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<!DOCTYPE html>
<html lang="en">
<head>
<title>Artificial Intelligence: A Modern Approach</title>
<link rel="stylesheet" href="styles.css">
<script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
</head>
<body>
<div class="jumbotron">
<div class="container text-center">
<h1>Artificial Intelligence</h1>
<h2>A Modern Approach</h2>
<p>Stuart Russell and Peter Norvig</p>
</div>
</div>
<div class="container">
<h2>Part Select</h2>
<ul class="nav nav-tabs">
<li class="active"><a data-toggle="tab" href="#part1">Part I</a></li>
<li><a data-toggle="tab" href="#part2">Part II</a></li>
<li><a data-toggle="tab" href="#part3">Part III</a></li>
<li><a data-toggle="tab" href="#part4">Part IV</a></li>
<li><a data-toggle="tab" href="#part5">Part V</a></li>
<li><a data-toggle="tab" href="#part6">Part VI</a></li>
<li><a data-toggle="tab" href="#part7">Part VII</a></li>
</ul>
<div class="tab-content">
<div id="part1" class="tab-pane fade in active">
<h3>Artificial Intelligence</h3>
<dl class="dl-horizontal">
<dt><a href="./1-Introduction/">1. Introduction</a></dt>
<dd>In which we try to explain why we consider artificial intelligence to be a subject
most worthy of study, and in which we try to decide what exactly it is, this being a
good thing to decide before embarking.</dd>
<dt><a href="./2-Intelligent-Agents/">2. Intelligent Agents</a></dt>
<dd>In which we discuss the nature of agents, perfect or otherwise, the diversity of
environments, and the resulting menagerie of agent types.</dd>
</dl>
</div>
<div id="part2" class="tab-pane fade">
<h3>Problem Solving</h3>
<dl class="dl-horizontal">
<dt><a href="./3-Solving-Problems-By-Searching/">3. Solving Problems By Searching</a></dt>
<dd>In which we see how an agent can find a sequence of actions that achieves its
goals when no single action will do.</dd>
<dt><a href="./4-Beyond-Classical-Search/">4. Beyond Classical Search</a></dt>
<dd>In which we relax the simplifying assumptions of the previous chapter, thereby
getting closer to the real world.</dd>
<dt><a href="./5-Adversarial-Search/">5. Adversarial Search</a></dt>
<dd>In which we examine the problems that arise when we try to plan ahead in a world
where other agents are planning against us.</dd>
<dt><a href="./6-Constraint-Satisfaction-Problems/">6. Constraint Satisfaction Problems</a></dt>
<dd>In which we see how treating states as more than just little black boxes leads to the
invention of a range of powerful new search methods and a deeper understanding
of problem structure and complexity.</dd>
</dl>
</div>
<div id="part3" class="tab-pane fade">
<h3>Knowledge Reasoning and Planning</h3>
<dl class="dl-horizontal">
<dt><a href="./7-Logical-Agents/">7. Logical Agents</a></dt>
<dd>In which we design agents that can form representations of a complex world, use a
process of inference to derive new representations about the world, and use these
new representations to deduce what to do.</dd>
<dt><a href="./8-First-Order-Logic/">8. First Order Logic</a></dt>
<dd>In which we notice that the world is blessed with many objects, some of which are
related to other objects, and in which we endeavor to reason about them.</dd>
<dt><a href="./9-Inference-In-First-Order-Logic/">9. Inference In First Order Logic</a></dt>
<dd>In which we define effective procedures for answering questions posed in firstorder
logic.</dd>
<dt><a href="./10-Classical-Planning/">10. Classical Planning</a></dt>
<dd>In which we see how an agent can take advantage of the structure of a problem to
construct complex plans of action.</dd>
<dt><a href="./11-Planning-And-Acting-In-The-Real-World/">11. Planning and Acting in the Real World</a></dt>
<dd>In which we see how more expressive representations and more interactive agent
architectures lead to planners that are useful in the real world.</dd>
<dt><a href="./12-Knowledge-Representation/">12. Knowledge Representation</a></dt>
<dd>In which we show how to use first-order logic to represent the most important
aspects of the real world, such as action, space, time, thoughts, and shopping.</dd>
</dl>
</div>
<div id="part4" class="tab-pane fade">
<h3>Uncertain Knowledge and Reasoning</h3>
<dl class="dl-horizontal">
<dt><a href="./13-Quantifying-Uncertainity/">13. Quantifying Uncertainity</a></dt>
<dd>In which we see how an agent can tame uncertainty with degrees of belief.</dd>
<dt><a href="./14-Probabilistic-Reasoning/">14. Probabilistic Reasoning</a></dt>
<dd>In which we explain how to build network models to reason under uncertainty
according to the laws of probability theory.</dd>
<dt><a href="./15-Probabilistic-Reasoning-Over-Time/">15. Probabilistic Reasoning Over Time</a></dt>
<dd>In which we try to interpret the present, understand the past, and perhaps predict
the future, even when very little is crystal clear.</dd>
<dt><a href="./16-Making-Simple-Decisions/">16. Making Simple Decisions</a></dt>
<dd>In which we see how an agent should make decisions so that it gets what it wants on average, at least.</dd>
<dt><a href="./17-Making-Complex-Decisions/">17. Making Complex Decisions</a></dt>
<dd>In which we examine methods for deciding what to do today, given that we may
decide again tomorrow.</dd>
</dl>
</div>
<div id="part5" class="tab-pane fade">
<h3>Learning</h3>
<dl class="dl-horizontal">
<dt><a href="./18-Learning-From-Examples/">18. Learning From Examples</a></dt>
<dd>In which we describe agents that can improve their behavior through diligent
study of their own experiences.</dd>
<dt><a href="./19-Knowledge-In-Learning/">19. Knowledge In Learning</a></dt>
<dd>In which we examine the problem of learning when you know something already.</dd>
<dt><a href="./20-Learning-Probabilistic-Models">20. Learning Probabilistic Models</a></dt>
<dd>In which we view learning as a form of uncertain reasoning from observations.</dd>
<dt><a href="./21-Reinforcement-Learning">21. Reinforcement Learning</a></dt>
<dd>In which we examine how an agent can learn from success and failure, from reward
and punishment.</dd>
</dl>
</div>
<div id="part6" class="tab-pane fade">
<h3>Communicating, Acting and Perceiving</h3>
<dl class="dl-horizontal">
<dt><a href="./22-Natural-Language-Processing">22. Natural Language Processing</a></dt>
<dd>In which we see how to make use of the copious knowledge that is expressed in
natural language.</dd>
<dt><a href="./23-Natural-Language-For-Communication/">23. Natural Language For Communication</a></dt>
<dd>In which we see how humans communicate with one another in natural language,
and how computer agents might join in the conversation.</dd>
<dt><a href="./24-Perception">24. Perception</a></dt>
<dd>In which we connect the computer to the raw, unwashed world.</dd>
<dt><a href="./25-Robotics">25. Robotics</a></dt>
<dd>In which agents are endowed with physical effectors with which to do mischief.</dd>
</dl>
</div>
<div id="part7" class="tab-pane fade">
<h3>Conclusions</h3>
<dl class="dl-horizontal">
<dt><a href="./26-Philosophical-Foundations/">26. Philosophical Foundations</a></dt>
<dd>In which we consider what it means to think and whether artifacts could and
should ever do so.</dd>
<dt><a href="./27-AI-The-Present-And-Future/">27. AI The Present and Future</a></dt>
<dd>In which we take stock of where we are and where we are going, this being a good
thing to do before continuing.</dd>
</dl>
</div>
</div>
</div>
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