Course - Artificial Intelligence MT24

• Course Webpage
• Lecture Notes
  • 1, Introduction
  • 2, Problem solving and search
  • 3, Problem solving and search
  • 4, Informed search
  • 5, Local search
  • 6, Planning I
  • 7, Planning II
  • 8, Dealing with geometry of physical agents
  • 9, Dealing with geometry of physical agents
  • 10, Introduction to CSPs
  • 11, Solving CSPs
  • 12, Solving CSPs
  • 13, Playing games
  • 14, Playing games
  • 15, Beyond classical search
  • 16, Beyond classical search
• Other courses this term: [[Courses MT24]]^U
• Related textbooks: [[AI - A Modern Approach]]^N

Timetable

• Wednesday 3PM-4PM, Bill Roscoe Lecture Theatre (weeks 1-6)
• Friday 3PM-4PM, Bill Roscoe Lecture Theatre (weeks 1-8)
• Friday 2PM-3PM, Bill Roscoe Lecture Theatre (weeks 7-8)

Notes

• [[Notes - Artificial Intelligence MT24, Problem solving and search]]^U
• [[Notes - Artificial Intelligence MT24, Uninformed search]]^U
• [[Notes - Artificial Intelligence MT24, Informed search]]^U
• [[Notes - Artificial Intelligence MT24, Local search]]^U
• [[Notes - Artificial Intelligence MT24, Planning]]^U
• [[Notes - Artificial Intelligence MT24, Partial order planning]]^U
• [[Notes - Artificial Intelligence MT24, Planning with propositional logic]]^U
• [[Notes - Artificial Intelligence MT24, Robots]]^U
• [[Notes - Artificial Intelligence MT24, Constraint satisfaction problems]]^U
• [[Notes - Artificial Intelligence MT24, Playing perfect information games]]^U
• [[Notes - Artificial Intelligence MT24, Nondeterministic search]]^U
• [[Notes - Artificial Intelligence MT24, Partially observable environments]]^U
• [[Notes - Artificial Intelligence MT24, Online search]]^U

Related notes

Some more notes on the course textbook are available in [[AI - A Modern Approach]]^N, but the following are particularly relevant to this course:

• I: Introduction
  • Chapter 01: [[AIMA - Introduction]]^N
  • Chapter 02: [[AIMA - Intelligent Agents]]^N
• II: Problem Solving
  • Chapter 03: [[AIMA - Solving Problems by Searching]]^N
  • Chapter 04: [[AIMA - Constraint Satisfaction Problems]]^N
  • Chapter 05: [[AIMA - Adversarial Search and Games]]^N
  • Chapter 06: [[AIMA - Search in Complex Environments]]^N
• III: Knowledge, Reasoning, and Planning
  • Chapter 07: [[AIMA - Logical Agents]]^N
  • Chapter 08: [[AIMA - First-Order Logic]]^N
  • Chapter 09: [[AIMA - Inference in First-Order Logic]]^N
  • Chapter 10: [[AIMA - Knowledge Representation]]^N
  • Chapter 11: [[AIMA - Automated Planning]]^N

Problem Sheets

• Sheet 1
• Sheet 2
• From last year:
  • Sheet 1 (covers material from lectures 1, 2, 3, 4).
  • Sheet 2
  • Sheet 3
  • Sheet 4

Practicals

• Base code for practicals
• Practical 1
• Practical 2

To-do List

• Does $IDA^\star$ need a proof of optimality for admissible heuristics or does it follow immediately from the proof of optimality for ordinary $A^\star$?
• Disjoint subproblems in general?
• Proofs for completeness/optimality/time complexity/space complixty of uniformed and informed search algorithms
• Details about building partial order plans
• Understand faster implementation of the sweep algorithm
• Does the Tree CSP algorithm only work when the CSP is binary?
• Better understanding of tree decomposition algorithm, tree decompositions in general
  • There are three requirements, but is there another more basic one
• Example for alpha beta search
• Averaging over clairvoyance (13-14 playing games)