COSC 6368 and “What is AI?”

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

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 Organization

3. Prerequisites, Schedules, Grading, General Advice


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 ontologies, evolutionary computing, AI and the

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


2004 Organization COSC 63681. Introduction AI and Course Information (1-2 classes) 2. Heuristic Search (3-4 classes) 3. Evolutionary Computing (1-2 classes) 4. FOPL, Logical Reasoning, PROLOG, and Resolution (4

classes)5. Machine Learning and Data Mining (5 classes) 6. Ontologies, the Semantic Web and Intelligent Information

Retrieval (2 classes)7. Belief Networks and Reasoning in Uncertain Environments (3

classes) 8. Knowledge-based Systems and Expert Systems (1 class) 9. General Aspects of AI (1 class) 10. Other Activities: Midterm exam (1 class), review (1 class),

group project (1 class), homework/project-related discussions(1 class), 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.

• Planning and 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 --- AI for

Entertainment


Topics investigated:• Clustering for Classification• Decision Trees / Nearest Neighbor Classifiers / Support Vector Machines• Theoretical Aspects of Classifiers and Classification Tasks• Supervised Clustering• Summary Generation• Distance Function Learning• Using Reinforcement Learning for Data Mining• Making Sense of Data• Database Clustering• Feature Construction• Meta-Learning• Application of AI to Physics and Astronomy

UH Data Mining and Machine Learning Group Lead by: Ricardo Vilalta and Christoph F. Eick

UH Data Mining and Machine Learning Group Lead by: Ricardo Vilalta and Christoph F. Eick


Course Elements

22 Lectures• 2 Exams (one Midterm, 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)

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

• 1 Paper Walk-Throughs • Discussion of assignments and homeworks


Teaching 6368 in 2004 ---what changed since 2002?

• The second edition of the Russel/Norvig book came out in November 2002 (update of teaching material; better transparencies)

• Some 2002 teaching material will be replaced by other teaching material

• A lot more AI technique animations are available now --- I will try to use some of those for teaching purposes

• There will be some changes what will be covered in 2004 (see webpage)

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


Knowledge RepresentationProblem: Can the above chess board be coverer 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


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: 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)


2004 Organization COSC 63681. Introduction AI and Course Information (1-2 classes) 2. Heuristic Search (3-4 classes) 3. Evolutionary Computing (1-2 classes) 4. FOPL, Logical Reasoning, PROLOG, and Resolution (4

classes)5. Machine Learning and Data Mining (5 classes) 6. Ontologies, the Semantic Web and Intelligent Information

Retrieval (2 classes)7. Belief Networks and Reasoning in Uncertain Environments (3

classes) 8. Knowledge-based Systems and Expert Systems (1 class) 9. General Aspects of AI (1 class) 10. Other Activities: Midterm exam (1 class), review (1 class),

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

Documents

COSC 6368 and “What is AI?”