working vacuum agent

Author: hmp-anthony

vacuum_search_agent.ipynb

@@ -2,13 +2,26 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "id": "8538faa5-d001-4cd1-aca7-713308a31db2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5639076232910156\n",
+      "-1527\n"
+     ]
+    }
+   ],
    "source": [
     "from search_2 import *\n",
+    "import time\n",
+    "\n",
+    "N = 100\n",
     "\n",
+    "# First we code up the search agent\n",
     "class VacuumSearch(Problem):\n",
     "    def __init__(self, initial=(0,0), goal=None, dirt=[], **kwds):\n",
     "        Problem.__init__(self, initial=initial, goal=goal, dirt=dirt, **kwds)\n",
@@ -18,41 +31,53 @@
     "\n",
     "    def action_cost(self, s, action, s1):\n",
     "        if s == s1:\n",
-    "            # we have sucked up some dirt\n",
     "            return -100\n",
     "        return 1\n",
     "    \n",
     "    def is_goal(self, state):\n",
     "        return self.dirt == []\n",
     "\n",
     "    def result(self, state, action):\n",
-    "        return action if action not in self.dirt else state\n",
+    "        return action\n",
     "    \n",
     "    def actions(self, state):\n",
     "        if state in self.dirt:\n",
     "            self.dirt.remove(state)\n",
-    "            return set(state)\n",
+    "            S = {(state[0],state[1])}\n",
+    "            return S\n",
     "        x, y = state\n",
     "        S = {(x + dx, y + dy) for (dx, dy) in self.directions}\n",
-    "        Z = {x for x in S if x[0] in range(3) and x[1] in range(3)}\n",
+    "        Z = {x for x in S if x[0] in range(N) and x[1] in range(N)}\n",
     "        return Z\n",
     "\n",
-    "v = VacuumSearch(dirt=[(0,2), (1,2), (2,2)])\n",
-    "print(path_states(uniform_cost_search(v)))"
+    "\n",
+    "k = int(0.2 * N)\n",
+    "dirt = [(x,y) for x in range(N) for y in range(N)]\n",
+    "dirt = random.sample(dirt, k=k)\n",
+    "v = VacuumSearch(dirt=dirt)\n",
+    "\n",
+    "# execution time\n",
+    "t0 = time.time()\n",
+    "u1 = uniform_cost_search(v)\n",
+    "t1 = time.time()\n",
+    "print(t1-t0)\n",
+    "\n",
+    "# path cost of actions\n",
+    "print(u1.path_cost) "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dab5c21d-7f22-4cb2-938d-6a32d3aa4b93",
+   "id": "952c637b-82fc-4ad6-bc49-39998d9acd8e",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5d4efa04-60c9-4f4d-a100-9bd48aeaa5af",
+   "id": "4f3bf803-5b3d-48ca-bf5a-75161303aeb0",
    "metadata": {},
    "outputs": [],
    "source": []