Automated Reasoning Logic, AI Agents and LLMs

Understand Logic, Search, Planning, SAT/SMT, Theorem Proving and Verification for Generative AI and Neuro-Symbolic AI
What you'll learn

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Explain why Automated Reasoning is becoming increasingly important in Generative AI, LLMs, AI Agents, and the emerging Neuro-Symbolic AI paradigm.
Analyze how Logic, Knowledge Representation, Inference, Search, Planning, and Proof Systems enable machines to reason beyond probabilistic text generation
Distinguish and explain the roles of key Automated Reasoning technologies, including Theorem Proving, SAT/SMT Solvers, Symbolic Execution, Formal Verification
Connect Automated Reasoning concepts to real-world AI applications, and evaluate how Automated Reasoning can make AI systems more reliable and trustworthy
Requirements

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A basic understanding of Artificial Intelligence and Machine Learning is helpful, but no advanced AI or mathematics background is required.
You should be familiar with basic concepts such as AI, machine learning, neural networks, LLMs, and Generative AI.
No prior knowledge of formal logic, theorem proving, SAT/SMT solvers, constraint programming, or formal verification is required.
You do not need to be an expert programmer. The course focuses on understanding the ideas, motivations, and principles behind Automated Reasoning rather than implementing complex algorithms from scratch.
Curiosity about how AI can move beyond generating plausible answers toward reasoning, planning, and verification is the most important prerequisite.
Description

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"This course contains the use of artificial intelligence."
Automated Reasoning - The Missing Piece of Generative AI and AI Agents
Generative AI has become remarkably good at understanding language, generating content, and solving increasingly complex problems. But there is a fundamental limitation:generating a plausible answer is not the same as reasoning with certainty.This is whereAutomated Reasoning becomes increasingly important.
From LLMs and Generative AI to AI Agents and Neuro-Symbolic AI, the next generation of intelligent systems needs more than neural networks alone. It needs the ability to represent knowledge explicitly, search through possible solutions, enforce constraints, verify conclusions, prove correctness, and make decisions according to well-defined rules. Automated Reasoning provides many of these capabilities.
This course takes you on a journey fromLogic, Knowledge Representation, Search, Constraints, and Planning toTheorem Proving, SAT/SMT Solving, Symbolic Execution, and Formal Verification - and finally connects these ideas to real-world systems such as CPU verification at Intel and spacecraft software verification at NASA.
You will not learn Automated Reasoning as a collection of complicated mathematical formulas or isolated algorithms. Instead, you will learn to understandwhy these technologies exist, what problems they were created to solve, how they fit together, and why they are becoming increasingly relevant to Generative AI, AI Agents, and the emerging Neuro-Symbolic AI paradigm.
Understand the Reasoning Behind AI - Not Just the Algorithms
Much of modern AI is built around neural networks. LLMs can learn patterns from enormous amounts of data and generate remarkably sophisticated responses. However, neural generation has an important limitation: an answer can be convincing without necessarily being guaranteed to be correct.
This creates a fundamental question:What happens when AI needs to do more than generate - when it needs to reason, plan, verify, and act reliably?
Automated Reasoning offers a different perspective. Instead of asking only"What answer is likely?" , we can ask
What knowledge do we have?
What rules must hold?
What possibilities are allowed?
What constraints must be satisfied?
Can this conclusion be proven?
Can this program be guaranteed to behave correctly?
These ideas have existed for decades in logic, theorem proving, constraint solving, planning, and formal verification. What is changing today is their relationship with modern neural AI.
The emergingNeuro-Symbolic AI paradigm seeks to combine the strengths of both worlds
Neural AI provides perception, language understanding, learning, and flexible generation.
Symbolic AI and Automated Reasoning provide explicit knowledge, structured reasoning, constraints, search, planning, and verification.
The result is not simply a larger language model. It is a different way of thinking about intelligent systems:neural models generate possibilities; symbolic systems can constrain, reason about, search through, and verify those possibilities.
This course is designed for people who want to understand that bigger picture.
You will focus on theideas and principles behind the technology, rather than getting lost in mathematical notation or implementation details. For every major concept, the goal is to answer three fundamental questions:Why does it exist? What problem does it solve? How does it work at a conceptual level?
By the end of the course, you should be able to see Automated Reasoning not as an old and isolated branch of AI, but as one of the foundations that may become increasingly important as AI evolves from systems thatgenerate answers toward systems thatreason, plan, verify, and take actions.
Who this course is for

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AI and Machine Learning practitioners who want to understand what Automated Reasoning adds beyond neural networks and probabilistic learning.
Generative AI and LLM practitioners who want to understand why generating plausible answers is not the same as reasoning, and how symbolic reasoning can complement LLMs.
AI Agent developers and enthusiasts who want to understand the role of reasoning, planning, constraints, and verification in building more reliable agentic systems.
Software engineers, architects, and developers who want to understand how Logic, Search, Constraint Solving, Symbolic Execution, and Formal Verification are used to build and verify complex software and hardware systems.
AI students, researchers, and technology professionals who want a conceptual introduction to Automated Reasoning without starting with heavy mathematical notation or implementation details.
Anyone interested in Neuro-Symbolic AI and the emerging combination of neural models with symbolic knowledge, reasoning, planning, and verification.
Technology professionals who want to understand the "why" behind AI technologies - why they were created, what problems they solve, and how different approaches fit together - before diving into technical implementation.
Homepage

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