IA Series

[IA 9] Agent Design Process v2: Bridging the Agent Function and Acceptance Criteria

Making AI Theory Testable. There’s a gap between the Agent Function and the Agent Program and what the Agent should do and what it does do. ATDD can help bridge this. Here I detail how.

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[IA Series 7/n] Building a Self-Consistency LLM-Agent: From PEAS Analysis to Production Code

Building a Self-Consistency LLM-Agent: From PEAS Analysis to Production Code - a guide to designing an LLM-based agent.

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[IA Series 6/n] A Bayesian Learning Agent: Bayes Theorem and Intelligent Agents

The article discusses how to implement Bayes Theorem in a learning agent that updates its beliefs about an environment based on new evidence, illustrated through a game involving guessing a number derived from a dice throw.

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[IA Series 5/n] The Evolution from Logic to Probability to Deep Learning: A course correction to Transformers

Introduction In the previous post, I shared my view on “Why Study Logic?”, we looked at the Knowledge Representation and highlighted the importance of Logic and Reasoning in storing and accessing Knowledge. In this post I’m going to highlight a section from the book “Introduction to Artificial Intelligence” by Wolfgang Ertel. His approach with this book was to make AI more accessible than Russel and Norvig’s 1000+ page bible. It worked for me.

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[IA Series 4/n] A Big Question: Why Study Logic in a World of Probabilistic AI?

Introduction The purpose of this article is to help me answer the question “Why am I studying Logic?”. If it helps you, that’d be great, let me know! The question comes from a nagging feeling of, why don’t I see logic used more in the ‘real world’. It could be a personal bias as I more easily see the utility of Rosenblatt’s work, where he looked at both Symbolic Logic and Probability Theory to help solve a problem and choose Probability Theory ([NN Series 1/n] From Neurons to Neural Networks: The Perceptron), with that we had the birth of the Artificial Neuron and the rest is history!

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[IA Series 3/n] Intelligent Agents Term Sheet

“[IA Series 3/n] Intelligent Agents Term Sheet” breaks down essential AI terminology from Russell & Norvig’s seminal textbook. Learn what makes agents rational (or irrational), understand different agent types, and follow a structured 5-step design process from environment analysis to implementation. Perfect reference for AI practitioners and students. Coming next: how agents mirror human traits. #ArtificialIntelligence #IntelligentAgents #AIDesign

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[IA Series 2/n] Search Algorithms and Intelligent Agents

The document discusses various search algorithms used by Intelligent Agents for navigating mazes, detailing their types, characteristics, tradeoffs, and implementations.

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[IA Series 1/n] AI Search - Terms and Algorithms

This text introduces key concepts and algorithms related to intelligent agents in AI, focusing on search terms, uninformed and informed search strategies, and adversarial search techniques.

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