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Removed all Rule mechanism (aimacode#859)
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‎vacuum_world.ipynb

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"</head>\n",
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"<body>\n",
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"<h2></h2>\n",
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"\n",
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"<div class=\"highlight\"><pre><span></span><span class=\"k\">class</span> <span class=\"nc\">TrivialVacuumEnvironment</span><span class=\"p\">(</span><span class=\"n\">Environment</span><span class=\"p\">):</span>\n",
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"\n",
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" <span class=\"sd\">&quot;&quot;&quot;This environment has two locations, A and B. Each can be Dirty</span>\n",
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"<span class=\"sd\"> or Clean. The agent perceives its location and the location&#39;s</span>\n",
189+
"<span class=\"sd\"> status. This serves as an example of how to implement a simple</span>\n",
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"<span class=\"sd\"> Environment.&quot;&quot;&quot;</span>\n",
191+
"\n",
192+
" <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
193+
" <span class=\"nb\">super</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">()</span>\n",
194+
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">status</span> <span class=\"o\">=</span> <span class=\"p\">{</span><span class=\"n\">loc_A</span><span class=\"p\">:</span> <span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">choice</span><span class=\"p\">([</span><span class=\"s1\">&#39;Clean&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Dirty&#39;</span><span class=\"p\">]),</span>\n",
195+
" <span class=\"n\">loc_B</span><span class=\"p\">:</span> <span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">choice</span><span class=\"p\">([</span><span class=\"s1\">&#39;Clean&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Dirty&#39;</span><span class=\"p\">])}</span>\n",
196+
"\n",
197+
" <span class=\"k\">def</span> <span class=\"nf\">thing_classes</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
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" <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">Wall</span><span class=\"p\">,</span> <span class=\"n\">Dirt</span><span class=\"p\">,</span> <span class=\"n\">ReflexVacuumAgent</span><span class=\"p\">,</span> <span class=\"n\">RandomVacuumAgent</span><span class=\"p\">,</span>\n",
199+
" <span class=\"n\">TableDrivenVacuumAgent</span><span class=\"p\">,</span> <span class=\"n\">ModelBasedVacuumAgent</span><span class=\"p\">]</span>\n",
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"\n",
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" <span class=\"k\">def</span> <span class=\"nf\">percept</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">agent</span><span class=\"p\">):</span>\n",
202+
" <span class=\"sd\">&quot;&quot;&quot;Returns the agent&#39;s location, and the location status (Dirty/Clean).&quot;&quot;&quot;</span>\n",
203+
" <span class=\"k\">return</span> <span class=\"p\">(</span><span class=\"n\">agent</span><span class=\"o\">.</span><span class=\"n\">location</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">status</span><span class=\"p\">[</span><span class=\"n\">agent</span><span class=\"o\">.</span><span class=\"n\">location</span><span class=\"p\">])</span>\n",
204+
"\n",
205+
" <span class=\"k\">def</span> <span class=\"nf\">execute_action</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">agent</span><span class=\"p\">,</span> <span class=\"n\">action</span><span class=\"p\">):</span>\n",
206+
" <span class=\"sd\">&quot;&quot;&quot;Change agent&#39;s location and/or location&#39;s status; track performance.</span>\n",
207+
"<span class=\"sd\"> Score 10 for each dirt cleaned; -1 for each move.&quot;&quot;&quot;</span>\n",
208+
" <span class=\"k\">if</span> <span class=\"n\">action</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;Right&#39;</span><span class=\"p\">:</span>\n",
209+
" <span class=\"n\">agent</span><span class=\"o\">.</span><span class=\"n\">location</span> <span class=\"o\">=</span> <span class=\"n\">loc_B</span>\n",
210+
" <span class=\"n\">agent</span><span class=\"o\">.</span><span class=\"n\">performance</span> <span class=\"o\">-=</span> <span class=\"mi\">1</span>\n",
211+
" <span class=\"k\">elif</span> <span class=\"n\">action</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;Left&#39;</span><span class=\"p\">:</span>\n",
212+
" <span class=\"n\">agent</span><span class=\"o\">.</span><span class=\"n\">location</span> <span class=\"o\">=</span> <span class=\"n\">loc_A</span>\n",
213+
" <span class=\"n\">agent</span><span class=\"o\">.</span><span class=\"n\">performance</span> <span class=\"o\">-=</span> <span class=\"mi\">1</span>\n",
214+
" <span class=\"k\">elif</span> <span class=\"n\">action</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;Suck&#39;</span><span class=\"p\">:</span>\n",
215+
" <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">status</span><span class=\"p\">[</span><span class=\"n\">agent</span><span class=\"o\">.</span><span class=\"n\">location</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"s1\">&#39;Dirty&#39;</span><span class=\"p\">:</span>\n",
216+
" <span class=\"n\">agent</span><span class=\"o\">.</span><span class=\"n\">performance</span> <span class=\"o\">+=</span> <span class=\"mi\">10</span>\n",
217+
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">status</span><span class=\"p\">[</span><span class=\"n\">agent</span><span class=\"o\">.</span><span class=\"n\">location</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"s1\">&#39;Clean&#39;</span>\n",
218+
"\n",
219+
" <span class=\"k\">def</span> <span class=\"nf\">default_location</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">thing</span><span class=\"p\">):</span>\n",
220+
" <span class=\"sd\">&quot;&quot;&quot;Agents start in either location at random.&quot;&quot;&quot;</span>\n",
221+
" <span class=\"k\">return</span> <span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">choice</span><span class=\"p\">([</span><span class=\"n\">loc_A</span><span class=\"p\">,</span> <span class=\"n\">loc_B</span><span class=\"p\">])</span>\n",
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"</pre></div>\n",
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],
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"source": [
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"psource(TrivialVacuumEnvironment)"
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{
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"text": [
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"State of the Environment: {(0, 0): 'Dirty', (1, 0): 'Clean'}.\n"
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"State of the Environment: {(0, 0): 'Clean', (1, 0): 'Dirty'}.\n"
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"output_type": "stream",
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"text": [
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"RandomVacuumAgent is located at (0, 0).\n"
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"RandomVacuumAgent is located at (1, 0).\n"
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]
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}
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],
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},
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{
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"text": [
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"State of the Environment: {(0, 0): 'Dirty', (1, 0): 'Clean'}.\n",
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"RandomVacuumAgent is located at (0, 0).\n"
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"State of the Environment: {(0, 0): 'Clean', (1, 0): 'Dirty'}.\n",
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"RandomVacuumAgent is located at (1, 0).\n"
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]
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}
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],
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},
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},
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"text": [
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"State of the Environment: {(0, 0): 'Clean', (1, 0): 'Clean'}.\n",
291-
"TableDrivenVacuumAgent is located at (0, 0).\n"
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"State of the Environment: {(0, 0): 'Clean', (1, 0): 'Dirty'}.\n",
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"TableDrivenVacuumAgent is located at (1, 0).\n"
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}
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],
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"\n",
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"The schematic diagram shown in **Figure 2.9** of the book will make this more clear:\n",
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"\n",
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"<img src=\"/files/images/simple_reflex_agent.jpg\">"
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"\"![simple reflex agent](images/simple_reflex_agent.jpg)\""
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]
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},
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},
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
348-
"# TODO: Implement these functions for two-dimensional environment\n",
349-
"# Interpret-input function for the two-state environment\n",
350-
"def interpret_input(percept):\n",
351-
" pass\n",
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"\n",
353-
"rules = None\n",
487+
"loc_A = (0, 0)\n",
488+
"loc_B = (1, 0)\n",
489+
"\n",
490+
"\"\"\"We change the simpleReflexAgentProgram so that it doesn't make use of the Rule class\"\"\"\n",
491+
"def SimpleReflexAgentProgram():\n",
492+
" \"\"\"This agent takes action based solely on the percept. [Figure 2.10]\"\"\"\n",
493+
" \n",
494+
" def program(percept):\n",
495+
" loc, status = percept\n",
496+
" return ('Suck' if status == 'Dirty' \n",
497+
" else'Right' if loc == loc_A \n",
498+
" else'Left')\n",
499+
" return program\n",
354500
"\n",
355-
"# Rule-match function for the two-state environment\n",
356-
"def rule_match(state, rule):\n",
357-
" for rule in rules:\n",
358-
" if rule.matches(state):\n",
359-
" return rule \n",
360501
" \n",
361502
"# Create a simple reflex agent the two-state environment\n",
362-
"simple_reflex_agent = ReflexVacuumAgent()"
503+
"program = SimpleReflexAgentProgram()\n",
504+
"simple_reflex_agent = Agent(program)"
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]
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},
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{
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"text": [
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"State of the Environment: {(0, 0): 'Clean', (1, 0): 'Clean'}.\n",
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"SimpleReflexVacuumAgent is located at (0, 0).\n"
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"SimpleReflexVacuumAgent is located at (1, 0).\n"
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],
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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