Last modified on 4 March 2014, at 20:30

Artificial Intelligence

Welcome to the Wikibook about Artificial Intelligence.

Book ContentsEdit

The following is a first proposal for a basic layout. This is not yet complete, ideas are welcome. Discuss on the talk page or just add them here.

The book is laid out into 5 sections, with increasing detail and complexity. Each section contains a number of chapters. In addition to regular chapters, there are case-study chapters that investigate full and complex AI systems using several techniques from the regular chapters (as well as perhaps some new ones).

IntroductionEdit

Overview

25% developed  as of 23-Nov-2006 A chronological look at milestones in Artificial Intelligence

General conceptsEdit

0% developed  as of 03-Mar-2014 Semantic Networks
0% developed  as of 27-Nov-2006 Representational perspectives
25% developed  as of 31-Dec-2008 Zero-order logic: Propositional calculus
0% developed  as of 27-Nov-2006 Attributional logic
0% developed  as of 27-Nov-2006 First-order predicate logic
0% developed  as of 27-Nov-2006 Second-order logic
0% developed  as of 27-Nov-2006 Exhaustive search
25% developed  as of 31-Dec-2008 Depth-first search
25% developed  as of 31-Dec-2008 Breadth-first search
75% developed  as of 04-Apr-2009 Finite state automata
0% developed  as of 27-Nov-2006 Heuristic search
25% developed  as of 31-Dec-2008 Best-first search
25% developed  as of 31-Dec-2008 A* Search
25% developed  as of 03-Apr-2009 Bidirectional Search
25% developed  as of 11-Apr-2009 Tabu Search
25% developed  as of 03-Apr-2009 Beam Search
0% developed  as of 27-Nov-2006 Adversarial search
25% developed  as of 29-Mar-2008 Minimax Search
0% developed  as of 21-Feb-2013 Alpha-Beta Pruning
0% developed  as of 21-Feb-2013 Expectimax Search
0% developed  as of 03-Apr-2009 Iterative Improvement
25% developed  as of 03-Apr-2009 Hill Climbing
  • Probability: Describing the basics (philosophical and mathematical) of probability theory inference.

Basic AI topicsEdit

  • Planning, Decision making and Problem Solving: Expanding on the search chapter to show how simple agents and simple intelligent behavior can be created. Examples are solving a puzzle, navigating a small maze (with pits and monsters) or planning a trip to the supermarket.
  • Uncertainty: Introduction to reasoning, planning and decision making with uncertainty.
  • Case Study - Building a (relatively) strong game AI: Building a strong AI for some game (to be chosen) that combines techniques from the planning and uncertainty chapters. This should go deeper than the simplified algorithms that most books describe and actually produce a strong playing AI.
  • Inference in Logic: Backward and Forward chaining, Resolution and Logic Programming.
  • Knowledge Engineering: Ways to describe and store complicated knowledge. Databases, OO concepts, knowledge bases, representing space and time, inference from large datasets, diagnosis system etc.
  • Natural Language: Stuff like Markov models, POS taggers and CFG's.
  • Machine Learning: The basic idea of Machine Learning, (learning based on examples), and explanations of the basic techniques
  • Case Study - Artificial Life: Describes an environment with several evolving agents and some different techniques to construct agents. This should be able to draw on and compare pretty much all the chapters from section 2 (including the natural language chapter).
  • Philosophical Issues for Artificial Intelligence: Discusses AI problems typically dealt with by philosophers, such as what is theoretically possible to achieve with AI, whether programs can be conscious, or feel pain, intentionality, and symbol grounding.

More advanced topics and techniques in AIEdit

  • Advanced Expert Systems: Expands on the basic expert systems explained in Knowledge Engineering in section 2. Includes more in depth explanation of Bayesian Networks than in the Machine Learning section.
  • Case Study - Data Mining: Describes mining a large dataset (perhaps some part of the wikipedia database) using machine learning algorithms, using software like Weka.
  • Advanced Natural Language: A description of the various techniques for dealing with tenses, sentence focus, presuppositions, etc. in NLP and NLG. This focuses mostly on the underlying structure of language and how to translate into some logical language, rather than statistical methods and models.
  • Natural Language Learning: Deals with more advanced statistical models for learning to understand language.
  • Case Study - Dialogue System: Building system that can communicate (intelligently) in written natural language. In a nutshell, trying to pass the Turing test. Three basic paradigms; case based reasoning (like ALICE), Logic based (translating everything to and from some extended version of predicate logic) and some machine learning based solution.

Highly specific AI topics and techniquesEdit

  • Machine Vision: Interpreting visual data. Face recognition, 3d reconstruction etc.
  • Bayesian Decision Theory
  • Linear Discriminants and Perceptron
  • Support Vector Machines
  • Parameteric Techniques
  • Non-parametric Techniques
  • Speech Recognition, Text to Speech and OCR
  • Advanced Logics: Advanced logic systems.
  • Reinforcement Learning
  • Robotics: Detailed and technical introduction to the three basic paradigms of robotics. Deals with software and hardware. (Define: situated robotics.)

AI Circuits and algorithmsEdit

The Future of Artificial IntelligenceEdit

AppendicesEdit

  • Index, topics in alphabetical order
  • Resources, bibliography and further reading