AI-03 · Undergraduate · ENGLISH · PLANNED
Begin with a baseline, trace a model, measure errors and improve it.
Study time is an initial activity estimate, excluding extra reading, revision and extended laboratory or project work. Videos and question bodies have not been produced by adding this outline.
Complete these courses or demonstrate equivalent knowledge. These are recommended prerequisites, not enforced enrolment locks.
YouTube playlist: ARK Artificial Intelligence | Search, Logic and Knowledge Representation
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Sign in to save this learning pathPractical outcome: Find a path in a small maze and explain the search cost.
Explain state spaces using a visual and one worked real-world example. Context: Find a path in a small maze and explain the search cost. End with a prediction or check-for-understanding.
Explain successor functions using a visual and one worked real-world example. Context: Find a path in a small maze and explain the search cost. End with a prediction or check-for-understanding.
Explain breadth-first search using a visual and one worked real-world example. Context: Find a path in a small maze and explain the search cost. End with a prediction or check-for-understanding.
Explain depth-first search using a visual and one worked real-world example. Context: Find a path in a small maze and explain the search cost. End with a prediction or check-for-understanding.
Use the first concepts from this section. Find a path in a small maze and explain the search cost. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain uniform-cost search using a visual and one worked real-world example. Context: Find a path in a small maze and explain the search cost. End with a prediction or check-for-understanding.
Explain heuristics using a visual and one worked real-world example. Context: Find a path in a small maze and explain the search cost. End with a prediction or check-for-understanding.
Explain a-star using a visual and one worked real-world example. Context: Find a path in a small maze and explain the search cost. End with a prediction or check-for-understanding.
Explain admissibility using a visual and one worked real-world example. Context: Find a path in a small maze and explain the search cost. End with a prediction or check-for-understanding.
Find a path in a small maze and explain the search cost. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering State spaces, Successor functions, Breadth-first search and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Encode and check a fictional timetable's constraints.
Explain propositional logic using a visual and one worked real-world example. Context: Encode and check a fictional timetable's constraints. End with a prediction or check-for-understanding.
Explain entailment using a visual and one worked real-world example. Context: Encode and check a fictional timetable's constraints. End with a prediction or check-for-understanding.
Explain inference rules using a visual and one worked real-world example. Context: Encode and check a fictional timetable's constraints. End with a prediction or check-for-understanding.
Explain first-order logic using a visual and one worked real-world example. Context: Encode and check a fictional timetable's constraints. End with a prediction or check-for-understanding.
Use the first concepts from this section. Encode and check a fictional timetable's constraints. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain unification using a visual and one worked real-world example. Context: Encode and check a fictional timetable's constraints. End with a prediction or check-for-understanding.
Explain constraint satisfaction using a visual and one worked real-world example. Context: Encode and check a fictional timetable's constraints. End with a prediction or check-for-understanding.
Explain backtracking search using a visual and one worked real-world example. Context: Encode and check a fictional timetable's constraints. End with a prediction or check-for-understanding.
Explain constraint propagation using a visual and one worked real-world example. Context: Encode and check a fictional timetable's constraints. End with a prediction or check-for-understanding.
Encode and check a fictional timetable's constraints. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Propositional logic, Entailment, Inference rules and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Practical outcome: Choose actions in a small deterministic game model.
Explain minimax using a visual and one worked real-world example. Context: Choose actions in a small deterministic game model. End with a prediction or check-for-understanding.
Explain alpha-beta pruning using a visual and one worked real-world example. Context: Choose actions in a small deterministic game model. End with a prediction or check-for-understanding.
Explain expectimax using a visual and one worked real-world example. Context: Choose actions in a small deterministic game model. End with a prediction or check-for-understanding.
Explain planning states using a visual and one worked real-world example. Context: Choose actions in a small deterministic game model. End with a prediction or check-for-understanding.
Use the first concepts from this section. Choose actions in a small deterministic game model. Produce a labelled diagram, trace or calculation; explain each step and check one changed input.
3–6 questions on the preceding first-half concepts only: recall, visual interpretation, application and one plausible misconception. Explain each answer after submission.
Explain action preconditions using a visual and one worked real-world example. Context: Choose actions in a small deterministic game model. End with a prediction or check-for-understanding.
Explain planning graphs using a visual and one worked real-world example. Context: Choose actions in a small deterministic game model. End with a prediction or check-for-understanding.
Explain knowledge graphs using a visual and one worked real-world example. Context: Choose actions in a small deterministic game model. End with a prediction or check-for-understanding.
Explain reasoning under incomplete information using a visual and one worked real-world example. Context: Choose actions in a small deterministic game model. End with a prediction or check-for-understanding.
Choose actions in a small deterministic game model. Work without the example, compare two cases, record assumptions and limitations, then self-review correctness, reasoning and clarity.
6–12 questions covering Minimax, Alpha-beta pruning, Expectimax and the remaining section concepts, with cumulative retrieval from earlier sections. Include a new transfer problem; 70% unlocks progression.
Combine all three sections: Choose actions in a small deterministic game model. Submit an original worked artifact, evidence or tests, assumptions and a reflection. Rubric: correctness, method, practical usefulness, reproducibility and communication. Self-reviewed, not certified.
6–12 balanced questions sampling every section of Search, Logic and Knowledge Representation, including interpretation, calculation or reasoning, a misconception and a new application. Passing is 70%; review errors before retrying.
Data experiments, evaluations, model tracing and reproducible projects. Guided work leads to an independent attempt, with a diagram, explanation, trace, calculation or tested artifact. Question bodies are generated when opened in your private learning path. Programming submissions are stored as text; the platform does not execute submitted code.
Use original or appropriately licensed datasets. Practical science activities use models or supplied data unless a qualified supervisor has reviewed a physical experiment.
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