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AI Problem 5. (36 points) The remainder of the homework involves installing and running the aima code. Follow the instructions on the website for installing

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Problem 5. (36 points) The remainder of the homework involves installing and running the aima code. Follow the instructions on the website for installing the aima code onto your CSELabs account. Once it is installed, examine the agents.py module. There is a lot in there, but also many classes include examples of how to use them. Let's take a look at the ReflexVacuumAgent class: def ReflexVacuumAgent(): "\|" [Figure 2.8] A reflex agent for the two-state vacuum environment. >> agent = ReflexVacuumAgent() >> environment = TrivialVacuumEnvironment() >>> environment.add_thing(agent) >>> environment.run() >> environment.status =={(1,0) : 'Clean', (0,0): 'Clean' } True " " " Once you load in agent.py using the interactive python interpreter, these comments here show you the commands you can use to create a ReflexVacuumAgent object, then an environment for it, then run it in the environment. If you wanted to run the agent in that environment for a limited amount of time, you can pass an integer in as a parameter the the environment.run call (e.g. environment.run(50)) - Run the ReflexVacuumAgent in the TrivialVacuumEnvironment for 15 steps. Use the TraceAgent class to print out its perceptions and actions at each time step. Return the environment status. - Run the ModelBasedVacuumAgent in the TrivialVacuumEnvironment for 15 steps. Use the TraceAgent class to print out its perceptions and actions at each time step. Return the environment status. - Run the RandomVacuumAgent agent in the TrivialVacuumEnvironment for 15 steps. Use the TraceAgent class to print out its perceptions and actions at each time step. Return the environment status. - Use the compare_agents function to compare the three agents in the trivial environment. Return the results of the comparison

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