christoph f. eick: cosc 6368 and ‘what is ai?” 1 cosc 6368 and “what is ai?” 1.introduction...

Christoph F. Eick: COSC 6368 and ‘What is AI?” 1

COSC 6368 and “What is AI?”

1. Introduction to AI (today, and TH)• What is AI?• Sub-fields of AI• Problems investigated by AI research

2. Course Information


Part1a: Definitions of AI• “AI centers on the simulation of intelligence using

computers”• “AI develops programming paradigms, languages, tools,

and environments for application areas for which conventional programming fails”– Symbolic programming (LISP)– Functional programming – Heuristic Programming – Logical Programming (PROLOG)– Rule-based Programming (Expert system shells)– Soft Computing (Belief network tools, fuzzy logic tool boxes,…)– Object-oriented programming (Smalltalk)


More Definitions of AI

• Rich/Knight: ”AI is the study of of how to make computers do things which, at the moment, people do better”

• Winston: “AI is the study of computations that make it possible to perceive, reason, and act.

• Turing Test: If an artificial intelligent system is not distinguishable from a human being, it is definitely intelligent.


Physical Symbol System Hypothesis

• “What the brain does can be thought of at some level as a kind of computation”

• Physical Symbol System Hypothesis (PSSH): A physical symbol system has the sufficient and necessary means for general, intelligent actions.

Remarks PSSH:1. Subjected to empirical validation2. If false AI is quite limited3. Important for psychology and philosophy


Questions/Thoughts about AI• What are the limitations of AI? Can computers only do what they are told?

Can computers be creative? Can computers think? What problems cannot be solved by computers today?

• Computers show promise to control the current waste of energy and other natural resources.

• Computer can work in environment that are unsuitable for human beings.

• If computers control everything --- who controls the computers?

• If computers are intelligent what civil rights should be given to computers?

• If computers can perform most of our work; what should the human beings do?

• Only those things that can be represented in computers are important.

• It is fun to play with computers.


Topics Covered in COSC 6368• More general topics:

– heuristic search and search algorithm in general– logical reasoning (FOPL as a language)– making sense out of data

• AI-specific Topics:– resolution / theorem proving– reasoning in uncertain environments and belief networks– machine learning and data mining– brief coverage of planning, evolutionary computing,

knowledge-based systems and philosophical aspects of AI– Exposure to AI tools (belief networks, decision trees,…)


2009 Organization COSC 63681. Introduction AI and Course Information (1-2 classes) 2. Heuristic Search (4-5 classes) 3. Evolutionary Computing (2 classes) 4. FOPL, Logical Reasoning, Resolution, and PROLOG (3-4

classes)5. Inductive Learning, Reinforcement Learning, Brief Introduction

to Data Mining (4 classes) 6. Knowledge-based Systems and Expert Systems (1 class) 7. Planning (1-2 classes)8. Ontologies and Philosophical Aspects of AI (1-2 classes) 9. Belief Networks and Reasoning in Uncertain Environments (3-

4 classes) 10. Other Activities: midterm exam (1 class), review (2 classes),

homework/project-related discussions(1 class), possibly paper walk-through (1 class).


AI in General and What Is not Covered in COSC 6368

• Robotics is a quite important sub-field of AI, but very few teach it in the graduate AI class.

• Natural language understanding probably will not be covered.• Intelligent Agents and AI for the Internet could/should possibly

be covered in a little more depth.• Artificial intelligence programming is not covered.• Techniques employed in systems that automate decision

making in uncertain environments deserves more attention (e.g. fuzzy logic, rule-based programming languages and expert system shells, fuzzy controllers).


Positive Forces for AI

• Knowledge Discovery in Data and Data Mining (KDD)• Intelligent Agents for WWW• Robotics (Robot Soccer, Intelligent Driving, Robot

Waiters, industrial robots, rovers, toy robots…)• Creating of Knowledge Bases and Sharing of Knowledge

(especially for Science and Engineering)• Computer Chess and Computer Games in General --- AI

for Entertainment


6368 Homepage

• http://www2.cs.uh.edu/~ceick/6368.html

IJCAI 2009 Homepage http://ijcai-09.org/

http://www2.cs.uh.edu/~ceick/6368.html

http://ijcai-09.org/


Course Elements

21 Lectures• 3 Exams (two midterms, one final exam)• 4 Graded Assignments (review questions, exam style paper

and pencil problems, a few more challenging problems that might require programming; problems that require using AI tools; searching for something and reporting)

• Un-graded Homeworks (solutions will usually discussed in class)

• 1 Paper Walk-Throughs (group activity) if class size <20• Discussion of assignments and home works• We will try to use more demos and animations --- we have

to see if this turns out to be useful

AIIntelligent Agents & Distributed AI

Planning

Learning & Knowledge Discovery

Communicating,Perceiving and

Acting

Coping with Vague,Incomplete and

Uncertain Knowledge

Knowledge-basedand Expert Systems

Searching Intelligently

Logical Reasoning& Theorem Proving

Knowledge Representation AI Programming

Part1b:

Christoph F. Eick: COSC 6368 and ‘What is AI?”

Part1b: Examples of Problems Investigated by Different

Subfields of AI

13


Knowledge RepresentationProblem: Can the above chess board be cover by 31 domino piecesthat cover 2 fields?

AI’s contribution: object-oriented and frame-based systems, ontologylanguages, logical knowledge representation frameworks, belief networks


Natural Language Understanding

• I saw the Golden Gate Bridge flying to San Francisco.

• I ate dinner with a friend. I ate dinner with a fork.

• John went to a restaurant. He ordered a steak. After an hour John left happily.

• I went to three dentists this morning.


Planning

Objective: Construct a sequence of actions that will achieve a goal.

Example: John want to buy a house


Heuristic Search• Heuristo (greek): I find• Copes with problems for which it is not feasible to

look at all solutions• Heuristics: rules a thumb (help you to explore the

more promising solutions first), based on experience, frequently fuzzy

• Main ideas of heuristics: search space reduction, ordering solutions intelligently, simplifications of computations

Example problems: puzzles, traveling salesman problem, …


Figure


Evolutionary Computing• Evolutionary algorithms are global search techniques.• They are built on Darwin’s theory of evolution by natural

selection.• Numerous potential solutions are encoded in structures, called

chromosomes.• During each iteration, the EA evaluates solutions adn generates

offspring based on the fitness of each solution in the task.• Substructures, or genes, of the solutions are then modified

through genetic operators such as mutation or recombination.• The idea: structures that led to good solutions in previous

evaluations can be mutated or combined to form even better solutions.


Logical Reasoning

• Learn how to represents natural language statements in logic (AI as language)

• Automated theorem proving

• Foundation for PROLOG


Soft ComputingConventional Programming: • Relies on two-valued logic• Mostly uses a symbolic (non-numerical knowledge

representation framework)Soft Computing (e.g. Fuzzy Logic, Belief Networks,..):• Tolerance for uncertainty and imprecision • Uses weights, probabilities, possibilities• Strongly relies on numeric approximation and interpolation

Remark: There seem to be two worlds in computer science; one views the world as consisting of numbers; the other views the world as consisting of symbols.


• Learning agent receives feedback with respect to its actions (e.g. using a teacher)– Supervised Learning/Learning from

Examples/Inductive Learning: feedback is received with respect to all possible actions of the agent

– Reinforcement Learning: feedback is only received with respect to the taken action of the agent

• Unsupervised Learning: Learning without feedback

Different Forms of LearningDifferent Forms of Learning


Machine Learning Classification- Model Construction (1)

TrainingData

NAME RANK YEARS TENUREDMike Assistant Prof 3 noMary Assistant Prof 7 yesBill Professor 2 yesJim Associate Prof 7 yesDave Assistant Prof 6 noAnne Associate Prof 3 no

ClassificationAlgorithms

IF rank = ‘professor’OR years > 6THEN tenured = ‘yes’

Classifier(Model)


Classification Process (2): Use the Model in Prediction

Classifier

TestingData

NAME RANK YEARS TENUREDTom Assistant Prof 2 noMerlisa Associate Prof 7 noGeorge Professor 5 yesJoseph Assistant Prof 7 yes

Unseen Data

(Jeff, Professor, 4)

Tenured?


Knowledge Discovery in Data [and Data Mining] (KDD)

Let us find something interesting!

• Definition := “KDD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” (Fayyad)


2. General Course Information

Course Id: COSC 6368 Machine Learning

Time: TU/TH 1-2:30

Instructor: Christoph F. Eick

Classroom: 232 PGH

E-mail: [email protected]

Homepage: http://www2.cs.uh.edu/~ceick/


Prerequisites Background

• Algorithms

– basic data structures, complexity…

• Sound programming skills (no knowledge of LISP or PROLOG is requred)

• Ability to deal with “abstract mathematical concepts”

• Basic knowledge of logic would be helpful


Textbook

http://aima.cs.berkeley.edu/


Grading

2 Exams 60%4 Assignment 40%

NOTE: PLAGIARISM IS NOT TOLERATED.

Remark: Weights are subject to change


Tentative 2009 Teaching Plan (Subject To Change)Week Topic

Jan 20 Introduction / Search

Jan 27 Search

Feb. 3 Search/Evolutionary Computing (EC)

Feb. 10 EC, Logical Reasoning (LR)

Feb. 17 LR

Feb. 24 LR/Learning from Examples(LFE)

March 3 LFE/Reinforcement Learning

March 10 Review,/Midterm Exam

March 24 Leftovers/Knowledge-based Systems

March 31 Ontologies/ Philosophical Foundations of AI

April 7 Planning

April 14 Reasoning in Uncertain Environments (RIE)

April 21 RIE

April 28 RIE/Review for Final Exam

Remark: Topics in brown color may be skipped or replaced by something else


Dates to Remember

Dates to remember Events

Last day before Spring Break; May 12

Exams

March 17 /19 No class (Spring Break)


Exams

Will be open notes/textbookWill be open notes/textbook Will get a review list before the examWill get a review list before the exam Exams will center (80% or more) on material that was covered in the Exams will center (80% or more) on material that was covered in the lecturelecture Exam scores will be immediately converted into number gradesExam scores will be immediately converted into number gradesA few sample exams are available A few sample exams are available


Other UH-CS Courses with Overlapping Contents

• COSC 6342: COSC 6342: Machine LearningMachine Learning• Strong Overlap: Decision Trees, Bayesian Belief Networks, Strong Overlap: Decision Trees, Bayesian Belief Networks,

Learning from Examples in general Learning from Examples in general • Medium Overlap: Reinforcement LearningMedium Overlap: Reinforcement Learning

• COSC 6335: COSC 6335: Data MiningData Mining• Overlap: Decision trees, Learning from Examples in generalOverlap: Decision trees, Learning from Examples in general• Preprocessing/Exploratory DA, AdaBoostPreprocessing/Exploratory DA, AdaBoost

• COSC 6367: Evolutionary Computing • Overlap: SearchOverlap: Search• We also will have 2 lectures on Evolutionary ComputingWe also will have 2 lectures on Evolutionary Computing

christoph f. eick: cosc 6368 and ‘what is ai?” 1 cosc 6368 and “what is ai?” 1.introduction...

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