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Lecture 40 of 42

Lecture 40 of 42. Final Review Part 1 of 2. Monday, 05 December 2005 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org http://www.cis.ksu.edu/~bhsu Reading: None

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Lecture 40 of 42

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  1. Lecture 40 of 42 Final Review Part 1 of 2 Monday, 05 December 2005 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org http://www.cis.ksu.edu/~bhsu Reading: None Final Review: Chapters 1-15, 18-19, 23, 24 R&N (emphasis on 14-15, 18-19)

  2. Lecture 1:The Intelligent Agent Framework • Artificial Intelligence (AI) • Operational definition: study / development of systems capable of “thought processes” (reasoning, learning, problem solving) • Constructive definition: expressed in artifacts (design and implementation) • Intelligent Agents • Topics and Methodologies • Knowledge representation • Logical • Uncertain (probabilistic) • Other (rule-based, fuzzy, neural, genetic) • Search • Machine learning • Planning • Applications • Problem solving, optimization, scheduling, design • Decision support, data mining • Natural language processing, conversational and information retrieval agents • Pattern recognition and robot vision

  3. Lecture 2:Agents and Problem Solving • Agent Frameworks • Reactivity vs. state • From goals to preferences (utilities) • Applications and Automation Case Studies • Search: game-playing systems, problem solvers • Planning, design, scheduling systems • Control and optimization systems • Machine learning: pattern recognition, data mining (business decision support) • Things to Check Out Online • Resources page: www.kddresearch.org/Courses/Fall-2001/CIS730/Resources • Yahoo! Group discussions: groups.yahoo.com/group/ksu-cis730-fall2001 • Suggested project topics, resources – posted in YG

  4. Lecture 3:Search and Constraints • Today’s Reading: Sections 3.5-3.8, Russell and Norvig • Solving Problems by Searching • Problem solving agents: design, specification, implementation • Specification components • Problems – formulating well-defined ones • Solutions – requirements, constraints • Measuring performance • Formulating Problems as (State Space) Search • Example Search Problems • Toy problems: 8-puzzle, 8-queens, cryptarithmetic, toy robot worlds, constraints • Real-world problems: layout, scheduling • Data Structures Used in Search • Uninformed Search Algorithms: BFS, DFS, Branch-and-Bound • Next Class: Informed Search Strategies • State space search handout (Winston) • Search handouts (Ginsberg, Rich and Knight)

  5. Lecture 4:Uninformed Search Algorithms • Search • Problem formulation: state space (initial / operator / goal test / cost), graph • State space search approaches • Blind (uninformed) • Heuristic (informed) • Applications • Problem solving • Optimization • Scheduling • Design • Machine learning (hypothesis space search) • More Resources Online • http://www-jcsu.jesus.cam.ac.uk/~tdk22/project • See also http://groups.yahoo.com/group/ksu-cis730-fall2001 (“REFERENCES”) • Course Project Guidelines Posted in YG • Part I: format • Part II: writing quality and criteria • Part III: resources and suggested topics

  6. Lecture 5:Heuristic Search Algorithms – Greedy, A* • More Heuristic Search • Best-First Search • Greedy • A/A* • Search as function maximization • Problems: ridge; foothill; plateau, jump discontinuity • Solutions: macro operators; global optimization • Constraint Satisfaction Search • Next Class: IDA*, Hill-Climbing, Iterative Improvement • Gradient descent • Global search • MCMC: intuition • Some examples of state-of-the-art applications • Properties and tradeoffs

  7. Lecture 6:More Heuristic Search – A*, Hill-Climbing / SA • More Heuristic Search • Best-First Search: A/A* concluded • Iterative improvement • Hill-climbing • Simulated annealing (SA) • Search as function maximization • Problems: ridge; foothill; plateau, jump discontinuity • Solutions: macro operators; global optimization (genetic algorithms / SA) • Next Class: Constraint Satisfaction Search, Heuristic Search • Next Week: Adversarial Search (e.g., Game Tree Search) • Competitive problems • Minimax algorithm

  8. Lecture 7:Constraint Satisfaction Problems • Constraint Satisfaction Problems (CSPs) • Problem definition • Domain • Constraints • Examples: N-queens, cryptarithmetic, etc. • Issues to be Covered Later • Knowledge representation: how to express domain, constraints • Relational constraints • In classical logic (propositional, predicate, first-order) • In uncertain reasoning • Solving CSPs • Propositional constraints: satisfiability solver • First-order relational constraints: difficulties – later • Speeding up CSPs: iterative improvement • Gradient (hill-climbing) optimization • Simulated annealing

  9. Lecture 8:Game Tree Search: Minimax • Game Graph Search • Frameworks • Two-player versus multi-player • Zero-sum versus cooperative • Perfect information versus partially-observable (hidden state) • Concepts • Utility and representations (e.g., static evaluation function) • Reinforcements: possible role for machine learning • Game tree: node/move correspondence, search ply • Family of Algorithms for Game Trees: Minimax • Propagation of credit • Imperfect decisions • Issues • Quiescence • Horizon effect • Need for (alpha-beta) pruning

  10. Lecture 9:More Game Tree Search: -, Expectiminimax • Games as Search Problems • Frameworks • Concepts: utility, reinforcements, game trees • Static evaluation under resource limitations • Family of Algorithms for Game Trees: Minimax • Static evaluation algorithm • To arbitrary ply • To fixed ply • Sophistications: iterative deepening, pruning • Credit propagation • Intuitive concept • Basis for simple (delta-rule) learning algorithms • State of The Field • Uncertainty in Games: Expectiminimax and Other Algorithms

  11. Lecture 10:Logical Agents and Knowledge Representations • Logical Agents • Knowledge Bases (KB) • Logic in general • Representation languages, syntax • Inference systems • Calculi • Propositional • First-order (FOL, FOPC) • Possible Worlds • Entailment • Models • IA Toy Worlds • Wumpus world • Blocks world

  12. Lecture 11:Propositional and Predicate Logic • Logical Frameworks • Knowledge Bases (KB) • Logic in general: representation languages, syntax, semantics • Propositional logic • First-order logic (FOL, FOPC) • Model theory, domain theory: possible worlds semantics, entailment • Normal Forms • Conjunctive Normal Form (CNF) • Disjunctive Normal Form (DNF) • Horn Form • Proof Theory and Inference Systems • Sequent calculi: rules of proof theory • Derivability or provability • Properties • Soundness (derivability implies entailment) • Completeness (entailment implies derivability)

  13. Lecture 12:Foundations of First-Order Logic • FOL in Practice • FOL agents • Example: Wumpus World in FOL • Situation calculus • Frame problem and variants (see R&N sidebar) • Representational vs. inferential frame problems • Qualification problem: “what if?” • Ramification problem: “what else?” (side effects) • Successor-state axioms • Logical Languages • Propositional logic • Predicates, terms, functions, atoms (atomic sentences / atomic WFFs), WFFs • First-order logic (FOL, FOPC): universal and existentialquantification

  14. Lecture 13:First-Order Knowledge Bases • Properties of Knowledge Bases (KBs) • Satisfiability and validity • Entailment and provability • Properties of Proof Systems: Soundness and Completeness • Normal Forms: CNF, DNF, Horn; Clauses vs. Terms • Frame, Ramification, Qualification Problems

  15. Lecture 14:Resolution Theorem Proving • Resolution Theorem Proving • Conjunctive Normal Form (clausal form) • Inference rule • Single-resolvent form • General form • Proof procedure: refutation • Decidability properties • FOL-SAT • FOL-NOT-SAT (language of unsatisfiable sentences; complement of FOL-SAT) • FOL-VALID • FOL-NOT-VALID • Next Class • More Prolog • Implementing unification

  16. Lecture 15:Logic Programming Techniques • Properties of Proof Systems (Again) • Soundness and completeness • Decidability, semi-decidability, undecidability • Resolution • Refutation • Satisfiability, Validity • Unification • Occurs check • Most General Unifier • Prolog: Tricks of The Trade • Demodulation, paramodulation • Unit resolution, set of support, input / linear resolution, subsumption • Indexing (table-based, tree-based)

  17. Lecture 16:Classical Planning • Classical Planning • Planning versus search • Problematic approaches to planning • Forward chaining • Situation calculus • Representation • Initial state • Goal state / test • Operators • Efficient Representations • STRIPS axioms • Components: preconditions, postconditions (ADD, DELETE lists) • Clobbering / threatening • Reactive plans and policies • Markov decision processes

  18. Lecture 17:Partial-Order Planning • Classical Planning Framework • Planning versus search • Representation: initial state, goal state / test, operators • STRIPS Operators • Components: preconditions, postconditions (ADD, DELETE lists) • STRIPS and interference • Clobbering / threatening • Promotion / demotion • Partial-Order Planners (POP systems) • Next Week • Hierarchical abstraction planning: ABSTRIPS • Conditional plans • Reactive plans and policies • Markov decision processes

  19. Lecture 18:STRIPS and ABSTRIPS • Classical Planning Framework • Planning versus search • Representation: initial state, goal state / test, operators • STRIPS Operators • Components: preconditions, postconditions (ADD, DELETE lists) • STRIPS and interference • Clobbering / threatening • Promotion / demotion • Partial-Order Planners (POP systems) • Next Week • Hierarchical abstraction planning: ABSTRIPS • Conditional plans • Reactive plans and policies • Markov decision processes Adapted from slides by S. Russell, UC Berkeley

  20. Lecture 19:Reaction and Replanning • Classical Planning Framework • Planning versus search • Representation: initial state, goal state / test, operators • STRIPS operators • Partial versus total-order: property of plans • Interleaved vs. noninterleaved: property of planners • Last Week • Hierarchical abstraction planning: ABSTRIPS • Conditional plans • This Week • Monitoring and replanning • Reactive plans and policies • Later • Decision theory • Markov decision processes

  21. Lecture 20:Reasoning under Uncertainty • Introduction to Probabilistic Reasoning • Framework: using probabilistic criteria to search H • Probability foundations • Definitions: subjectivist, objectivist; Bayesian, frequentist, logicist • Kolmogorov axioms • Bayes’s Theorem • Definition of conditional (posterior) probability • Product rule • Maximum APosteriori (MAP) and Maximum Likelihood (ML) Hypotheses • Bayes’s Rule and MAP • Uniform priors: allow use of MLE to generate MAP hypotheses • Relation to version spaces, candidate elimination • Next Week: Chapter 15, Russell and Norvig • Later: Bayesian learning: MDL, BOC, Gibbs, Simple (Naïve) Bayes • Categorizing text and documents, other applications

  22. Lecture 21:Introduction to Bayesian Networks • Graphical Models of Probability • Bayesian belief networks (BBNs) akabelief networksakacausal networks • Conditional independence, causal Markovity • Inference and learning using Bayesian networks • Representation of distributions: conditional probability tables (CPTs) • Learning polytrees (singly-connected BBNs) and tree-structured BBNs (trees) • BBN Inference • Type of probabilistic reasoning • Finds answer to query about P(x) - akaQA • Learning in BBNs: In Two Weeks • Known structure • Partial observability

  23. Lecture 22:Introduction to Machine Learning • Taxonomies of Learning • Definition of Learning: Task, Performance Measure, Experience • Concept Learning as Search through H • Hypothesis space H as a state space • Learning: finding the correct hypothesis • General-to-Specific Ordering over H • Partially-ordered set: Less-Specific-Than (More-General-Than) relation • Upper and lower bounds in H • Version Space Candidate Elimination Algorithm • S and G boundaries characterize learner’s uncertainty • Version space can be used to make predictions over unseen cases • Learner Can Generate Useful Queries • Next Tuesday: When and Why Are Inductive Leaps Possible?

  24. Lecture 23:Decision Trees • (Inductive) Bias: Preference for Some h H (Not Consistency with D Only) • Decision Trees (DTs) • Boolean DTs: target concept is binary-valued (i.e., Boolean-valued) • Building DTs • Histogramming: amethod of vector quantization (encoding input using bins) • Discretization: continuous input  discrete (e.g.., by histogramming) • Entropy and Information Gain • Entropy H(D) for a data set D relative to an implicit concept c • Information gain Gain (D, A) for a data set partitioned by attribute A • Impurity, uncertainty, irregularity, surprise • Heuristic Search • Algorithm Build-DT: greedy search (hill-climbing without backtracking) • ID3 as Build-DT using the heuristicGain(•) • Heuristic : Search :: Inductive Bias : Inductive Generalization • MLC++ (Machine Learning Library in C++) • Data mining libraries (e.g., MLC++) and packages (e.g., MineSet) • Irvine Database: the Machine Learning Database Repository at UCI

  25. Lecture 24:Perceptrons and Artificial Neural Networks • Neural Networks (NNs): Parallel, Distributed Processing Systems • Biological NNs and artificial NNs (ANNs) • PerceptronakaLinear Threshold Gate (LTG), Linear Threshold Unit (LTU) • Model neuron • Combination and activation (transfer, squashing) functions • Multi-Layer ANNs • Focused on one species: (feedforward) multi-layer perceptrons (MLPs) • Input layer: an implicit layer containing xi • Hidden layer: a layer containing input-to-hidden unit weights and producing hj • Output layer: a layer containing hidden-to-output unit weights and producing ok • n-layer ANN: an ANN containing n - 1 hidden layers • Epoch: one training iteration • Overfitting • Overfitting: h does better than h’ on training data and worse on test data • Prevention, avoidance, and recovery techniques

  26. Minimum Description Length (MDL) • Bayesian Information Criterion (BIC) • BIC = additive inverse of MDL (i.e., BIC(h) = -MDL(h)) • Bayesian Classification: Finding Most Probable v Given Examples x • Bayes Optimal Classifier (BOC) • Probabilistic learning criteria: measures of P(prediction | D) or P(hypothesis | D) • BOC: a gold standard for probabilistic learning criteria • Gibbs Classifier • Randomly sample h according to P(h | D), then use to classify • Ratio bound: error no worse than 2 • Bayes optimal error • MCMC methods (Gibbs sampling): Monte Carlo integration over H • Simple BayesakaNaïve Bayes • Assumption of conditional independence of attributes given classification • Naïve Bayes classifier: factors conditional distribution of x given label v Lecture 25:Introduction to Bayesian Learning

  27. Lecture 28:NLP Survey • More on Simple Bayes, aka Naïve Bayes • Learning in Natural Language Processing (NLP) • Learning over text: problem definitions • Bayesian approaches to NLP • Issues: word sense disambiguation, part-of-speech tagging • Applications: spelling; reading/posting news; web search, IR, digital libraries • Layers: Syntax, Semantics, Pragmatics, Discourse • Problems: Scanning, Parsing, Typing (POS Tagging), Pragmatics, Discourse • Thursday: Final Exam Review

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