Course Announcement: Fall 2004,

Cognitive Science:

The Science of Intelligent Systems

Joint listed: CS 438, Psych 467

Tues & Thurs, 4:00 - 5:15
ME 214

 

Instructor:

George Luger, Computer Science, 277-3204, FEC 349E, luger@cs.unm.edu

Includes lectures by:

Tom Caudell, Dept of Electrical & Computer Engineering

Tim Goldsmith, Dept of Psychology

Fred Schuller, Dept. of Philosophy

Akaysha Tang, Depts of Psychology and Computer Science

Lance Williams, Dept of Computer Science

Ron Yeo, Dept of Psychology

 

Course Textbook:

Cognitive Science: The Science of Intelligent Systems,

Academic Press, 1994

by George Luger with P. Johnson, C. Stern, J. Neuman, & R. Yeo

Course Description:

 

Cognitive Science is concerned with the interdisciplinary effort of Cognitive Psychologists, Computer Scientists, Electrical Engineers, Linguists, Neuroscientists, Mathematicians, and Philosophers to identify the essential properties of intelligent systems. The basic assumption shared by this diverse group of scientists is that intelligence is a natural category, and as such, has a fundamental set of principles that describe its functioning. In fact, it requires the combined skills of this interdisciplinary scientific community to elucidate the issues surrounding intelligence. The lecturers present intelligence from the physical symbol system, connectionist, neurophysiological, philosophical, and cognitive perspectives.

 

Prerequisites:

 

Given the wide range of students taking this course we do not require any specific prerequisites, except that students be at the 400 course level in your own department. If you have any questions, give Prof Luger a call (or e-mail luger@cs.unm.edu). A more detailed syllabus is available.

Cognitive Science:

The Science of Intelligent Systems

Joint listed: CS 438, Psych 467

Syllabus:

 1. (weeks 1 & 2)

Introduction to Cognitive Science

General Background

Intelligence as a Natural Category

Definitions of Intelligence

Scope of the Course

Readings: Luger, Chapter 1

2. (week 3)

A Vocabulary for Intelligent Systems

Folk Psychology

Philosophical & Methodological Behaviorism

The Neuro-Science approach

The Automated Formal System

Readings: Luger, Chapter 2

3. (weeks 4 & 5)

The Representational Tools

Why Representations?

Computational tools

A psychological Methodology

Examples, including the neural network

Readings: Luger, Chapter 3

4. (week 6)

Constraining the Architecture of Minds

From viable to valid models of mind

Weak and strong equivalence

The methodology

Is strong equivalence possible?

Readings: Luger, Chapter 4

5. (weeks 7 & 8)

Neurophisiological Aspects of Intelligence

The Celular Basis of Learning

Levels of Neurological Organization

Cortical Resourses and Connectivity

Parallel Computations and Mappings

Readings: Luger, Chapter 5

A mid-term exam will take place about the eighth week

 

6. (weeks 9 & 10)

Representations from the Artificial Intelligence Tradition

The Semantic Network

Conceptual Dependencies and Scripts

Frames and Schemas

Objects and Inheritance Systems

Readings: Luger, Part II, Ch 6

7. (weeks 11 & 12)

Alternative Models of Cognition

The Problem of Learning

Genetic and Emergent Models

Language Generation and Understanding

Semanticis, Pragmatics, and Reference

Readings: Luger, Part V, Ch 14 & 15

8. (weeks 13 & 14)

Neural Networks and the Connectionist Approach

Introduction

The Connectionist Models

Neural Realism & Neural Network Approaches

Extensions and Limitations

Readings: Luger, Part III, Ch 11 & 12

8. (week 15)

Selected Advanced Topics

Newell/Simon vs Connectionist Approaches

Architectures for Multi-Agent Problem Solving

The Limitations of the Scientific Method

Context and the Role of Society

Readings: Luger, Part IV, Chapter 16

10. (week 16)

Course Review

Semantic Grounding Issues

Philosophical Challenges

Strong Equivalence Revisited

Summary of Progress in Cognitive Science

Final Exam and Term Papers due at end of the semester

 

Course Credit:

Midterm exam: 30%

Final Exam: 30%

Course Paper: 30%

Other small assignments: 10%

 

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