CS-INFO372-Module3-1-1 - Explorations in Artificial...

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1 Explorations in Artificial Intelligence Prof. Carla P. Gomes gomes@cs.cornell.edu Module 3-1 Logic Based Reasoning
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Knowledge and Reasoning Knowledge and Reasoning humans are very good at acquiring new information by combining raw knowledge, experience, with reasoning; Examples: diagnosis –e.g., a physician diagnoses a patient, i.e., it infers what disease he/she has, based on the knowledge he/she acquired as a student, textbooks, prior cases and also some reasoning process (patterns of association, or other process) that he/she may not be able to describe. car repair diagnosis Common sense reasoning Inventions, new ideas Key issues: Representation of knowledge Reasoning processes
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Knowledge-base Agents Key issues: Representation of knowledge knowledge base Reasoning processes inference/reasoning mechanisms to query what is known, to derive new information, to make decisions. (*) called knowledge representation language Knowledge base = set of sentences in a formal language representing facts about the world(*)
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Knowledge bases Key aspects: How to add sentences to the knowledge base How to query the knowledge base Both tasks may involve inference i.e. how to derive new sentences from old sentences Logical agents – inference must obey the fundamental requirement that when one asks a question to the knowledge base, the answer should follow from what has been told to the knowledge base previously . (In other words the inference process should not “make things” up…)
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Logic: Outline 1. General principles of logic – main vehicle for representing knowledge 2. Wumpus World - a toy world; how a knowledge based agent operates 3. Propositional logic 4. Predicate logic 5. Proof Methods 1. Inference Rules (including induction) 2. Model Checking Algorithmic approaches 1. Satisfiability as an Encoding language
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Logic in general Logics are formal languages for formalizing reasoning, in particular for representing information such that conclusions can be drawn A logic involves: A language with a syntax for specifying what is a legal expression in the language; syntax defines well formed sentences in the language Semantics for associating elements of the language with elements of some subject matter. Semantics defines the "meaning" of sentences (link to the world); i.e., semantics defines the truth of a sentence with respect to each possible world Inference rules for manipulating sentences in the language Original motivation: Early Greeks, settle arguments based on purely rigorous (symbolic/syntactic) reasoning starting from a given set of premises. 6
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7 Example of a formal language: Arithmetic E.g., the language of arithmetic x+2 ≥ y is a sentence; ( x2+y > {} is not a sentence) x+2 ≥ y is true iff the number x+2 is no less than the number y x+2 ≥ y is true in a world where x = 7, y = 1 x+2 ≥ y is false in a world where x = 0, y = 6
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8 Several systems – biological, mechanical, electric, etc ---
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CS-INFO372-Module3-1-1 - Explorations in Artificial...

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