F29AI: Artificial Intelligence and Intelligent Agents - Computer Science Assessment Answer

F29AI

Computer Science Assessment Task

Quality of report and solution 
The quality mark will primarily be based on your 2-page written report which will outline your solution, design decisions, and additional domain feature. Your report should be well prepared and presented, with good English usage and grammar, and appropriate visual appearance. Your PDDL code will also be checked and the report will be used to help supplement the code in understanding your overall approach to the problem and the quality of your solution. Usual program quality criteria (e.g., use of whitespace, comments, naming conventions, etc.) will apply here to assess the readability of the code.

Correctness of solution
The correctness mark will primarily be based on the PDDL code you have supplied and how correctly it implements the coursework specification. You may submit a single PDDL domain file and multiple PDDL problem files in a single zip/tar file. The supplied files should provide coverage of the various scenario requirements and demonstrate correct behaviour. The marker will test a selection of the specified PDDL files for plan correctness. If you have developed your solution using a planner not mentioned in class, this should be clearly indicated in your report; however, the PDDL files will only be tested on editor.planning.domains, FF, or Fast Downward.
Correctness will be assessed not only against the coursework requirements but also with respect to the specific implemented solution. (I.e., non-obvious or incorrect/missing action preconditions or effects may lead to strange plan output and mark deductions.) Up to 2 marks will be allocated for the correctness of the additional feature.

This coursework is meant to contribute to the following high-level aims for F29AI:

• To introduce the fundamental concepts and techniques of AI, including planning, search, and knowledge representation.
• To introduce the scope, subfields and applications of AI, including autonomous agents.
• To develop skills in AI programming in an appropriate language.

It is also meant to contribute to the following specific learning objectives for the course:

• Critical understanding of traditional AI problem solving and knowledge representation methods.
• Use of knowledge representation techniques (such as predicate logic).
• Critical understanding of different systematic and heuristic search techniques.
• Practice in expressing problems in terms of state-space search.
• Broad knowledge and understanding of the subfields and applications of AI.
• Detailed knowledge of one subfield of AI (e.g., planning) and ability to apply its formalisms and representations to small problems.
• Detailed understanding of different approaches to autonomous agent and robot architectures, and the ability to critically evaluate their advantages and disadvantages in different contexts.
• Practice in the implementation of simple AI systems using a suitable language.
• Identification, representation and solution of problems.
• Research skills and report writing.

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