Title: Knowledge-Based and Decision Support Systems  ISS534
     
Time:          TBA
Instructor:    Dr. Michael E. Thombs
Office Loc:    O'Hare 204, Ex. 3115

Prerequisite:  ISS502

Purpose:

       Expert Systems (ES) and Artificial Intelligence (AI) was once
     considered by some authorities as part of a broader field called
     Decision Support Systems (DSS).  Over the past 20 years, AI and 
     ES matured and became separate fields of study.  It is ironic to 
     see that this trend is reversing and the fields are coming 
     together again.  Today, there are many misconceptions about the
     domains covered by the titles: DSS, AI, and ES. This course will
     introduce the topic of AI and explore its constituent parts.
     The course includes working exercises using 1stClass Expert System
     Shell, VP-Expert System Shell, Lotus 1-2-3, and PROLOG.  Students 
     will perform laboratory exercises that will help them improve 
     their appreciation and awareness of commercial software.

Approach for Part - I:  
     
       This part of the course will cover two thirds of the curriculum
     and provide two thirds of the earned grade.  All assignments for 
     Part - I of the course will be assigned prior to week 9 and by the
     end of the fall semester a partial grade will be calculated.  The
     partial grade for this section will be added to your partial grade 
     for the second section; two thirds KBS / one third: DSS.  You will
     have the entire semester to complete project assignments.  
     
       Class sessions will consist of lectures, discussions, oral 
     reports by students, and software demonstrations using a portable
     computer with an overhead projection device, and supervised
     laboratory time.  
       
   

Required Texts:
   
   - Building and Using Expert Systems with 1stClass.
     Mocciola. ( WorkBook )

   - Developing Knowledge-Based Systems using VP-Expert.
     Dologite, MacMillian:  ISBN:0-02-3811886-7.

   - Decision Support & Expert Systems. Turban, Efraim  
     
Optional Texts 
     
   - KNOWLEDGE-BASED SYSTEMS: An Introduction to Expert Systems.
     Mockler.  MacMillian. ISBN:  0-02-381897-2
   
   - Developing Business Expert Systems with LEVEL 5.  Barker.
     ( Work Book )
     
  - LEARNING A Survey of Psychological Interpretations.
    Winfred F. Hill, 5th ed. C1985 Harper and Row. ISBN: 0-06-042828-X

  - Artificial Intelligence: A Knowledge Based Approach,
    Firebaugh.  Boyd & Fraser.   ISBN:0-87835-325-9.

Course Objectives:
     
Upon completion of this course, you will be able to:
     
     1. present a conceptual overview of expert systems to managers,
        end-users, and colleagues.  
     
     2. design, build, and implement a prototype expert system using
        1stClass Expert System Shell (ESS) or VP-Expert (ESS). 
     
     3. describe the major elements and phases of the Knowledge Base
        System Development Life Cycle.  
     
     4. discuss differences between two AI development software
        shells at the procedural level. 
     
     5. discuss the difference between general purpose AI languages
        such as LISP and PROLOG, Expert System Shells, and other
        development tools at the conceptual level.  
     
     6. describe the difference between rule-based production 
        systems, neural networks, frame-based object-oriented
        development platforms, and languages that use predicate 
        calculus. 
     
     7. discuss the role and function of a Knowledge Engineer and
        the knowledge acquisition process. 
     
     
     8. name the key historical figures and discuss their contribution
        to the field of AI. 
     
     9. discuss several successful AI implementations and the factors
        that lead to their success. 
     
     10.identify situations and applications where expert systems
        are appropriate.
  

Class Policy:
     
       Class participation and class attendance are a positive factor
     considered when determining both the midterm and final grades.  
     Students entering late will excuse themselves for the interruption to
     their classmates.  

Course Requirements for Part - I: 
               
       Students  are  responsible  for  all materials appearing in the 
     textbook; class handouts and supplemental reading assignments; class 
     lecture notes; lab. projects, lab. assignments, and practice projects. 
       Each student missing a class is responsible for obtaining any and 
     all information pertaining to the missed class session(s) from  
     students who attended the class(es).  The instructor should be
     notified about absences, ahead of time, via voice or E-Mail.       
Laboratory Policy: 
     
       Students are encouraged to help each other, but all projects in all
     parts must be the original work of the individual or team passing such
     work for partial course credit.  Your instructor has the right to demand
     proof at any time of the genuineness and originality of the work.  This
     process would most likely be demonstrated by asking a student to
     reproduce a piece of the work from scratch at a terminal in a live
     performance.  Class and Lab Attendance:
     
     - Attendance is mandatory and will be taken at the end of every
       class and lab.  Authorized absences will be accepted only with prior
       approved notice. 
     - Athletes must give written notice of absences  prior to conflicting
       events from the head of the Athletic Department.
     - Each student  missing a class or lab is responsible for obtaining 
       any and all information pertaining to the missed class lab session(s).
     

 
Evaluation: Examination(s)
     
Points:   Exam:                        
                              
  15      Concepts Examination.  Turban.  Week 9. 
     
  15      1stClass  Programming Project, reproduce in class assignment 
          using student version of 1stClass.
     
  15      VP-Expert Programming Project, reproduce the 1stClass project 
          using VP-Expert System Shell. 
     
   5      PROLOG Sample program: debug, test, and execute 
     
  10      Module of Expertise  MOE.  Select a topic and software platform
          of interest.  Research, develop, and present to the class an
          application that is relevant.
                     
   6      Class participation - Class participation can be a positive factor. 
  

General Course Requirements: 
    
           - Textbook readings and class handouts and supplements.
           - Class and Laboratory lecture notes.
           - Lab projects 
           - Purchase and format three 3«" diskettes. 
           - Research and present one current events project.     
 
Program Projects: 
     
    Programs must be structured and well documented. 
     
    A magnetic copy must be presented along with the hardcopy
    listing of the program to receive credit for each project.  
     
    Program listings must contain the following:
       a. Student name.
       b. Course number and section id..
       d. Date of submission.
       e. Assignment number. 
     
      Program projects will not be returned  unless they are
    unsatisfactory.  Students may make an appointment with the
    instructor to the review their code.   Students wishing an 
    acknowledgement of acceptance may attach a cover page to the
    listing.  This page will be returned with comments and
    recommendations.
     
      Plagiarism will not be tolerated.  Copies of other student's
    work will be marked "F" and the occurrence will be immediately
    reported to the department chair and the academic dean in writing. 
              

   
   
            Module of Expertise ( M.O.E. )
                            
  This is your opportunity to show what you have learned about 
knowledge-based system development and especially your topic.  Your
final product may be written, oral, or demonstrative.  This outline
lists the criteria that will be used to evaluate your MOE. 
     
     
1.  Relevance:  You bear the responsibility to demonstrate the 
relevance of your topic to the field, your profession, academia, or 
another recognized group.  You should think about originality, scope,
and appropriateness of the level of  complexity.  
     
2.  Choice of tools used:  You should justify the appropriateness of
the tools (software) used in conjunction with the subject domain,  
expertise levels available, and end- user interface.  Be able to answer 
the question, Why is this tool the best choice for this application?  
Given the nature of discovery learning, it is also satisfactory to
demonstrate how the tool(s) or techniques were not appropriate.  
Explain what went wrong, what you would do differently next time, and
how would you perform a better preliminary investigation.  
     
3.  Knowledge Acquisition:  Show how you acquired the knowledge used 
in your MOE.  If you are doing a PURE research  based project then
describe how you would perform Knowledge Acquisition.  
     
4.  User interface:  Demonstrate the depth of your knowledge using the 
tool or technique that you chose for your MOE.  This is your chance to 
add color, graphics, hypertext, sound, and other available options that 
promote end-user acceptance and ease of  use ( user friendly ).    
     
5.  Field Test:  Did you test your project or theory on a small sample 
population?  What were the results?  There is no need to incorporate 
these suggestions into your prototype ( time restriction ) but you 
should document and share the evaluation criteria and results in your 
presentation or report.  
     
6.  General:  Your paper or presentation should reflect a high level 
of quality appropriate for a major work done at the graduate level.  
All work should be professionally packaged and presented.