Engineering AI Agents
BOOK
Foundations
Training Deep Networks
Perception
State Estimation
Large Language Models
Multimodal Reasoning
Planning
Markov Decision Processes
Reinforcement Learning
COURSES
Introduction to AI
AI for Robotics
Deep Learning for Computer Vision
DATA MINING - BEING PORTED
ABOUT ME
Logical Reasoning
Logical Reasoning
Symbolic representations and Logical Inference
Syllabus
Syllabus
Foundations
Foundations
Deep Neural Networks
Training Deep Neural Networks
Perception
Perception
Recursive State Estimation
State Estimation
Large Language Models
Large Language Models
Logical Reasoning
Automated Reasoning
World Models
Logical Inference
Logical Agents
Planning
Planning
Acting - Markov Decision Processes
Markov Decision Processes
Acting - Reinforcement Learning
Reinforcement Learning
Math Background
Math for ML Textbook
Probability Basics
Linear Algebra for Machine Learning
Calculus
Resources
Your Programming Environment
Training Keras with the SLURM Scheduler
NYU JupyrterHub Environments
Submitting Your Assignment / Project
Learn Python
Assignments
Assignment 1
Assignment 2
Assignment 3
Assignment 4
Project
Chat with your Video Library
Categories
All
(4)
Automated Reasoning
We have seen in an earlier chapter where we introduced a
dynamical system
governing the state evolution of the environment that a state is composed of variables and such
fact…
Logical Agents
In this chapter we see how agents equipped with the ability to represent internally the state of the environment and reason about the effectiveness of possible actions using…
Logical Inference
The wumpus world despite its triviality, contains some deeper abstractions that are worth summarizing.
World Models
For each problem we can define a number of
world models
each representing every possible state (configuration) of the environment that the agent may be in.
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