Logic and AI: Knowledge Representation, Logic-Based AI Use Cases
Explore the foundational concepts of symbolic reasoning, logic, and AI, including propositional and first-order logic, logical agents, and knowledge representation. Discover the significance of logic in AI applications such as Hunt the Wumpus game, semantic knowledge graphs, and neural network enhancements.
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Presentation Transcript
Symbolic Reasoning Logic and AI Chapters 7, 8.1 8.3, 9
Logic roadmap overview Basic concepts, Hunt the Wumpus use case Propositional logic Problems with propositional logic First-order logic Properties, relations, functions, quantifiers, Terms, sentences, wffs, axioms, theories, proofs, Variations and extensions to first-order logic Logical agents Reflex agents Representing change: situation calculus, frame problem Preferences on actions Goal-based agents
For starters What is knowledge? How can we represent knowledge? How can we use it to help understand the world, what people say, what we see? Possible example: All elephants are grey Clyde is an elephant What color is Dumbo? Logic as knowledge motivated by this example But there s much more to knowledge
Disclaimer Logic, like whiskey, loses its beneficial effect when taken in too large quantities. - Lord Dunsany
Big Ideas Logic: great knowledge representation (KR) language for many AI problems Propositional logic: simple foundation and fine for many AI problems First order logic (FOL): more expressive as a KR language; needed for many AI problems Variations on classical FOL are common: horn logic, higher-order logic, modal logic, three- valued logic, probabilistic logic, fuzzy logic, etc.
AI Use Cases for Logic Logic has many use cases even in a time dominated by deep learning, including these examples: Modeling and using knowledge in the Hunt the Wumpus game Supporting AI planning systems that allow agents to efficiently achieve their goals Defining and using semantic knowledge graphs such as schema.org and Wikidata Supporting common sense and causal reasoning Adding features to neural network systems
Fin Fin 7
