Apply state space representation to model a problem

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Reference no: EM133996786

Artificial Intelligence Fundamentals

Assessment - Case-based Response

Devise appropriate representations for state space search and game playing to solve practical problems.

Purpose
Apply state space representation to model a problem by defining states, operators, and goal conditions.
Demonstrate understanding of search strategies by manually tracing how algorithms explore a state space to reach a solution.
Analyse and justify solution paths to explain how search methods generate outcomes for a given problem.
Specific Instructions To complete this assessment, you will prepare an analysis document of approximately 800 words addressing the questions provided in the task description.
Key tip: Students who perform well in this assessment demonstrate a clear understanding of the search concepts and state space modelling techniques taught in the subject and are able to apply these concepts directly to the given problem scenario.
You may refer to the lecture materials and examples discussed in class when completing this assessment. You are not required to use additional external sources. If you choose to include external references, ensure they are relevant to the concepts being discussed and are properly cited.
This assessment must be completed individually. Collaboration, sharing answers, or submitting work that is not your own is not permitted.
You may not use Python or any programming code for this task.
Your response must clearly explain the reasoning and steps used in your analysis.
Simply providing the final path without showing the OPEN and CLOSED trace will receive minimal marks.
If the heuristic design or justification is weak or unclear, marks will be deducted.

Case Study: Smart Delivery Robot (Constrained Navigation)

A delivery robot operates inside a university building consisting of 8 rooms labelled: R1, R2, R3, R4, R5, R6, R7, R8
The map below represents the layout of rooms in the university building. All movements between connected rooms are bidirectional. The robot starts in room R1 (Initial state) and must reach room R8 (Goal state). The robot may enter room R6 only once due to the low battery constraint.
Constraints:

The robot has a low battery mode:
It may NOT enter R6 more than once.
If it revisits R6, it shuts down.

Questions
State Space Formalisation (3 marks) You must:
Define the state representation formally
Define the initial state
Define the goal test
Define the permissible operators
Clearly explain how the constraint affects the state definition
Rule:
A simple state representation such as at(Rx) may NOT be sufficient. You must justify whether additional information must be encoded.
Breadth-First Search Trace (2 marks) Using your state representation:
Show OPEN and CLOSED lists step-by-step
Stop when the first goal is found
Return the solution path
You are not required to expand the full tree, only until the first solution. No AI shortcuts — Only authentic assignment help from real expert tutors.
Heuristic Design and Greedy Search Trace (3 marks) Design a heuristic function:
h(n) = ?
Your heuristic must:
Be clearly defined mathematically
Be domain-informed (not random)

Be justified Then:
Perform a Greedy Best-First Search
Show OPEN ordering at each step
Return the solution path
You do NOT need to perform A* in this assignment.
Critical Reflection (2 mark)
In 150-200 words, discuss:
Why BFS may generate unnecessary nodes in this problem
Whether your heuristic guarantees optimality
Under what modification this problem would require Uniform Cost Search

Research and references
Please use APA 7th edition as your referencing style. For more information, see the Academic Referencing Tool of the Library.

Reference no: EM133996786

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