/AI21_part2

Pacman IA Problem solve with bayes filter

Primary LanguagePython

Project II

Table of contents

Deliverables

You are requested to deliver a tar.gz archive containing:

  • Your report named report.pdf.
    • Your report must be at most 5 pages long.
    • Fill in the following template to write your report.
    • In French or English.
  • A file named bayesfilter.py containing your implementation of the Bayes filter algorithm.
    • Simply modify the provided bayesfilter.py file.
    • ⚠️ Do not change the class name (BeliefStateAgent).
  • (optional) A file named pacmanagent.py containing your implementation of the BONUS.
    • Simply modify the provided pacmanagent.py file. :warning: A penalty of -2 points on the final grade will be applied if the files are not named based on the instructions above.

Instructions

This part is due by November 25, 2021 at 23:59. This is a hard deadline.

In this third part of the project, Pacman got tired of ghosts wandering around him. So he decided to buy a laser gun and kill them. But while he shot them, he figured out that the gun has instead turned them into invisible but edible ghosts! Fortunately, as it was part of a box that he bought from a flea market, he also got his hands on a rusty sensor, which still works but is subject to measurement errors which are described in the user manual.

A lot of confusion arose since Pacman shot the ghosts: he has no idea where they currently are in the maze! However, he knows that the ghosts are confused and should be willing to escape from him. More precisely, he knows that the first ghost, named scared, is more fearful than the second ghost, named afraid, who is more fearful than the third ghost, named confused.

Your task is to design an intelligent agent based on the Bayes filter algorithm (see Lecture 6) for locating all the ghosts in the maze.

You may use the following command line to start a game where the sole eadible scared ghost wanders around the maze while Pacman, controlled by the humanagent.py policy, tries to locate him with a (very) rusty sensor:

python run.py --agentfile humanagent.py --bsagentfile bayesfilter.py --ghostagent scared --nghosts 1 --seed -1 --layout large_filter

Note that when you use multiple ghosts, they all run the same policy (e.g., all scared). Change the value of seed - for random number generator - to a positive value to ease reproducibility of your experiments.

You are asked to answer the following questions:

  1. Bayes filter

    • 1.a. - 2 point - Describe mathematically the sensor model of the rusty sensor, as implemented in _get_evidence of the BeliefStateAgent class.
    • 1.b. - 2 points - Provide a unified parametrized transition model from which the ghosts scared, afraid and confused can be derived. Derive this model from the ghost implementations found in /pacman_module/ghostAgents.py (functions getDistribution). Your model should specify a single free parameter. :warning: Be aware that in project 2, the ghosts are now able to go move backward, on the contrary to project 1.

    Answers to the previous questions should not make any reference to the API nor include pseudo-code.

  2. Implementation

    • 2.a. - 6 points - Implement the Bayes filter algorithm to compute Pacman's belief state. This should be done in the _get_updated_belief function of bayesfilter.py.
      • Your function _get_updated_belief (2 points) should use the functions _get_sensor_model (2 points) and _get_transition_model (2 points) that you should also define yourself.
      • Your implementation must work with multiple ghosts (all running the same policy).
      • Pacman's belief state should eventually converge to an uncertainty area for each ghost.
      • Your filter should consider the Pacman position, as Pacman may wander freely in the maze.

  3. Experiment

    • 3.a. - 1 point - Provide a measure which summarizes Pacman's belief state (i.e., its uncertainty).
    • 3.b. - 1 point - Provide a measure of the quality of the belief state(s). You may assume access to the ground truth (i.e., the true position of the ghost(s)).
    • 3.c. - 3 points - Run your filter implementation on the /pacman_module/layouts/large_filter.lay and the /pacman_module/layouts/large_filter_walls.lay layouts, against each type of ghost. Report your results graphically.
      • Record your measures (see _record_metrics function in bayesfilter.py) averaged over several trials.
      • Your results should come with error bars.
      • The number of trials must be high enough and their duration long enough so that the measures have converged.
    • 3.d. - 1 points - Discuss the effect of the ghost transition model parameter on its own behavior and on Pacman's belief state. Consider the two provided layouts. Motivate your answer by using your measures and the model itself. Use the default sensor variance.
    • 3.e. - 1 points - Discuss the effect of the sensor variance (as set through the --sensorvariance command line argument) on Pacman's belief state.
    • 3.f. - 1 points - How would you implement a Pacman controller to eat ghosts using only its current position, the set of legal actions and its current belief state?
    • 3.g. - BONUS 3 points - Implement this controller in the pacmanagent.py file.

Evaluation

Besides the questions you're expected to answer, you will also be evaluated according to the following criteria:

  • Code style - 2 points
    • PEP8 compatibility - 0.8 point - PEP8 guidelines are provided at Style Guide for Python Code. A script will be executed to check the compatibility of your code.
      • 0.8 point : the script runs without error.
      • 0 point: any error during the execution of the script.
    • Specification - 1.2 point - correctness of the specification of your functions.
      • 1.2 point : all specifications are correct.
      • 0.9 point : at least 75% correct specifications.
      • 0.6 point : at least 50% correct specifications.
      • 0.3 point : at least 25% correct specifications.
      • 0 point : less than 25% correct specifications.

Note that your implementation might be tested on other layouts, with Pacman moving arbitrarily.

⚠️ Take care of providing a clearly written report, which fully follows the provided template. We reserve the right to refuse to evaluate a report (i.e. to consider it as not provided) which would be difficult to read and understand. We may also refuse to evaluate discussion blocks that are truly confusing, even if the underlying idea might be right. Sanctions will be imposed in case of non-respect of the guidelines about the structure and length of the report:

  • Any modification of the template: - 2 points
  • Only the first 5 pages of the report will be taken into account for the evaluation.

⚠️ Plagiarism is checked and sanctioned by a grade of 0. Cases of plagiarism will all be reported to the Faculty.

Credits

The programming projects are adapted from CS188 (UC Berkeley).