Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
|
Buy Now
Planning with Markov Decision Processes - An AI Perspective (Paperback)
Loot Price: R1,052
Discovery Miles 10 520
|
|
Planning with Markov Decision Processes - An AI Perspective (Paperback)
Series: Synthesis Lectures on Artificial Intelligence and Machine Learning
Expected to ship within 10 - 15 working days
|
Markov Decision Processes (MDPs) are widely popular in Artificial
Intelligence for modeling sequential decision-making scenarios with
probabilistic dynamics. They are the framework of choice when
designing an intelligent agent that needs to act for long periods
of time in an environment where its actions could have uncertain
outcomes. MDPs are actively researched in two related subareas of
AI, probabilistic planning and reinforcement learning.
Probabilistic planning assumes known models for the agent's goals
and domain dynamics, and focuses on determining how the agent
should behave to achieve its objectives. On the other hand,
reinforcement learning additionally learns these models based on
the feedback the agent gets from the environment. This book
provides a concise introduction to the use of MDPs for solving
probabilistic planning problems, with an emphasis on the
algorithmic perspective. It covers the whole spectrum of the field,
from the basics to state-of-the-art optimal and approximation
algorithms. We first describe the theoretical foundations of MDPs
and the fundamental solution techniques for them. We then discuss
modern optimal algorithms based on heuristic search and the use of
structured representations. A major focus of the book is on the
numerous approximation schemes for MDPs that have been developed in
the AI literature. These include determinization-based approaches,
sampling techniques, heuristic functions, dimensionality reduction,
and hierarchical representations. Finally, we briefly introduce
several extensions of the standard MDP classes that model and solve
even more complex planning problems. Table of Contents:
Introduction / MDPs / Fundamental Algorithms / Heuristic Search
Algorithms / Symbolic Algorithms / Approximation Algorithms /
Advanced Notes
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
You might also like..
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.