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The code and full question for this project is attracted below

In this project, you will design agents for the classic version of Pacman, including ghosts. Along the way, you will implement both minimax and expectimax search and try your hand at evaluation function design.

The code base has not changed much from the previous project, but please start with a fresh installation, rather than intermingling files from project 1.

As in project 1, this project includes an autograder for you to grade your answers on your machine. This can be run on all questions with the command:

<code>python autograder.py
</code>

It can be run for one particular question, such as q2, by:

<code>python autograder.py <span class="nt">-q</span> q2
</code>

It can be run for one particular test by commands of the form:

<code>python autograder.py <span class="nt">-t</span> test_cases/q2/0-small-tree
</code>

By default, the autograder displays graphics with the -t option, but doesn’t with the -q option. You can force graphics by using the --graphics flag, or force no graphics by using the --no-graphics flag.

See the autograder tutorial in Project 0 for more information about using the autograder.

Question 1 Reflex Agent

Improve the ReflexAgent in multiAgents.py to play respectably. The provided reflex agent code provides some helpful examples of methods that query the GameState for information. A capable reflex agent will have to consider both food locations and ghost locations to perform well. Your agent should easily and reliably clear the testClassic layout:

Question 2 Minimax

Now you will write an adversarial search agent in the provided MinimaxAgent class stub in multiAgents.py. Your minimax agent should work with any number of ghosts, so you’ll have to write an algorithm that is slightly more general than what you’ve previously seen in lecture. In particular, your minimax tree will have multiple min layers (one for each ghost) for every max layer.

Your code should also expand the game tree to an arbitrary depth. Score the leaves of your minimax tree with the supplied self.evaluationFunction, which defaults to scoreEvaluationFunction. MinimaxAgent extends MultiAgentSearchAgent, which gives access to self.depth and self.evaluationFunction. Make sure your minimax code makes reference to these two variables where appropriate as these variables are populated in response to command line options.

Question 3 Alpha-Beta Pruning

Make a new agent that uses alpha-beta pruning to more efficiently explore the minimax tree, in AlphaBetaAgent. Again, your algorithm will be slightly more general than the pseudocode from lecture, so part of the challenge is to extend the alpha-beta pruning logic appropriately to multiple minimizer agents.

You should see a speed-up (perhaps depth 3 alpha-beta will run as fast as depth 2 minimax). Ideally, depth 3 on smallClassic should run in just a few seconds per move or faster.

Question 4 Expectimax

Minimax and alpha-beta are great, but they both assume that you are playing against an adversary who makes optimal decisions. As anyone who has ever won tic-tac-toe can tell you, this is not always the case. In this question, you will implement the ExpectimaxAgent, which is useful for modeling probabilistic behavior of agents who may make suboptimal choices.

As with the search and constraint satisfaction problems covered so far in this class, the beauty of these algorithms is their general applicability. To expedite your own development, we’ve supplied some test cases based on generic trees. You can debug your implementation on small game trees using the command:

Question 5 Evaluation Function

Write a better evaluation function for Pacman in the provided function betterEvaluationFunction. The evaluation function should evaluate states, rather than actions like your reflex agent evaluation function did. With a depth 2 search, your evaluation function should clear the smallClassic layout with one random ghost more than half the time and still run at a reasonable rate (to get full credit, Pacman should be averaging around 1000 points when he’s winning).

Unit 4 : part 2 due 07.23.2022

Wk3 6051 assgn1 | SOCW 6051 – Diversity, Human Rights, and Social Justice | Walden University

He who passes the sentence should swing the sword”: Justice, Honor and Death
A man’s code of justice defines who he is. What is the relationship between honor and justice? How does the caste system instill its own code of justice?How are the events of the book representative of a moral value, or lack thereof? Compare the attitudes towards death and justice from different characters and cultures.

2.  “Power is a trick. A shadow on the wall.” Meditation on power: Do the ends justify the means? ……. ….

Some organizing principles of power are heredity, money, war, cunning and honor. If rule must come with the consent of the governed, then how is legitimacy judged? By what means is ultimate power attained? Why do people consent to obey?  Consider the symbol of the dragon and Danerys transcendence. You may compare to other power structures discussed in the course.

3. Ned vs Dany
Compare the decisions of Eddard and Danerys as their respective story lines climax. Contrast Ned’s sense of honor and Danys sense of decency. Are more modern sentiments familiar to our time represented by certain cultures or characters? Who has a gentle or merciful heart?  How do they fare in the story?

4. “Cripples, bastards and broken things”
Describe the roles of characters who fall on the outskirts of the nobility, such as Tyrion and Jon Snow. How do they define themselves? In what ways do they cope with their troubles in comparison to those of full nobility?

The book is Game of Thrones: you have to chose from one of those topic and write about ……. ….

II. Issue Analysis A. Select a criminological theory that informs your understanding of the issue, justifying your selection. B. Identify factors contributing to the issue, justifying your identifications with the selected criminological theory. In other words, what are the factors contributing to the issue? How is the identification of these factors supported by the selected theory? C. Describe how the issue impacts the criminal justice system, providing specific examples of how the branches of the criminal justice system are affected by the issue. For example, how does the issue impact law enforcement, the courts, and corrections? D. Describe how the issue impacts the general public, providing specific examples. Does the issue affect specific populations? E. Determine the key stakeholders of the issue, identifying both the key people impacted by the issue, as well as those with the power to influence change. Justify your selections. F. Describe strategies that are currently employed to address the issue. These strategies may be used in the community with the issue, or in a different community with the same issue. G. Assess the effectiveness of the previously identified strategies in addressing the issue, justifying your assessment. How effectively do the strategies combat the issue, and where are there gaps? H. How does your previous assessment impact the specific branches of the criminal justice system? Justify your response. In which branches are the strategies most and least effective?

TOPIC – EXCESSIVE USE OF FORCE 

REFERENCE 3-2 ATTACHMENT