Slides

This page contains slides from David L. Poole and Alan K. Mackworth, Artificial Intelligence: foundations of computational agents, 3rd edition, Cambridge University Press, 2023.

These slides are in PDF format and can be read using the free acrobat reader or with recent versions of Ghostscript.

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.

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.

Instructors can get sources of all of the slides (including all figures and in-class clicker questions) from the instructor resources at Cambridge University Press (see CUP FAQ for access instructions). They were written using the LaTeX beamer class (included in standard (La)TeX distributions such as TeX Live, MiKTeX, and MacTeX). We plan to release new versions every April, August and December for the foreseeable future.

Chapter 1: Artificial Intelligence and Agents

Lecture 1.1: Introduction to artificial intelligence and the role of agents (slides, handout)
Lecture 1.2: Dimensions of complexity (slides, handout)
Lecture 1.3: Applications domains (slides, handout)
Lecture 1.4: Introduction to knowledge representation (slides, handout)

Chapter 2: Agent Architectures and Hierarchical Control

Lecture 2.1: Agent architecture and control (slides, handout)
Lecture 2.2: Hierarchical control (slides, handout)
Lecture 2.3: Social Impact: moral machines (slides, handout)

Chapter 3: Searching for Solutions

Lecture 3.1: Searching and graphs (slides, handout)
Lecture 3.2: Uninformed search strategies (slides, handout)
Lecture 3.3: Heuristic search, including best-first search and A* search (slides, handout)
Lecture 3.4: Refinements to search strategies, including loop checking, multiple-path pruning, bidirectional search, and dynamic programming (slides, handout)
Lecture 3.5: Bounded search, iterative deepening, branch and bound (slides, handout)

Chapter 4: Reasoning with Constraints

Lecture 4.1: Posing a constraint satisfaction problem (slides, handout)
Lecture 4.2: Systematic methods: search and arc consistency (slides, handout)
Lecture 4.3: Local search, randomized algorithms and genetic algorithms for solving CSPs (slides, handout)
Lecture 4.4: Variable elimination (slides, handout)

Chapters 5: Propositions and Inference

Lecture 5.1: Propositional reasoning (slides, handout)
Lecture 5.2: Propositional Definite Clauses and proof procedures (slides, handout)
Lecture 5.3: Ask-the-user and knowledge-level debugging (slides, handout)
Lecture 5.4: Proof by contradiction, conflicts, and consistency-base diagnosis (slides, handout)
Lecture 5.5: Complete knowledge assumption and negation as failure (slides, handout)
Lecture 5.6: Propositional causal reasoning (slides, handout)

Chapter 6: Planning with Certainty

Lecture 6.1: Action semantics and representations (slides, handout)
Lecture 6.2: Forward planning (slides, handout)
Lecture 6.3: Regression planning (slides, handout)
Lecture 6.4: Constraint-based planning (slides, handout)

Chapter 7: Supervised Machine Learning

Lecture 7.1: Introduction to machine learning and the issues facing any learning algorithm. (slides, handout)
Lecture 7.2: Simplest cases of learning (slides, handout)
Lecture 7.3: Basic models of supervised learning (decision trees, linear classifiers) (slides, handout)
Lecture 7.4: Handling overfitting (regularization and cross validation) (slides, handout)
Lecture 7.5: Ensembles and gradient-boosted trees. (slides, handout)

Chapter 8: Neural Networks and Deep Learning

Lecture 8.1: Feed-forward Neural Networks (slides, handout)
Lecture 8.2: Training NNs. (slides, handout)
Lecture 8.3: Convolutional neural networks. (slides, handout)
Lecture 8.4: Neural models for sequences (slides, handout)
Lecture 8.5: Large language models and social issues (slides, handout)

Chapter 9: Reasoning with Uncertainty

Lecture 9.1: Probability (slides, handout)
Lecture 9.2: Conditional independence and belief networks (slides, handout)
Lecture 9.3: Properties of conditional independence (slides, handout)
Lecture 9.4: Representing conditional probabilities (slides, handout)
Lecture 9.5: Exact inference using recursive conditioning (slides, handout)
Lecture 9.6: Exact inference using variable elimination (slides, handout)
Lecture 9.7: Probabilistic reasoning and time; Markov models (slides, handout)
Lecture 9.8: Approximate inference using stochastic simulation (slides, handout)

Chapter 10: Learning with Uncertainty

Lecture 10.1: Baayesian Learning (slides, handout)
Lecture 10.2: Unsupervised learning (slides, handout)
Lecture 10.3: Learning belief networks (slides, handout)

Chapter 11: Causality

Lecture 11.1: Causal models (slides, handout)
Lecture 11.2: Missing data (slides, handout)
Lecture 11.3: Inferring causality (slides, handout)
Lecture 11.4: Counterfactual reasoning (slides, handout)

Chapter 12: Planning with Uncertainty

Lecture 12.1: Utility theory (slides, handout)
Lecture 12.2: Decision theory and finite stage decision networks (slides, handout)
Lecture 12.3: Markov decision processes (slides, handout)
Lecture 12.4: Social issues: utility assessment (slides, handout)

Chapter 13: Reinforcement Learning

Lecture 13.1: Reinforcement learning: basic algorithms (slides, handout)
Lecture 13.2: Exploration and exploitation (slides, handout)
Lecture 13.3: On-policy, model-based RL, and RL with generalization (slides, handout)

Chapter 14: Multiagent Systems

Lecture 14.1: Representations of games (slides, handout)
Lecture 14.2: Finding equilibria (slides, handout)
Lecture 14.3: Group decision making and mechanism design (slides, handout)

Chapter 15: Individuals and Relations

Lecture 15.1: Datalog: syntax and semantics (slides, handout)
Lecture 15.2: Proof procedures with variables (slides, handout)
Lecture 15.3: Complete knowledge assumption and negation-as-failure (slides, handout)
Lecture 15.4: Logic for natural language processing (slides, handout)

Chapter 16: Knowledge Graphs and Ontologies

Lecture 16.1: Flexible representations, knowledge graphs (slides, handout)
Lecture 16.2: Classes and properties (slides, handout)
Lecture 16.2: Ontologies and knowledge sharing (slides, handout)

Chapter 17: Relational Learning and Probabilistic Reasoning

Lecture 17.1: From knowledge graphs to random variables and features (slides, handout)
Lecture 17.2: Embedding-based models (slides, handout)
Lecture 17.3: Relational probabilistic podels (slides, handout)
Lecture 17.4: Some specific relational probabilistic models; existence and identity uncertainty (slides, handout)

Chapter 18: The Social Impact of Artificial Intelligence

Lecture 18.1: The social impact of AI; digital economy, values and biases (slides, handout)
Lecture 18.2: Human-centered AI (slides, handout)
Lecture 18.3: Work and automation (slides, handout)
Lecture 18.4: Sustainability (slides, handout)
Lecture 18.5: Ethics (slides, handout)
Lecture 18.6: Governance and regulation (slides, handout)

Chapter 19: Retrospect and Prospect

Lecture 19.1: Review: AI and Agents (slides, handout)
Lecture 19.2: Deploying AI (slides, handout)
Lecture 19.3: Dimensions of complexity revisited (slides, handout)
Lecture 19.4: Looking ahead (slides, handout)

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Last updated 2023-09-06, David L. Poole, Alan K. Mackworth

Artificial Intelligence 3e