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2010/2011 Semester 2

Introduction: Chapter 1

ARTIFICIAL INTELLIGENCE

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Course overview

• Introduction and Agents (chapters 1,2)Introduction and Agents (chapters 1,2)• Search (chapters 3,4,5,6)Search (chapters 3,4,5,6)• Logic (chapters 7,8,9)Logic (chapters 7,8,9)• Planning (chapters 11,12)Planning (chapters 11,12)• Uncertainty (chapters 13,14)Uncertainty (chapters 13,14)• Learning (chapters 18,20)Learning (chapters 18,20)• Natural Language Processing (chapter 22,23)Natural Language Processing (chapter 22,23)

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Search (Ch 3,4,5,6)Search (Ch 3,4,5,6)• Ch3: Solving problems by searchingCh3: Solving problems by searching

– Problem =Problem = Search Search

• Ch4: Informed Search and ExplorationCh4: Informed Search and Exploration– AA** search /Heuristic Search search /Heuristic Search – Simulated annealing search / Genetic algorithmsSimulated annealing search / Genetic algorithms

• Ch5: Constraint Satisfaction ProblemsCh5: Constraint Satisfaction Problems– Constraint Satisfaction Problems (CSP)Constraint Satisfaction Problems (CSP)

– Backtracking search for CSPs / Local search for CSPsBacktracking search for CSPs / Local search for CSPs

• Ch6: Adversarial SearchCh6: Adversarial Search– Optimal decisions / α-β pruningOptimal decisions / α-β pruning– Imperfect, real-time decisionsImperfect, real-time decisions

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Logical Agents

• Logical AgentsLogical Agents– Knowledge-based agentsKnowledge-based agents– Inference rules and theorem provingInference rules and theorem proving

• First-Order LogicFirst-Order Logic

– Syntax and semantics of FOLSyntax and semantics of FOL

– Knowledge engineering in FOLKnowledge engineering in FOL

• Inference in FOLsInference in FOLs– First-order inference / UnificationFirst-order inference / Unification– Generalized Modus PonensGeneralized Modus Ponens

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Knowledge RepresentationKnowledge Representation

• Knowledge RepresentationKnowledge Representation– Ageneral ontology / categories / Actions / MentalAgeneral ontology / categories / Actions / Mental– reasoning about categoriesreasoning about categories

• Planning Planning – Planning with State-Space SearchPlanning with State-Space Search– Planning with Propositional LogicPlanning with Propositional Logic

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Uncertainty and Inference Uncertainty and Inference

• UncertaintyUncertainty– Probability / Syntax and Semantics– Inference / Independence and Bayes' Rule

• Probabilistic Reasoning Probabilistic Reasoning – Syntax and Semantics of Bayesian networks– Exact inference by enumeration / variable elimination– Approximate inference by stochastic simulation/ MCMC

• Probabilistic Reasoning over Time Probabilistic Reasoning over Time – Inference: Filtering, prediction, smoothing– Hidden Markov models– Kalman Filters (a brief mention)

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• Learning (chapters 18,20) Learning (chapters 18,20) – Active and Interactive Learning Active and Interactive Learning

• Natural Language Processing (chapter 22,23)Natural Language Processing (chapter 22,23)

• Computer Vision Computer Vision

• Search / Data Mining (Doc / Image / Video / Search / Data Mining (Doc / Image / Video / Event ) Event )

• Multi-agents / InteractionsMulti-agents / Interactions

• Knowledge Organization Knowledge Organization

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Intelligent SystemsIntelligent Systems