# Slides

This page contains slides from David Poole and Alan Mackworth, Artificial Intelligence: foundations of computational agents, Cambridge University Press, 2010. All lecture materials are copyright © Poole and Mackworth, 2010 and are licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.5 Canada License.

These slides are in PDF format and can be read using the free acrobat reader or with recent versions of Ghostscript. You can get a zip file of the latest distribution of all of the slides that includes the sources. They were written using the LaTeX beamer class. To regenerate them you will also need the figures, and our beamer style file.

We have divided the slides roughly into lectures. The division is largely on logical separation, rather than what can be carried out in one say 50 or 90 minute slot. We have found that one lecture here takes between 30 and 100 minutes to explain in class (augmented with class discussion and more detailed examples). These slides may be more terse than some instructors may prefer to put on slides; they need to be augmented with worked out examples, such as those on our online learning resources. There is another collection of slides for a course based on the book that are made available by Giuseppe Carenini.

We haven't attempted to cover every topic in these lectures; rather, we have attempted to give a deeper view of fewer topics. Revising these slides is an ongoing activity; we would appreciate any feedback you would like to give.

## Chapter 1: Artificial Intelligence and Agents

- Lecture 1 introduction to artificial intelligence and the role of agents.
- Lecture 2 dimensions of models.
- Lecture 3 applications domains.
- Lecture 4 introduction to knowledge representation.

## Chapter 2: Agent Architectures and Hierarchical Control

## Chapter 3: States and Searching

- Lecture 1 introduction to searching and graphs.
- Lecture 2 uninformed search strategies.
- Lecture 3 heuristic search, including best-first search and A* search.
- Lecture 4 refinements to search strategies, including loop checking, multiple-path pruning, bidirectional search, and dynamic programming.
- Lecture 5 bounded search, iterative deepening, branch and bound.

## Chapter 4: Features and Constraints

- Lecture 1 constraint satisfaction problems and consistency algorithms (arc consistency).
- Lecture 2 local search, randomized algorithms and genetic algorithms for solving CSPs.
- Lecture 3 CSPs revisited, including dual representations and variable elimination.

## Chapters 5: Propositions and Inference

- Lecture 1 propositional reasoning and definite clauses.
- Lecture 2 bottom-up proof procedure.
- Lecture 3 top-down proof procedure.
- Lecture 4 ask-the-user and knowledge-level explanation and debugging.
- Lecture 5 proof by contradiction, conflicts, and consistency-base diagnosis.
- Lecture 6 complete knowledge assumption and negation as failure.
- Lecture 7 assumption-based reasoning.
- Lecture 8 default reasoning.
- Lecture 9 evidential and causal reasoning.

## Chapter 6: Reasoning Under Uncertainty

- Lecture 1 probability.
- Lecture 2 conditional independence and belief networks.
- Lecture 3 properties of conditional independence.
- Lecture 4 exact inference using variable elimination.
- Lecture 5 approximate inference using stochastic simulation.
- Lecture 6 probabilistic reasoning and time.

## Chapter 7: Learning: Overview and Supervised Learning

- Lecture 1 introduction to machine learning and the issues facing any learning algorithm.
- Lecture 2 simplest cases of learning
- Lecture 3 basic models of supervised learning (decision trees, linear classifiers, Bayesian classifiers)
- Lecture 4composite models and handling overfitting.
- Lecture 5 case-based reasoning.
- Lecture 6 Bayesian learning.

## Chapter 8: Planning

- Lecture 1 action semantics and representations.
- Lecture 2 forward planning.
- Lecture 3 regression planning.
- Lecture 4 constraint-based planning.

## Chapter 9: Planning Under Uncertainty

- Lecture 1 utility theory.
- Lecture 2 decision theory and finite stage decision networks
- Lecture 3 decision processes.

## Chapter 10: Multi-agent Systems

- Lecture 1 introduction to game theory.

## Chapter 11: Beyond Supervised Learning

- Lecture 1 unsupervised learning.
- Lecture 2 learning belief networks.
- Lecture 3 reinforcement learning.

## Chapter 12: Individuals and Relations

- Lecture 1 the language Datalog
- Lecture 2 model-based semantics of Datalog
- Lecture 3 semantics of variables, and semantic debugging
- Lecture 4 proof procedures with variables
- Lecture 5 natural language interfaces

## Chapter 13: Ontologies and Knowledge-based Systems

- Lecture 1 flexible representations, semantic networks, frames, and property inheritance.
- Lecture 2 knowledge sharing and ontologies.
- Lecture 3 knowledge-based systems architectures and ask-the-user.
- Lecture 4 knowledge-based explanation and debugging.
- Lecture 5 meta-interpreters.
- Lecture 6 more-advanced meta-interpreters.

## Chapter 14: Relational Planning, Learning, and Probabilistic Reasoning

- Lecture 1 relational representations of actions and situation calculus.
- Lecture 2 relational learning.
- Lecture 3 probabilistic relational models.

These slides are licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.5 Canada License. Last updated 2010-02-02, David Poole, poole@cs.ubc.ca