Python code for Artificial Intelligence: Foundations of Computational Agents (Updated)

Contents
1 Python for Artificial Intelligence
	1.1 Why Python?
	1.2 Getting Python
	1.3  Running Python
	1.4 Pitfalls
	1.5 Features of Python
		1.5.1 f-strings
		1.5.2 Lists, Tuples, Sets, Dictionaries and Comprehensions
		1.5.3 Functions as first-class objects
		1.5.4 Generators
	1.6 Useful Libraries
		1.6.1 Timing Code
		1.6.2 Plotting: Matplotlib
	1.7 Utilities
		1.7.1 Display
		1.7.2 Argmax
		1.7.3 Probability
	1.8 Testing Code
2 Agent Architectures and Hierarchical Control
	2.1 Representing Agents and Environments
	2.2 Paper buying agent and environment
		2.2.1 The Environment
		2.2.2 The Agent
		2.2.3 Plotting
	2.3 Hierarchical Controller
		2.3.1 Environment
		2.3.2 Body
		2.3.3 Middle Layer
		2.3.4 Top Layer
		2.3.5 Plotting
3 Searching for Solutions
	3.1 Representing Search Problems
		3.1.1 Explicit Representation of Search Graph
		3.1.2 Paths
		3.1.3 Example Search Problems
	3.2 Generic Searcher and Variants
		3.2.1 Searcher
		3.2.2 GUI for Tracing Search
		3.2.3 Frontier as a Priority Queue
		3.2.4 A* Search
		3.2.5 Multiple Path Pruning
	3.3 Branch-and-bound Search
4 Reasoning with Constraints
	4.1 Constraint Satisfaction Problems
		4.1.1 Variables
		4.1.2 Constraints
		4.1.3 CSPs
		4.1.4 Examples
	4.2 A Simple Depth-first Solver
	4.3 Converting CSPs to Search Problems
	4.4 Consistency Algorithms
		4.4.1 Direct Implementation of Domain Splitting
		4.4.2 Consistency GUI
		4.4.3 Domain Splitting as an interface to graph searching
	4.5 Solving CSPs using Stochastic Local Search
		4.5.1 Any-conflict
		4.5.2 Two-Stage Choice
		4.5.3 Updatable Priority Queues
		4.5.4 Plotting Run-Time Distributions
		4.5.5 Testing
	4.6 Discrete Optimization
		4.6.1 Branch-and-bound Search
5 Propositions and Inference
	5.1 Representing Knowledge Bases
	5.2 Bottom-up Proofs (with askables)
	5.3 Top-down Proofs (with askables)
	5.4 Debugging and Explanation
	5.5 Assumables
	5.6 Negation-as-failure
6 Deterministic Planning
	6.1 Representing Actions and Planning Problems
		6.1.1 Robot Delivery Domain
		6.1.2 Blocks World
	6.2 Forward Planning
		6.2.1 Defining Heuristics for a Planner
	6.3 Regression Planning
		6.3.1 Defining Heuristics for a Regression Planner
	6.4 Planning as a CSP
	6.5 Partial-Order Planning
7 Supervised Machine Learning
	7.1 Representations of Data and Predictions
		7.1.1 Creating Boolean Conditions from Features
		7.1.2 Evaluating Predictions
		7.1.3 Creating Test and Training Sets
		7.1.4 Importing Data From File
		7.1.5 Augmented Features
	7.2 Generic Learner Interface
	7.3 Learning With No Input Features
		7.3.1 Evaluation
	7.4 Decision Tree Learning
	7.5 Cross Validation and Parameter Tuning
	7.6 Linear Regression and Classification
	7.7 Boosting
		7.7.1 Gradient Tree Boosting
8 Neural Networks and Deep Learning
	8.1 Layers
		8.1.1 Linear Layer
		8.1.2 ReLU Layer
		8.1.3 Sigmoid Layer
	8.2 Feedforward Networks
	8.3 Improved Optimization
		8.3.1 Momentum
		8.3.2 RMS-Prop
	8.4 Dropout
		8.4.1 Examples
9 Reasoning with Uncertainty
	9.1 Representing Probabilistic Models
	9.2 Representing Factors
	9.3 Conditional Probability Distributions
		9.3.1 Logistic Regression
		9.3.2 Noisy-or
		9.3.3 Tabular Factors and Prob
		9.3.4 Decision Tree Representations of Factors
	9.4 Graphical Models
		9.4.1 Showing Belief Networks
		9.4.2 Example Belief Networks
	9.5 Inference Methods
		9.5.1 Showing Posterior Distributions
	9.6 Naive Search
	9.7 Recursive Conditioning
	9.8 Variable Elimination
	9.9 Stochastic Simulation
		9.9.1 Sampling from a discrete distribution
		9.9.2 Sampling Methods for Belief Network Inference
		9.9.3 Rejection Sampling
		9.9.4 Likelihood Weighting
		9.9.5 Particle Filtering
		9.9.6 Examples
		9.9.7 Gibbs Sampling
		9.9.8 Plotting Behavior of Stochastic Simulators
	9.10 Hidden Markov Models
		9.10.1 Exact Filtering for HMMs
		9.10.2 Localization
		9.10.3 Particle Filtering for HMMs
		9.10.4 Generating Examples
	9.11 Dynamic Belief Networks
		9.11.1 Representing Dynamic Belief Networks
		9.11.2 Unrolling DBNs
		9.11.3 DBN Filtering
10 Learning with Uncertainty
	10.1 Bayesian Learning
	10.2 K-means
	10.3 EM
11 Causality
	11.1 Do Questions
	11.2 Counterfactual Example
		11.2.1 Firing Squad Example
12 Planning with Uncertainty
	12.1 Decision Networks
		12.1.1 Example Decision Networks
		12.1.2 Decision Functions
		12.1.3 Recursive Conditioning for decision networks
		12.1.4 Variable elimination for decision networks
	12.2 Markov Decision Processes
		12.2.1 Problem Domains
		12.2.2 Value Iteration
		12.2.3 Value Iteration GUI for Grid Domains
		12.2.4 Asynchronous Value Iteration
13 Reinforcement Learning
	13.1 Representing Agents and Environments
		13.1.1 Environments
		13.1.2 Agents
		13.1.3 Simulating an Environment-Agent Interaction
		13.1.4 Party Environment
		13.1.5 Environment from a Problem Domain
		13.1.6 Monster Game Environment
	13.2 Q Learning
		13.2.1 Exploration Strategies
		13.2.2 Testing Q-learning
	13.3 Q-leaning with Experience Replay
	13.4 Stochastic Policy Learning Agent
	13.5 Model-based Reinforcement Learner
	13.6 Reinforcement Learning with Features
		13.6.1 Representing Features
		13.6.2 Feature-based RL learner
	13.7 GUI for RL
14 Multiagent Systems
	14.1 Minimax
		14.1.1 Creating a two-player game
		14.1.2 Minimax and - Pruning
	14.2 Multiagent Learning
		14.2.1 Simulating Multiagent Interaction with an Environment
		14.2.2 Example Games
		14.2.3 Testing Games and Environments
15 Individuals and Relations
	15.1 Representing Datalog and Logic Programs
	15.2 Unification
	15.3 Knowledge Bases
	15.4 Top-down Proof Procedure
	15.5 Logic Program Example
16 Knowledge Graphs and Ontologies
	16.1 Triple Store
	16.2 Integrating Datalog and Triple Store
17 Relational Learning
	17.1 Collaborative Filtering
		17.1.1 Plotting
		17.1.2 Loading Rating Sets from Files and Websites
		17.1.3 Ratings of top items and users
	17.2 Relational Probabilistic Models
18 Version History
Bibliography
Index