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Acting rationally: The rational agent approach.
An agent is just something that acts (agento comes from the Latin agere, to do). Of course, all computer programs do something, but computer agents are expected do to more: operate autonomously, perceive their environment, persis over a prolonged time period, adapt to change and create and pursue goals. A rational agent is one that acto so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.
Artificial Intelligence, A Modern Aproach, 4th Edition. Stuart Russell, UCB Peter Norvig, Google Inc.
Conceptual illustrations of the book chapters using DALL-E.
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1 – Solving problems by searching
2 – Search in Complex Environments.
3 – Adversarial Search and Games.
4 – Constraint Satisfaction Problems.
5 – Logical Agents
6 – First Order Logic.
7 – Inference in First Order Logic.
8 – Knowledge Representation.
9 – Automated Planning.
10 – Quantifying Uncertainty.
11 – Probabilistic Reasoning.
12 – Probabilistic Reasoning Over Time.
13 – Probabilistic Programming
14 – Making simple decisions
15 – Making complex decisions
16 – Multiagent Decision Making
17 – Learning from examples.
18 – Learning Probabilistic Models.
19 – Deep Learning.
20 – Reinforcement Learning.
21 – Natural Language Learning.
22 – Deep Learning for Natural Language Processing.
23 – Computer Vision.
24 – Robotics
aima code
Code for the book “Artificial Intelligence: A modern approach”.
https://github.com/aimacode
In 2012, leveraging Artificial Intelligence, we lead the integration of Wolfram Alpha and Mathematica, introducing Step-bye-Step solutions across more than 200 topics in mathematics, chemistry and physics. This initiative has been embraced by colleges and high schools nation wide, evidenced by numerous support letters, significantly enriching the learning experience and academic resources available to students.
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