https://algorithmsbook.com/

MIT press provides another excellent book in creative commons.

Algorithms for decision making: free download book

I plan to buy it and I recommend you do. This book provides a broad introduction to algorithms for decision making under uncertainty.

The book takes an agent based approach

An agent is an entity that acts based on observations of its environment. Agents

may be physical entities, like humans or robots, or they may be nonphysical entities,

such as decision support systems that are implemented entirely in software.

The interaction between the agent and the environment follows an observe-act cycle or loop.

  • The agent at time t receives an observation of the environment
  • Observations are often incomplete or noisy;
  • Based in the inputs, the agent then chooses an action at through some decision process.
  • This action, such as sounding an alert, may have a nondeterministic effect on the environment.
  • The book focusses on agents that interact intelligently to achieve their objectives over time.
  • Given the past sequence of observations and knowledge about the environment, the agent must choose an action at that best achieves its objectives in the presence of various sources of uncertainty including:
  1. outcome uncertainty, where the effects of our actions are uncertain,
  2. model uncertainty, where our model of the problem is uncertain,
    3. state uncertainty, where the true state of the environment is uncertain, and
  3. interaction uncertainty, where the behavior of the other agents interacting in the environment is uncertain.

The book is organized around these four sources of uncertainty.

Making decisions in the presence of uncertainty is central to the field of artificial intelligence

Table of contents is

Introduction

Decision Making

Applications

Methods

History

Societal Impact

Overview

PROBABILISTIC REASONING

 Representation

Degrees of Belief and Probability

Probability Distributions

Joint Distributions

Conditional Distributions

Bayesian Networks

Conditional Independence

Summary

Exercises

viii contents

 

Inference

Inference in Bayesian Networks

Inference in Naive Bayes Models

Sum-Product Variable Elimination

Belief Propagation

Computational Complexity

Direct Sampling

Likelihood Weighted Sampling

Gibbs Sampling

Inference in Gaussian Models

Summary

Exercises

 Parameter Learning

Maximum Likelihood Parameter Learning

Bayesian Parameter Learning

Nonparametric Learning

Learning with Missing…

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