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Artificial Intelligence and Machine Learning Fundamentals

You're reading from   Artificial Intelligence and Machine Learning Fundamentals Develop real-world applications powered by the latest AI advances

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Product type Paperback
Published in Dec 2018
Publisher
ISBN-13 9781789801651
Length 330 pages
Edition 1st Edition
Languages
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Author (1):
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Zsolt Nagy Zsolt Nagy
Author Profile Icon Zsolt Nagy
Zsolt Nagy
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Toc

Heuristics


In this topic, we will formalize informed search techniques by defining and applying heuristics to guide our search.

Uninformed and Informed Search

In the Tic-Tac-Toe example, we implemented a greedy algorithm that first focused on winning, and then focused on not losing. When it comes to winning the game immediately, the greedy algorithm is optimal, because there is never a better step than winning the game. When it comes to not losing, it matters how we avoid the loss. Our algorithm simply chose a random safe move without considering how many winning opportunities we have created.

Breadth First Search and Depth First Search are uninform, because they consider all possible states in the game. An informed search explores the space of available states intelligently.

Creating Heuristics

If we want to make better decisions, we apply heuristics to guide the search in the right direction by considering longer-term utility. This way, we can make a more informed decision in the present...

You have been reading a chapter from
Artificial Intelligence and Machine Learning Fundamentals
Published in: Dec 2018
Publisher:
ISBN-13: 9781789801651
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