Highlights
1.1 QA.1 BUILDING YOUR WORLD
Create a 2D world that will play host to a game to be played by agents. Provide context for the mechanics of the world and the conditions under which the game is complete. The intent of this exercise is for you to demonstrate your understanding of how a problem can be modeled abstractly. To this end, implement three different agents, providing a PEAS description of each and a rationale for their inclusion.
• Specify, compare and contrast, the advantages and disadvantages of each agent type.
• Demonstrate each agents performance in the 2-dimensional world.
• Discuss, and evaluate, the agents suitability in worlds of arbitrary sizes.
• NB: Support the above with empirical validation in a tabular format (multiple runs, generated results, for each point).
The game should be implemented in Python, you may use any of the libraries made avail-able from the AIMA python repository but they must be clearly referenced, your own code and contributions should be clearly highlighted. Write a clear and concise description of the agent-based game. The purpose of this is to articulate an understanding of the underlying concepts being implemented both from a theoretical and practical perspective.
1.2 QA.2 SEARCHING YOUR WORLD
• Formulate a well defined problem statement and identify a goal-state under which your game is complete. Why is this important to search? As part of your solution you
should be including the initial state, the set of actions, the transition model, a goal test function and a path cost function.
• Select two uninformed search techniques and discuss their appropriateness to your world under appropriate headings for search.
• Implement the two uninformed search techniques discussed for your world. Discuss the results.
• NB: Support this with empirical validation in a tabular format.
Write a clear and concise report detailing the search techniques utility with regards your agent-based game. The purpose of this is to articulate an understanding of the underlying concepts being implemented both from a theoretical and practical perspective.
1.3 QA.3 FORWARD-CHAINING AND BACKWARD-CHAINING
Forward-Chaining and Backward-Chaining introduce the capacity for inference in an environment. How does this benefit the operation of an agent, in particular in your world? Provide a short critical analysis of both approaches. Thereafter demonstrate their applicability by utilizing them in your world (Note: if this requires a bending of the rules in your world that is ok - the important part is the discussion/analysis).
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