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)

CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 1: The Intelligent Agent Framework

Artificial Intelligence (AI)

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Operational definition : study / development of systems capable of “thought processes” (reasoning, learning, problem solving)

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Constructive definition: expressed in artifacts (design and implementation) Intelligent Agents Topics and Methodologies

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Knowledge representation

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Logical Uncertain (probabilistic) Other (rule-based, fuzzy, neural, genetic)

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Search Machine learning Planning Applications

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Problem solving, optimization, scheduling, design Decision support, data mining Natural language processing, conversational and information retrieval agents Pattern recognition and robot vision CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 2: Agents and Problem Solving

Agent Frameworks

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Reactivity vs. state From goals to preferences (utilities) Applications and Automation Case Studies

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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

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Resources page: www.kddresearch.org/Courses/Fall-2001/CIS730/Resources

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Yahoo! Group discussions: groups.yahoo.com/group/ksu-cis730-fall2001 Suggested project topics, resources – posted in YG CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 3: Search and Constraints

Today’s Reading: Sections 3.5-3.8, Russell and Norvig Solving Problems by Searching

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Problem solving agents: design, specification, implementation Specification components

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Problems Solutions – formulating well-defined ones – requirements, constraints

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Measuring performance Formulating Problems as (State Space) Search Example Search Problems

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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

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State space search handout (Winston) Search handouts (Ginsberg, Rich and Knight) CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

Lecture 4: Uninformed Search Algorithms

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Search

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Problem formulation: state space (initial / operator / goal test / cost), graph State space search approaches

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Blind (uninformed) Heuristic (informed) Applications

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Problem solving

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Optimization

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Scheduling Design

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Machine learning (hypothesis space search) More Resources Online

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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

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Part I: format Part II: writing quality and criteria Part III: resources and suggested topics CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 5: Heuristic Search Algorithms – Greedy, A*

More Heuristic Search

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Best-First Search

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Greedy

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A/A*

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Search as function maximization

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Problems: ridge; foothill; plateau, jump discontinuity Solutions: macro operators; global optimization Constraint Satisfaction Search Next Class: IDA*, Hill-Climbing, Iterative Improvement

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Gradient descent Global search

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MCMC: intuition Some examples of state-of-the-art applications Properties and tradeoffs CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 6: More Heuristic Search – A*, Hill-Climbing / SA

More Heuristic Search

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Best-First Search: A/A* concluded Iterative improvement

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Hill-climbing Simulated annealing (SA)

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Search as function maximization

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Problems: ridge; foothill; plateau, jump discontinuity

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Solutions: macro operators; global optimization (genetic algorithms / SA) Next Class: Constraint Satisfaction Search, Heuristic Search Next Week: Adversarial Search (e.g., Game Tree Search)

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Competitive problems Minimax algorithm CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 7: Constraint Satisfaction Problems

Constraint Satisfaction Problems (CSPs)

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Problem definition

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Domain

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Constraints

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Examples: N-queens, cryptarithmetic, etc.

Issues to be Covered Later

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Knowledge representation: how to express domain, constraints Relational constraints

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In classical logic (propositional, predicate, first-order) In uncertain reasoning Solving CSPs

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Propositional constraints: satisfiability solver First-order relational constraints: difficulties – later Speeding up CSPs: iterative improvement

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Gradient (hill-climbing) optimization

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Simulated annealing CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 8: Game Tree Search: Minimax

Game Graph Search

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Frameworks

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Two-player versus multi-player

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Zero-sum versus cooperative Perfect information versus partially-observable (hidden state)

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Concepts

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Utility and representations (e.g., static evaluation function)

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Reinforcements: possible role for machine learning Game tree: node/move correspondence, search ply Family of Algorithms for Game Trees: Minimax

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Propagation of credit Imperfect decisions Issues

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Quiescence Horizon effect

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Need for (alpha-beta) pruning CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 9: More Game Tree Search:

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-



, Expectiminimax

Games as Search Problems

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Frameworks Concepts: utility, reinforcements, game trees Static evaluation under resource limitations Family of Algorithms for Game Trees: Minimax

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Static evaluation algorithm

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To arbitrary ply To fixed ply Sophistications: iterative deepening, pruning

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Credit propagation

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Intuitive concept Basis for simple (delta-rule) learning algorithms State of The Field Uncertainty in Games: Expectiminimax and Other Algorithms CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 10: Logical Agents and Knowledge Representations

Logical Agents

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Knowledge Bases (KB) Logic in general

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Representation languages, syntax Inference systems

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Calculi

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Propositional

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First-order (FOL, FOPC) Possible Worlds

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Entailment Models IA Toy Worlds

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Wumpus world Blocks world CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 11: Propositional and Predicate Logic

Logical Frameworks

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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

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Conjunctive Normal Form (CNF) Disjunctive Normal Form (DNF) Horn Form Proof Theory and Inference Systems

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Sequent calculi: rules of proof theory Derivability or provability Properties

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Soundness (derivability implies entailment)

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Completeness (entailment implies derivability) CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 12: Foundations of First-Order Logic

FOL in Practice

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FOL agents Example: Wumpus World in FOL Situation calculus Frame problem and variants (see R&N sidebar)

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Representational vs. inferential frame problems Qualification problem: “what if?” Ramification problem: “what else?” (side effects)

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Successor-state axioms Logical Languages

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Propositional logic Predicates, terms, functions, atoms (atomic sentences / atomic WFFs), WFFs First-order logic (FOL, FOPC): universal and existential quantification CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 13: First-Order Knowledge Bases

Properties of Knowledge Bases (KBs)

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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 CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 14: Resolution Theorem Proving

Resolution Theorem Proving

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Conjunctive Normal Form (clausal form) Inference rule

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Single-resolvent form General form

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Proof procedure: refutation Decidability properties

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FOL-SAT FOL-NOT-SAT (language of unsatisfiable sentences; complement of FOL-SAT)

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FOL-VALID FOL-NOT-VALID Next Class

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More Prolog Implementing unification CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 15: Logic Programming Techniques

Properties of Proof Systems (Again)

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Soundness and completeness Decidability, semi-decidability, undecidability Resolution Refutation Satisfiability, Validity Unification

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Occurs check Most General Unifier Prolog: Tricks of The Trade

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Demodulation, paramodulation Unit resolution, set of support, input / linear resolution, subsumption Indexing (table-based, tree-based) CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 16: Classical Planning

Classical Planning

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Planning versus search Problematic approaches to planning

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Forward chaining Situation calculus

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Representation

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Initial state

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Goal state / test Operators Efficient Representations

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STRIPS axioms

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Components: preconditions, postconditions (ADD, DELETE lists)

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Clobbering / threatening

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Reactive plans and policies Markov decision processes CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 17: Partial-Order Planning

Classical Planning Framework

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Planning versus search Representation: initial state, goal state / test, operators STRIPS Operators

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Components: preconditions, postconditions (ADD, DELETE lists) STRIPS and interference

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Clobbering / threatening Promotion / demotion

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Partial-Order Planners (POP systems) Next Week

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Hierarchical abstraction planning: ABSTRIPS Conditional plans Reactive plans and policies Markov decision processes CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 18: STRIPS and ABSTRIPS

Classical Planning Framework

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Planning versus search Representation: initial state, goal state / test, operators STRIPS Operators

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Components: preconditions, postconditions (ADD, DELETE lists) STRIPS and interference

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Clobbering / threatening Promotion / demotion

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Partial-Order Planners (POP systems) Next Week

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Hierarchical abstraction planning: ABSTRIPS Conditional plans Reactive plans and policies Markov decision processes Adapted from slides by S. Russell, UC Berkeley CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 19: Reaction and Replanning

Classical Planning Framework

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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

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Hierarchical abstraction planning: ABSTRIPS Conditional plans This Week

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Monitoring and replanning Reactive plans and policies Later

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Decision theory Markov decision processes CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 20: Reasoning under Uncertainty

Introduction to Probabilistic Reasoning

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Framework: using probabilistic criteria to search H Probability foundations

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Definitions: subjectivist, objectivist; Bayesian, frequentist, logicist Kolmogorov axioms Bayes’s Theorem

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Definition of conditional (posterior) probability Product rule Maximum A Posteriori (MAP) and Maximum Likelihood (ML) Hypotheses

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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

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Later: Bayesian learning: MDL, BOC, Gibbs, Simple (Naïve) Bayes Categorizing text and documents, other applications CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 21: Introduction to Bayesian Networks

Graphical Models of Probability

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Bayesian belief networks (BBNs) aka belief networks aka causal networks Conditional independence, causal Markovity Inference and learning using Bayesian networks

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Representation of distributions: conditional probability tables (CPTs)

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Learning polytrees (singly-connected BBNs) and tree-structured BBNs (trees) BBN Inference

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Type of probabilistic reasoning Finds answer to query about P(x) - aka QA Learning in BBNs: In Two Weeks

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Known structure Partial observability CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 22: Introduction to Machine Learning

Taxonomies of Learning Definition of Learning: Task, Performance Measure, Experience Concept Learning as Search through H

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Hypothesis space H as a state space Learning: finding the correct hypothesis General-to-Specific Ordering over H

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Partially-ordered set: Less-Specific-Than (More-General-Than) relation Upper and lower bounds in H Version Space Candidate Elimination Algorithm

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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?

CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 23: Decision Trees

(Inductive) Bias: Preference for Some h

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H (Not Consistency with D Only) Decision Trees (DTs)

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Boolean DTs: target concept is binary-valued (i.e., Boolean-valued) Building DTs

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Histogramming: a method of vector quantization (encoding input using bins) Discretization: continuous input

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discrete (e.g.., by histogramming) Entropy and Information Gain

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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

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Algorithm Build-DT: greedy search (hill-climbing without backtracking) ID3 as Build-DT using the heuristic Gain (•) Heuristic : Search :: Inductive Bias : Inductive Generalization MLC++ (Machine Learning Library in C++)

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Data mining libraries (e.g., MLC++) and packages (e.g., MineSet) Irvine Database: the Machine Learning Database Repository at UCI CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 24: Perceptrons and Artificial Neural Networks

Neural Networks (NNs): Parallel, Distributed Processing Systems

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Biological NNs and artificial NNs (ANNs) Perceptron aka Linear Threshold Gate (LTG), Linear Threshold Unit (LTU)

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Model neuron Combination and activation (transfer, squashing) functions Multi-Layer ANNs

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Focused on one species: (feedforward) multi-layer perceptrons (MLPs) Input layer: an implicit layer containing x

i

Hidden layer: a layer containing input-to-hidden unit weights and producing h

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Output layer: a layer containing hidden-to-output unit weights and producing o

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n-layer ANN: an ANN containing n - 1 hidden layers Epoch: one training iteration Overfitting

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Overfitting: h does better than h’ on training data and worse on test data Prevention, avoidance, and recovery techniques CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

Lecture 25: Introduction to Bayesian Learning

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Minimum Description Length (MDL)

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Bayesian Information Criterion (BIC) BIC = additive inverse of MDL (i.e., BIC(h) = -MDL(h))

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Bayesian Classification: Finding Most Probable v Given Examples x

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Bayes Optimal Classifier (BOC)

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Probabilistic learning criteria: measures of P(prediction | D) or P(hypothesis | D) BOC: a gold standard for probabilistic learning criteria

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Gibbs Classifier

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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

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Simple Bayes aka Naïve Bayes

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Assumption of conditional independence of attributes given classification Naïve Bayes classifier: factors conditional distribution of x given label v

v NB

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max v j

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V

 

j



i



x i | v j



CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences

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Lecture 28: NLP Survey

More on Simple Bayes, aka Naïve Bayes Learning in Natural Language Processing (NLP)

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Learning over text: problem definitions Bayesian approaches to NLP

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Issues: word sense disambiguation, part-of-speech tagging

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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 CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences