Now fully expanded into a textbook format, this second edition builds on the foundation of the 2013 lecture notes-style first edition, transforming it into an ideal resource for one-semester graduate or upper-level undergraduate courses. Retaining the original structure while significantly enhancing accessibility and depth, this edition features:

# New theoretical advancements in NAL design, particularly in later chapters.

# Clearer exposition of core concepts, including the rationale behind NAL's formalism.

# Practical insights into key design choices for computer implementations.

# Enhanced pedagogy: Detailed examples, chapter summaries, and end-of-chapter exercises to reinforce learning.

# Critical context: Footnotes comparing NAL to alternative models and exploring advanced topics.

# Meticulous revisions: Corrections of errors and typos for improved accuracy.

Readers with a college-level background in AI, computer science, or mathematical logic will find this book indispensable for understanding the principles and applications of NAL in AI development.