    cost_next_state = state_cost[current_state] + 1\n",
+    "                    if next_state not in state_cost or cost_next_state < state_cost[next_state]:\n",
+    "                        heuristic_value = self.h(actions, next_state, goals)\n",
+    "                        if heuristic_value == float(\"inf\"):\n",
+    "                            # State non-reachable.\n",
+    "                            continue\n",
+    "                        state_cost[next_state] = cost_next_state\n",
+    "                        heuristic_at[next_state] = heuristic_value\n",
+    "                        parent_state[next_state] = current_state\n",
+    "                        action_applied[next_state] = action\n",
+    "                        frontier.put(next_state, cost_next_state + heuristic_value)\n",
     "        return None"
    ]
   },
@@ -1841,11 +1832,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-05-16T18:04:55.198053Z",
-     "start_time": "2020-05-16T18:04:46.178756Z"
+     "end_time": "2020-05-17T17:43:03.270695Z",
+     "start_time": "2020-05-17T17:42:27.781616Z"
     },
     "deletable": false,
     "nbgrader": {
@@ -1861,11 +1852,19 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Expected 17, got: 174. False!\n",
-      "Expected 0, got: 0. Correct!\n"
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-239cf38559c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplanner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHeuristicPlanner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mplan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplanner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdwr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpb1\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      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expected 17, got:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\". Correct!\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplan\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m17\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\". False!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mplan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplanner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdwr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpb2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/media/water/files/Projects/heuristic-planning-2020-1-claudioscheer/pddl/pddl_planner.py\u001b[0m in \u001b[0;36msolve_file\u001b[0;34m(self, domainfile, problemfile)\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mground_actions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# compute stats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m         plan = self.solve(\n\u001b[0m\u001b[1;32m     34\u001b[0m             \u001b[0mground_actions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpositive_goals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnegative_goals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         )\n",
+      "\u001b[0;32m<ipython-input-12-a64011e3a7b0>\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, actions, state, goals)\u001b[0m\n\u001b[1;32m     54\u001b[0m                     \u001b[0mcost_next_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate_cost\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_state\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mnew_state\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstate_cost\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mcost_next_state\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mstate_cost\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_state\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m                         \u001b[0mheuristic_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgoals\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     57\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mheuristic_value\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m                             \u001b[0;31m# State non-reachable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/media/water/files/Projects/heuristic-planning-2020-1-claudioscheer/pddl/heuristic.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, actions, initial_state, goals)\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mh_calls\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgoals\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     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdomain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgoals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-3-81d2c01d1281>\u001b[0m in \u001b[0;36mh\u001b[0;34m(self, actions, state, goals)\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgoals_missing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreachable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m             \u001b[0;31m# Get all actions applicable to the current state level.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m             \u001b[0mlast_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mactions\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpositive_preconditions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreachable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\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     13\u001b[0m             \u001b[0;31m# The next state will contain all the actions from previous states,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0;31m# plus the effects actions when executing the actions applicable to the current state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-3-81d2c01d1281>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgoals_missing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreachable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m             \u001b[0;31m# Get all actions applicable to the current state level.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m             \u001b[0mlast_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mactions\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpositive_preconditions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreachable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\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     13\u001b[0m             \u001b[0;31m# The next state will contain all the actions from previous states,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0;31m# plus the effects actions when executing the actions applicable to the current state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
diff --git a/paper/paper.pdf b/paper/paper.pdf
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GIT binary patch
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diff --git a/paper/paper.tex b/paper/paper.tex
index 3f742c9..35870d4 100644
--- a/paper/paper.tex
+++ b/paper/paper.tex
@@ -60,9 +60,9 @@ Six domains were tested in the implementations. In this section, I explain this
 \subsection{Blocksworld}
 \subsection{Dinner}
 \subsection{Dompteur}
-\subsection{DWR}
+\subsection{DWR - Dock Worker Robots}
 \subsection{Logistics}
-\subsection{TSP}
+\subsection{TSP - Travel Sales Person}
 
 
 \section{Heuristics}
-- 
GitLab