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COSC 6368 and “What is AI?”. Introduction to AI (today, and TH) What is AI? Sub-fields of AI Problems investigated by AI research Course Information. Part1a: Definitions of AI. “AI centers on the simulation of intelligence using computers”

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Cosc 6368 and what is ai
COSC 6368 and “What is AI?”

  • Introduction to AI (today, and TH)

    • What is AI?

    • Sub-fields of AI

    • Problems investigated by AI research

  • Course Information

Part1a definitions of ai
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
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
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:

  • Subjected to empirical validation

  • If false  AI is quite limited

  • Important for psychology and philosophy

Questions thoughts about ai
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
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 6368
2009 Organization COSC 6368

  • Introduction AI and Course Information (1-2 classes)

  • Heuristic Search (4-5 classes)

  • Evolutionary Computing (2 classes)

  • FOPL, Logical Reasoning, Resolution, and PROLOG (3-4 classes)

  • Inductive Learning, Reinforcement Learning, Brief Introduction to Data Mining (4 classes)

  • Knowledge-based Systems and Expert Systems (1 class)

  • Planning (1-2 classes)

  • Ontologies and Philosophical Aspects of AI (1-2 classes)

  • Belief Networks and Reasoning in Uncertain Environments (3-4 classes)

  • 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
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
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
6368 Homepage


IJCAI 2009 Homepage

Course elements
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

AI Programming

Knowledge Representation


and Expert Systems




Coping with Vague,

Incomplete and

Uncertain Knowledge



Logical Reasoning

& Theorem Proving


Perceiving and


Intelligent Agents

& Distributed AI

Learning & Knowledge Discovery

Part1b examples of problems investigated by different subfields of ai
Part1b: Examples of Problems Investigated by Different Subfields of AI

Knowledge representation
Knowledge Representation

Problem: Can the above chess board be cover by 31 domino pieces

that cover 2 fields?

AI’s contribution: object-oriented and frame-based systems, ontology

languages, logical knowledge representation frameworks, belief networks

Natural language understanding
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.


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

Example: John want to buy a house

Heuristic search
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, …

Evolutionary computing
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
Logical Reasoning

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

  • Automated theorem proving

  • Foundation for PROLOG

Soft computing
Soft Computing

Conventional 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.

Different Forms of Learning

  • 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

Machine learning classification model construction 1





Machine Learning Classification- Model Construction (1)



IF rank = ‘professor’

OR years > 6

THEN tenured = ‘yes’

Classification process 2 use the model in prediction




Unseen Data

Classification Process (2): Use the Model in Prediction

(Jeff, Professor, 4)


Knowledge discovery in data and data mining kdd
Knowledge Discovery in Data [and Data Mining] (KDD)

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

Let us find something interesting!

2 general course information
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]




  • 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



2 Exams 60%

4 Assignment 40%

Remark: Weights are subject to change


Tentative 2009 teaching plan subject to change
Tentative 2009 Teaching Plan (Subject To Change)

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


  • Will be open notes/textbook

  • Will get a review list before the exam

  • Exams will center (80% or more) on material that was covered in the lecture

  • Exam scores will be immediately converted into number grades

  • A few sample exams are available

Other uh cs courses with overlapping contents
Other UH-CS Courses with Overlapping Contents

  • COSC 6342: Machine Learning

    • Strong Overlap: Decision Trees, Bayesian Belief Networks, Learning from Examples in general

    • Medium Overlap: Reinforcement Learning

  • COSC 6335: Data Mining

    • Overlap: Decision trees, Learning from Examples in general

    • Preprocessing/Exploratory DA, AdaBoost

  • COSC 6367: Evolutionary Computing

    • Overlap: Search

    • We also will have 2 lectures on Evolutionary Computing