C951 - Introduction to Artificial Intelligence

(WGU-C951) / ISBN : 978-1-61691-372-4
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1

Welcome to Introduction to Artificial Intelligence

  • Learning Resources
  • Pacing Guide
2

Introduction

  • What Is AI?
  • The Foundations of Artificial Intelligence
  • The History of Artificial Intelligence
  • The State of the Art
  • Summary, Bibliographical and Historical Notes, Exercises
3

Intelligent Agents

  • Agents and Environments
  • Good Behavior: The Concept of Rationality
  • The Nature of Environments
  • The Structure of Agents
  • Summary, Bibliographical and Historical Notes, Exercises
4

Solving Problems by Searching

  • Problem-Solving Agents
  • Example Problems
  • Searching for Solutions
  • Uninformed Search Strategies
  • Informed (Heuristic) Search Strategies
  • Heuristic Functions
  • Finding Relevant Code
  • Summary, Bibliographical and Historical Notes, Exercises
5

Beyond Classical Search

  • Local Search Algorithms and Optimization Problems
  • Local Search in Continuous Spaces
  • Searching with Nondeterministic Actions
  • Searching with Partial Observations
  • Online Search Agents and Unknown Environments
  • Summary, Bibliographical and Historical Notes, Exercises
6

Adversarial Search

  • Games
  • Optimal Decisions in Games
  • Alpha–Beta Pruning
  • Imperfect Real-Time Decisions
  • Stochastic Games
  • Partially Observable Games
  • State-of-the-Art Game Programs
  • Alternative Approaches
  • Summary, Bibliographical and Historical Notes, Exercises
7

Constraint Satisfaction Problems

  • Defining Constraint Satisfaction Problems
  • Constraint Propagation: Inference in CSPs
  • Backtracking Search for CSPs
  • Local Search for CSPs
  • The Structure of Problems
  • Summary, Bibliographical and Historical Notes, Exercises
8

Logical Agents

  • Knowledge-Based Agents
  • The Wumpus World
  • Logic
  • Propositional Logic: A Very Simple Logic
  • Propositional Theorem Proving
  • Effective Propositional Model Checking
  • Agents Based on Propositional Logic
  • Summary, Bibliographical and Historical Notes, Exercises
9

First-Order Logic

  • Representation Revisited
  • Syntax and Semantics of First-Order Logic
  • Using First-Order Logic
  • Knowledge Engineering in First-Order Logic
  • Summary, Bibliographical and Historical Notes, Exercises
10

Inference in First-Order Logic

  • Propositional vs. First-Order Inference
  • Unification and Lifting
  • Forward Chaining
  • Backward Chaining
  • Resolution
  • Summary, Bibliographical and Historical Notes, Exercises
11

Classical Planning (Supplemental)

  • Definition of Classical Planning
  • Algorithms for Planning as State-Space Search
  • Planning Graphs
  • Other Classical Planning Approaches
  • Analysis of Planning Approaches
  • Summary, Bibliographical and Historical Notes, Exercises
12

Planning and Acting in the Real World

  • Time, Schedules, and Resources
  • Hierarchical Planning
  • Planning and Acting in Nondeterministic Domains
  • Multiagent Planning
  • Summary, Bibliographical and Historical Notes, and Exercise
13

Focus: Chatbots

  • What are Chatbots?
  • Pandorabots and the AIML Language
  • Creating a Chatbot
14

Knowledge Representation

  • Ontological Engineering
  • Categories and Objects
  • Events
  • Mental Events and Mental Objects
  • Reasoning Systems for Categories
  • Reasoning with Default Information
  • The Internet Shopping World
  • Summary, Bibliographical and Historical Notes, Exercises
15

Quantifying Uncertainty

  • Acting under Uncertainty
  • Basic Probability Notation
  • Inference Using Full Joint Distributions
  • Independence
  • Bayes' Rule and Its Use
  • The Wumpus World Revisited
  • Summary, Bibliographical and Historical Notes, Exercises
16

Probabilistic Reasoning

  • Representing Knowledge in an Uncertain Domain
  • The Semantics of Bayesian Networks
  • Efficient Representation of Conditional Distributions
  • Exact Inference in Bayesian Networks
  • Approximate Inference in Bayesian Networks
  • Relational and First-Order Probability Models
  • Other Approaches to Uncertain Reasoning
  • Summary, Bibliographical and Historical Notes, Exercises
17

Probabilistic Reasoning over Time (Supplemental)

  • Time and Uncertainty
  • Inference in Temporal Models
  • Hidden Markov Models
  • Kalman Filters
  • Dynamic Bayesian Networks
  • Keeping Track of Many Objects
  • Summary, Bibliographical and Historical Notes, Exercises
18

Making Simple Decisions (Supplemental)

  • Combining Beliefs and Desires under Uncertainty
  • The Basis of Utility Theory
  • Utility Functions
  • Multiattribute Utility Functions
  • Decision Networks
  • The Value of Information
  • Decision-Theoretic Expert Systems
  • Summary, Bibliographical and Historical Notes, Exercises
19

Making Complex Decisions (Supplemental)

  • Sequential Decision Problems
  • Value Iteration
  • Policy Iteration
  • Partially Observable MDPs
  • Decisions with Multiple Agents: Game Theory
  • Mechanism Design
  • Summary, Bibliographical and Historical Notes, Exercises
20

Learning from Examples

  • Forms of Learning
  • Supervised Learning
  • Learning Decision Trees
  • Evaluating and Choosing the Best Hypothesis
  • The Theory of Learning
  • Regression and Classification with Linear Models
  • Artificial Neural Networks
  • Nonparametric Models
  • Support Vector Machines
  • Ensemble Learning
  • Practical Machine Learning
  • Summary, Bibliographical and Historical Notes, Exercises
21

Knowledge in Learning

  • A Logical Formulation of Learning
  • Knowledge in Learning
  • Explanation-Based Learning
  • Learning Using Relevance Information
  • Inductive Logic Programming
  • Feature Space Engineering
  • Data Preparation and Preprocessing
  • Summary, Bibliographical and Historical Notes, Exercises
22

Learning Probabilistic Models

  • Statistical Learning
  • Learning with Complete Data
  • Learning with Hidden Variables: The EM Algorithm
  • Summary, Bibliographical and Historical Notes, Exercises
23

Reinforcement Learning

  • Introduction
  • Passive Reinforcement Learning
  • Active Reinforcement Learning
  • Generalization in Reinforcement Learning
  • Policy Search
  • Applications of Reinforcement Learning
  • Summary, Bibliographical and Historical Notes, Exercises
24

Natural Language Processing (Supplemental)

  • Language Models
  • Text Classification
  • Information Retrieval
  • Information Extraction
  • Summary, Bibliographical and Historical Notes, Exercises
25

Natural Language for Communication (Supplemental)

  • Phrase Structure Grammars
  • Syntactic Analysis (Parsing)
  • Augmented Grammars and Semantic Interpretation
  • Machine Translation
  • Speech Recognition
  • Summary, Bibliographical and Historical Notes, Exercises
26

Perception (Supplemental)

  • Image Formation
  • Early Image-Processing Operations
  • Object Recognition by Appearance
  • Reconstructing the 3D World
  • Object Recognition from Structural Information
  • Using Vision
  • Summary, Bibliographical and Historical Notes, Exercises
27

Robotics

  • Introduction
  • Robot Hardware
  • Robotic Perception
  • Planning to Move
  • Planning Uncertain Movements
  • Moving
  • Robotic Software Architectures
  • Application Domains
  • Summary, Bibliographical and Historical Notes, Exercises
28

Focus: Robotics and Feature Engineering

  • Coppelia Robotics
  • Robotics: Feature Engineering
29

Philosophical Foundations

  • Weak AI: Can Machines Act Intelligently?
  • Strong AI: Can Machines Really Think?
  • The Ethics and Risks of Developing Artificial Intelligence
  • Summary, Bibliographical and Historical Notes, Exercises
30

AI: The Present and Future

  • Agent Components
  • Agent Architectures
  • Are We Going in the Right Direction?
  • What If AI Does Succeed?
A

Appendix A: Mathematical background

  • A.1 Complexity Analysis and O() Notation
  • A.2. Vectors, Matrices, and Linear Algebra
  • A.3 Probability Distributions
B

Appendix B: Notes on Languages and Algorithms

  • B.1 Defining Languages with Backus–Naur Form (BNF)
  • B.2 Describing Algorithms with Pseudocode

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