John McCarthy, a pioneer in artificial intelligence, describes his views on the subject.
"This article for the layman answers basic questions about artificial intelligence. The opinions expressed here are not all consensus opinion among researchers in AI."
Frequent questions are answered, and an overview of the field of artificial intelligence and its branches is provided.
introduction
AI Topics Overview
If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behaviour and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) ...
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) ...
Category: all from http://www.aaai.org
Decision Trees Tutorial
Written by Andrew Moore, the document is available as PDF.
The Decision Tree is one of the most popular classification algorithms in current use in Data Mining and Machine Learning. This tutorial can be used as a self-contained introduction to the flavour and terminology of data mining without needing to review many statistical or probabilistic pre-requisites. If you're new to data mining you'll enjoy it, but your eyebrows will raise at how simple it all is! After having defined the job of classification, we explain how information gain (next Andrew Tutorial) can be used to find predictive input attributes. We show how applying this procedure recursively allows us to build a decision tree to predict future events. We then look carefully at a question so fundamental, it is the basis for much of all statistics and machine learning theory: how do you choose between a complicated model t
The Decision Tree is one of the most popular classification algorithms in current use in Data Mining and Machine Learning. This tutorial can be used as a self-contained introduction to the flavour and terminology of data mining without needing to review many statistical or probabilistic pre-requisites. If you're new to data mining you'll enjoy it, but your eyebrows will raise at how simple it all is! After having defined the job of classification, we explain how information gain (next Andrew Tutorial) can be used to find predictive input attributes. We show how applying this procedure recursively allows us to build a decision tree to predict future events. We then look carefully at a question so fundamental, it is the basis for much of all statistics and machine learning theory: how do you choose between a complicated model t
Category: all from http://www.autonlab.org
Game Tree Search Algorithms Tutorial
Tutorial slides by Andrew Moore available as PDF.
Introduction to algorithms for computer game playing. We describe the assumptions about two-player zero-sum discrete finite deterministic games of perfect information. We also practice saying that noun-phrase in a single breath. After the recovery teams have done their job we talk about solving such games with minimax and then alpha-beta search. We also discuss the dynamic programming approach, used most commonly for end-games. We also debate the theory and practice of heuristic evaluation functions in games.
Introduction to algorithms for computer game playing. We describe the assumptions about two-player zero-sum discrete finite deterministic games of perfect information. We also practice saying that noun-phrase in a single breath. After the recovery teams have done their job we talk about solving such games with minimax and then alpha-beta search. We also discuss the dynamic programming approach, used most commonly for end-games. We also debate the theory and practice of heuristic evaluation functions in games.
Category: tutorial from http://www.autonlab.org
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