Artificial Intelligence (AI) Course Group Status Report

AI Courses
Course no. Title Credit 
Hours		 Reqd (R)/ 
Elective (E)
CIS 612
Introduction to Cognitive Science
3
E
CIS 630
Survey of Artificial Intelligence I: Basic Techniques
3
E
CIS 730
Survey of Artificial Intelligence II: Advanced Topics
3
E
CIS 731
Knowledge-Based Systems
4
E
CIS 732
Computational Linguistics
3
E
CIS 737
Proseminar in Cognitive Science
2
E
CIS 739
Knowledge-Based Systems in Engineering
3
E
CIS 776
Hardware/Software Interface Design Project
4
E
CIS 779
Introduction to Artificial Neural Networks
3
E

1. Summary

Artificial Intelligence (AI) is a major area of computer science, broadly concerned with how to make computers "smart". It is one of the focus areas of the Department.

The field of AI continues to evolve at a rapid pace, fueled by the ancient dream of the mankind to make a machine that behaves like a man, as well as by the research and development taking place in industry at companies such as Microsoft, IBM, AT&T, Lucent, GE, just to name a few. In addition, AI has become indispensable in a variety of application areas because of advances in computer vision, speech and language processing, inference engines, knowledge-based systems, and robotics. AI has also become a main bridge between computer science and many other disciplines, such as other engineering disciplines, cognitive science, linguistics, psychology, neuroscience, math/physics/statistics, and medicine.

We have evolved the AI curriculum continuously to keep pace with the development of the field, the interests of students, and the demands of employers. We currently have nine regular courses in AI, many of which are cross-listed with other departments, reflecting the interdisciplinary nature of the field. The 630/730 sequence is the core introduction to AI. The AI faculty also offer research seminars that cover recent developments in computer vision, neural networks, speech processing, knowledge systems, and cognitive science. Developing new courses on these topics is a priority of the AI faculty.

The AI curriculum satisfies many of the CSE and ABET objectives. On the application side, AI is an area in which students can exercise the principles of software design, databases, algorithms, data structures and programming languages which they have learned in the foundation courses. The AI courses rely heavily on the application of various principles from mathematics, physics, statistics, neuroscience, and psychology. Students who study AI in the Department receive a good grounding in the area, are usually ready for graduate studies in AI and cognitive science, and are very employable.

2. Detailed Analysis

The CIS department regularly offers the following AI courses: CIS 630, CIS 730, CIS 731, CIS 739, CIS 776, and CIS 779.  It also offers two courses in cognitive science: CIS 612 and CIS 737 on a regular basis. 

Section 2.1 describes the individual courses in the group. Section 2.2 explains how the group is related to the rest of the CSE program. Section 2.3 explains how the group helps meet a range of CSE and ABET objectives. Section 2.4 provides information on the feedback we have received from students, recruiters, etc., about the courses in the group. Section 2.5 summarizes the changes and new developments we are considering.

2.1 Summary of the courses

CIS 612: Introduction to Cognitive Science  provides an introduction to the interdisciplinary study of the nature of human thought, and psychological, philosophical, linguistic, and artificial intelligence approaches to knowledge representation. The course is cross-listed with the Departments of Linguistics, Philosophy, and Psychology.

CIS 630: Survey of Artificial Intelligence I: Basic Techniques  is a survey of the basic concepts and techniques of problem solving paradigms and knowledge representation schemes in Artificial Intelligence. This is the entry-level AI course, and is a prerequisite for many subsequent AI courses.

CIS 730: Survey of Artificial Intelligence II: Advanced Topics is a survey of advanced concepts, techniques, and applications of artificial intelligence, including perception, learning, reasoning under uncertainty, planning, natural language processing, and robotics. This course continues the introduction of artificial intelligence in CIS 630.

CIS 731: Knowledge-based Systems is a survey of theory and practice of expert systems and knowledge-based systems, and makes use of current knowledge-based systems software tools. This is a BSCSE capstone design course.

CIS 732: Computational Linguistics is an exploration of the computational processing of natural language; syntactic, semantic, and pragmatic processing techniques are applied to understanding and generating written English. This course is not recently offered, but work is underway to make it cross-listed with the Department of Linguistics and thus taught regularly.

CIS 737: Proseminar in Cognitive Science is an in-depth examination of the interdisciplinary field of cognitive science, and it emphasizes fundamental issues of each discipline and provides illustrations of representative research being conducted at OSU. This is the second course in cognitive science, subsequent to CIS 612. The course is cross-listed with ISE, Linguistics, Philosophy, Psychology, and Speech and Hearing Science.

CIS 739: Knowledge-Based Systems in Engineering is an application of knowledge-based system principles to engineering problems, including practical knowledge engineering, techniques for problem assessment, and implementation. This course is cross-listed with Chemical, Mechanical, and Nuclear Engineering Departments.

CIS 776: Hardware/Software Interface Design Project introduces principles and applications of hardware and software design: design, programming, testing, and evaluation of an autonomous mobile robot system.

CIS 779: Introduction to Artificial Neural Networks is a survey of fundamental methods and techniques of the field of artificial neural networks. Single-layer and multi-layer feedforward networks; Associative memory models; Recurrent and statistical networks; Supervised and unsupervised learning rules; Self-organization networks; Applications to signal processing, pattern recognition/generation, and optimization problems. This course is cross-listed with Electrical Engineering.

2.2 Relation of the courses to the rest of the CSE program

CIS 612: Introduction to Cognitive Science. Prerequisites for CIS 612 are the permission of instructor and a total of 12 credit hours from at least two of the following areas: computer science, linguistics, philosophy, and psychology.

CIS 630: Survey of Artificial Intelligence I: Basic Techniques. Prerequisites for CIS 630 are CIS 222 (Development of Software Components) and Math 366 (Discrete Mathematical Structures I).

CIS 730: Survey of Artificial Intelligence II: Advanced Topics. Prerequisite for CIS 730 is CIS 630 (Survey of Artificial Intelligence I: Basic Techniques).

CIS 731: Knowledge-based Systems. Prerequisite for CIS 731 is CIS 630 (Survey of Artificial Intelligence I: Basic Techniques) or permission of instructor.

CIS 732: Computational Linguistics. Prerequisites for CIS 732 are CIS 730 (Survey of Artificial Intelligence II: Advanced Topics); Linguistics 601 (Introduction to Linguistics) or permission of instructor.

CIS 737: Proseminar in Cognitive Science. Prerequisite for CIS 737 is CIS 612 (Introduction to Cognitive Science) or permission of instructor.

CIS 739: Knowledge-Based Systems in Engineering. Prerequisite for CIS 739 is CIS 630 (Survey of Artificial Intelligence I: Basic Technique) or permission of instructor.

CIS 776: Hardware/Software Interface Design Project. Prerequisites for CIS 776 are CIS 459.21 (Programming in C), CIS 660 (Introduction to Operating Systems); EE 567 (Microprocessor Laboratory I) or EE 329 (Electronic Devices and Control Laboratory).

CIS 779: Introduction to Artificial Neural Networks. Prerequisite for CIS 779 is CIS 630 (Survey of Artificial Intelligence I: Basic Techniques).

2.3 Relation to CSE and ABET objectives

The courses in this group play a significant role in meeting both CSE program objectives as well as ABET Criterion 3 objectives. In section 2.3.1 we consider the CSE objectives that this course group helps us meet, and in section 2.3.2 we consider the ABET objectives.

2.3.1 CSE Objectives

Objective 1.To provide graduates with a thorough grounding 
in the key principles and practices of computing, and in 
the basic engineering, mathematical, and scientific principles
that underpin them. Students will: 
  a.Demonstrate proficiency in the areas of software 
    design and development, algorithms, operating systems, 
    programming languages, and architecture. 
  b.Demonstrate proficiency in relevant aspects of mathematics, 
    including discrete mathematics, as well as the appropriate 
    concepts from physics and electrical circuits and devices. 
  c.Successfully apply these principles and practices to a 
    variety of problems. 
On the application side, AI is an area in which students can exercise the principles of software design, databases, algorithms, data structures, programming languages and architectures which they have learned in the foundation courses. AI relies heavily on basic mathematical and engineering principles, including calculus, algebra, geometry, discrete math, statistics, and signal processing. AI courses typically involve problem solving in the form of labs, and emphasize practical applications. 
Objective 2.To provide graduates with an understanding 
of additional engineering principles, and the mathematical 
and scientific principles that underpin them. Students will: 
   a.Demonstrate an understanding of differential and 
     integral calculus, differential equations, physics 
     and several areas of basic engineering sciences. 
   b.Have the ability to work with others and on 
     multi-disciplinary teams in both classroom and 
     laboratory environments.
Several AI courses are intimately concerned with developing the principles of mathematical analysis and physics into computational models for artifacts and neural systems. Such principles are also employed in machine perception and machine learning. 
Objective 3.To provide graduates with an understanding of 
the overall human context in which engineering and 
computing activities take place. Students will: 
  a.Demonstrate an ability to communicate effectively. 
  b.Obtain familiarity with basic ideas and contemporary 
    issues in the social sciences and humanities. 
  c.Obtain an understanding of social, professional 
    and ethical issues related to computing. 
As a highly interdisciplinary field, AI serves as a main bridge between computer science and other studies concerning human cognition and the overall human context within which computing takes place. The two cognitive science courses, CIS 612 and CIS 737, provide necessary knowledge for an understanding of AI in the general context of the study of the brain and the mind. Human computer interaction in multiple modalities, such as language and gesture, is also a major concern of AI. 
Objective 4.To prepare graduates for both immediate 
employment in the CSE profession and for admission to 
graduate programs in computing. 
   a.A large fraction of graduates will be immediately 
     employed in high-technology companies that utilize 
     their computing education. 
   b.Strong graduates from the program will be prepared 
     to enter good graduate programs in CSE
As a result of increased computing power, more sophisticated user demand, and the Internet, AI training is becoming more important to industry in everything from smart word processing, speech recognition, intelligent search techniques for web search engines to autonomous vehicles (e.g. the Pathfinder on the Mars) and undersea robots. The AI curriculum in the Department covers the basics of AI thoroughly. Students who study AI in the Department receive a good grounding in the area, are usually ready for graduate studies in AI/Cognitive Science, and are very employable.
 
Summary of Relation to CSE Objectives
Course no. CSE
1a		 CSE
1b		 CSE
1c		 CSE
2a		 CSE
2b		 CSE
3a		 CSE
3b		 CSE
3c		 CSE
4a		 CSE
4b
CIS 612          XX  XXX  XXX      X
CIS 630
X
XX
XXX
   XX XX XX   XX
XXX
CIS 730
X
XX
XXX
   XX XX XX   XX
XXX
CIS 731
X
X
XXX
  
XX
XX
X
  
XX
X
CIS 732
X
X
XX
  
XX
XXX
XX
  
X
 XX
CIS 737          XX  XXX  XXX      X
CIS 739
X
X
XXX
  
XX
XX
X
  
XX
X
CIS 776
XXX
XXX
XX
XX
XXX
XX
    
XX
XX
CIS 779
X
XXX
XX
XXX
XXX
X
XX
  
XX
XX


2.3.2 ABET Criterion 3: Program Outcomes and Assessment

Engineering programs must demonstrate their graduates have:

   a.  an ability to apply knowledge of mathematics, science, and engineering
   b.  an ability to design and conduct experiments, as well as analyze and interpret data
   c.  an ability to design a system, component, or process to meet desired needs
   d.  ability to function on multi-disciplinary teams
   e  an ability to identify, formulate, and solve engineering problems
   f.  an understanding of professional and ethical responsibility
   g.  an ability to communicate effectively
   h.  the broad education necessary to understand the impact of engineering solutions in a global and societal context
   i.  a recognition of the need for, and an ability to engage in life-long learning
   j.  a knowledge of contemporary issues
   k.  an ability to use techniques, skills, and modern engineering tools necessary for engineering practice.

The AI courses contribute strongly to a number of ABET objectives. The AI courses are heavily oriented towards interdisciplinary education involving other engineering and scientific disciplines, problem solving through a variety of labs, and theoretical foundations.
 

Summary of Relation to ABET Objectives
Course no. ABET
3a		 ABET
3b		 ABET
3c		 ABET
3d		 ABET
3e		 ABET
3f		 ABET
3g		 ABET
3h		 ABET
3i		 ABET
3j		 ABET
3k
CIS 612  
X
XX
 XXX     XXX XXX
X
X
X
CIS 630
XX
X
 XXX
XX
X
  
XX
XX
X
X
XX
CIS 730
XX
X
 XXX
XX
X
  
XX
XX
X
XX
XX
CIS 731
X
X
XXX
XX
XXX
  
XX
X
X
XX
XXX
CIS 732
X
X
XXX
XX
X
  
XXX
XX
X
XX
X
CIS 737  
X
XX
 XXX     XXX XXX
X
XXX
X
CIS 739
X
X
XXX
XX
XXX
  
XX
X
X
XX
XXX
CIS 776
XXX
X
XXX
XX
XXX
  
XX
  
X
X
XXX
CIS 779
XXX
X
XXX
XXX
XXX
  
X
XX
X
XX
XXX


2.4 Feedback

As elective courses, the popularity of AI courses speaks to the success of the curriculum. Students who have gone out to industry have reported back concerning the satisfaction of their AI education. Student evaluations have also expressed the value of the material. It is not uncommon for employment agencies to contact the AI faculty concerning the possibility of locating potential employees. And finally, both undergraduate and graduate student applicants often select OSU specifically because of its reputation in AI instruction and research.


2.5 Possible changes

The AI faculty have been discussing possible changes and enhancements to the AI curriculum. Textbooks for several AI courses have been changed recently. These changes reflect the changing nature of the field. In particular, the AI courses now emphasize skills and techniques for solving real-world problems.

Currently effort is underway to create several more specialized courses in AI, which reflect the research activity in the AI group at OSU. Three courses are being planned for computer vision, speech and language processing, and pattern recognition and machine learning, respectively. These courses are being designed in the context of and integrated with existing AI courses for the purpose of coherent, modern AI education.


3. Conclusions

AI is a popular application area for the CSE program and serves as a testbed for the core programming concepts and techniques taught in the spine of the curriculum. AI is also an intrinsically interdisciplinary area. The AI courses, as they currently stand, are doing well; students are generally satisfied with the courses. We are placing students in with top-notch employers and graduate programs in the field and there is strong faculty commitment to the courses.
 
Course no. Coordinator Recent Instructors
CIS 612 Palmer Myung, Palmer
CIS 630 Zhu Chandrasekaran, Davis, Lewis, Wang, Zhu
CIS 730 Wang Wang, Zhu 
CIS 731
 Davis Davis, Lewis
CIS 732
 
Not offered recently 
CIS 737
 Wang
 Todd, Wang
CIS 739
 Adeli
Adeli 
CIS 776
 Zhu Reeder, Zhu
CIS 779
 Wang
Ahalt, Wang 

People involved in preparing report: Leon Wang, with comments from Jim Davis and Song-Chun Zhu.

Date of report: Feb. 2001 

Leon Wang

Feb. 2001.