Semester two

Course Description 

This is a specialized Course Unit in information systems and information technology (IS/IT) for undergraduate MIS majors. The Course Unit is targeted to senior MIS students who want more expertise in developing, managing and using Decision Support Systems. It examines the design, development and implementation of information technology based systems that support managerial and professional work, including Communications-Driven and Group Decision Support Systems (GDSS), Data-Driven DSS, Model-Driven DSS, Document-Driven DSS, and Knowledge-Driven DSS.

 

Course Objectives 

1. To introduce to students the structures, modelling, and the application of various decision support systems in today's corporate environment.
2. To provide knowledge and understanding of decision support systems. Problems formulation. Linear programming and parametric programming, and their applications including transport and assignment problems. Inventory problems. Replacement, maintenance and reliability problems. Dynamic programming. The principles of optimality. Decision trees. Critical  path planning

3.    To provide knowledge and practical skills for computer support for organizational decision making, and various planning, analysis and control tasks.

4. To enable students examine user interface design issues and evaluate the user interfaces and capabilities of Decision Support Systems.

 

Course Learning Outcomes

By the end of the lecture the student will be able to:

1.    Identify structures, modelling, and the application of various decision support systems in today's corporate environment.

2.    Identify the characteristics of decision support systems. Problems formulation. Linear programming and parametric programming, and their applications including transport and assignment problems. Inventory problems. Replacement, maintenance and reliability problems. Dynamic programming. The principles of optimality. Decision trees. Critical  path planning

3.    Examine examples and case studies documenting computer support for organizational decision making, and various planning, analysis and control tasks.

4.    Examine user interface design issues and evaluate the user interfaces and capabilities of Decision Support Systems.

 

Indicative Content

1.    Managerial decision making and information systems: (12 Hours)

i)     Nature of managerial work and productivity;

ii)    defining management information needs;

iii)   requirement for decision support for decision making and other executive work;

iv)   types and levels of support systems.

2.    Decision Support Systems (DSS): (12 Hours)

i)      DSS characteristics and capabilities;

ii)     DSS taxonomies and frameworks;

iii)    decision modeling using multi-criteria decision making;

iv)    model management for decision support;

v)     DSS development tools;

vi)    DSS development approaches;

vii)   DSS analysis and design methods;

viii)  DSS documentation tools;

ix)    case studies of DSS development.

3.    Executive Support Systems (ESS). (6 Hours)

i)     Executive information systems (EIS) and executive support;

ii)    executives' information needs; ESS/EIS tools;

iii)   use of ESS for office support applications;

iv)   use of ESS in planning and control;

v)    system development and EIS implementation.

4.    Group Decision and Negotiation Support Systems (GDSS, NSS): (6 Hours)

i)      Goal of GDSS and its levels;

ii)     technology of GDSS, computer support for collaborative problem solving in meetings;

iii)    electronic meeting systems to support group work;

iv)    GDSS software; idea generation;

v)     negotiation support systems;

vi)    mediators and facilitators;

vii)   construction of a GDSS and determinants of its success;

viii)  support system for cooperative work.

5.    Expert Systems (ES): (6 Hours)

i)     Introduction to expert systems;

ii)    introduction to logic, knowledge engineering and knowledge acquisition;

iii)   knowledge representation and inference;

iv)   knowledge encoding;

v)    issues and methods in verification and validation of expert systems;

vi)   expert systems applications in management and finance.

6.    Intelligent Decision Support Systems: (6 Hours)

i)     Knowledge-based decision support systems;

ii)    intelligent DSS systems design with integration of expert system, hypermedia, and database technology;

iii)   case-based decision aiding systems, active decision support system and symbiotic systems;

iv)   neural networks, integration of expert systems and neural networks;

v)    integrated intelligent systems.

7.    Implementation of Support Systems: (6 Hours)

i)     Implementation successes versus failures;

ii)    technical versus human factors in implementation;

iii)   cost justification methods : value analysis, usability analysis;

iv)   implementation and sponsorship roles;

v)    linking systems to organizational objectives;

vi)   managing organizational resistance;

vii) managerial participation versus involvement;

viii)        organizational and societal impacts of computer-based support systems.

8.    Data mining and knowledge discovery systems: (6 Hours)

i)     Tools based on statistical and other techniques that are used to extract information and possible creation of knowledge from large and very large databases.

ii)    Quantitative models, like statistical or management science models, are often the core of decision support systems.

9.    Decision making requires knowledge and expertise. (6 Hours)

Cognitive mapping and soft system approaches are used to build models of human knowledge and problem representation.

 

Study Materials

Relevant text books, power point slides, a projector, lap top and teaching aids necessary.

 

Mode of Delivery

Lectures, Tutorials, Lab sessions, Case analysis

 

Mode of Assessment:

Test                                                     15%

Assignment                                       15%

End of semester examination         70%

Total                                                  100%

 

 

Reading List

1. E. Turban and J. Aronson, Decision Support Systems and Intelligent Systems, 5th edition, Prentice Hall, 1998.

Power, D. J. Decision Support Systems: Concepts and Resources for Managers.

Course Description 

This Course Unit is about the design and implementation of intelligent agent’s software or hardware entities that perform useful tasks with some degree of autonomy. This Course Unit provides an introduction to the basic principles and applications of Artificial Intelligence. Programming assignments are used to help clarify basic concepts. The emphasis of the Course Unit is on teaching the fundamentals, and not on providing a mastery of specific commercially available software tools or programming environments

 

This course enables students to carry out design and implementation of intelligent agent’s software or hardware entities that perform useful tasks with some degree of autonomy and Upon successful completion of the Course Unit, students will have an understanding of the basic areas of artificial intelligence including problem solving, knowledge representation, reasoning, decision making, planning, perception and action, and learning -- and their applications (e.g., data mining, information retrieval). It ensures the design and implementation of key components of intelligent agents of moderate complexity in Java and/or Lisp or Prolog and evaluates their performance and Graduate students will have developed familiarity with current research problems, research methods, and the research literature in AI.

 

Course Objectives

1.    To provide students with knowledge and understanding of the basic areas of artificial intelligence including problem solving, knowledge representation, reasoning, decision making, planning, perception and action, and learning -- and their applications.

2.    To explain to students the strengths and weaknesses of several methods for representing knowledge

3.    To provide with students the knowledge and understanding of several AI algorithms in area such as heuristic searching, game search

4.    To enable students design and implement intelligent agent’s software or hardware entities that perform useful tasks with some degree of autonomy.

5.    To avail students with the knowledge and practical skills development of various key components of intelligent agents of moderate complexity in Java and/or Lisp or Prolog and evaluate their performance and Graduate students will have developed familiarity with current research problems, research methods, and the research literature in AI.

 

Course Learning Outcomes

By the end of this course, the student will be able to:

1.    Demonstrate understanding of the basic areas of artificial intelligence including problem solving, knowledge representation, reasoning, decision making, planning, perception and action, and learning -- and their applications.

2.    Assess the strength and weakness of several methods for representing knowledge

3.    Assess the strength and weakness of several AI algorithms in area such as heuristic searching, game search

4.    Design and implementation of intelligent agent’s software or hardware entities that perform useful tasks with some degree of autonomy.

5.    Design and implement key components of intelligent agents of moderate complexity in Java and/or Lisp or Prolog and evaluate their performance and Graduate students will have developed familiarity with current research problems, research methods, and the research literature in AI.

 

Indicative Content

1.    Introduction to Artificial Intelligence: (4 Hours)

Simulation of Intelligence behavior

2.    Computational models of intelligent behaviour, (8 Hours)

i)     problem solving,

ii)    knowledge representation,

iii)   reasoning, planning, decision making, learning, perception, action, communication and interaction.

3.    Problem solving: (8 Hours)

i)     games, natural language question answering,

ii)    visual perception, learning;

iii)   Aim-oriented algorithms versus solution-guaranteed algorithms.

4.    Understanding Natural Languages: (8 Hours)

i)     Parsing techniques,

ii)    context-free and transformational grammars,

iii)   transition nets, augmented transition nets,

iv)   grammar-free analyzers,

v)    sentence generation.

5.    Knowledge Representation: (8 Hours)

i)     First-order predicate calculus;

ii)    PROLOG and LISP languages;

iii)   Semantic nets; partitioned nets;

iv)   Production rules;

v)    knowledge base,

vi)   the inference system,

vii) forward and backward deduction.

6.    Expert System: (8 Hours)

i)     Existing systems,

ii)    Domain exploration;

iii)   Meta-knowledge, expertise transfer, self-explaining systems.

7.    Pattern Recognition Structured Descriptions: (8 Hours)

i)     Symbolic description,

ii)    machine perception,

iii)   line finding, interpretation, semantics and models, object identification and speech recognition.

 

Study Materials

Relevant text books, power point slides, a projector, lap top and teaching aids necessary.

 

Mode of Delivery

Tutorials, Lectures, Seminars.

 

Mode of Assessment:

Test                                                     15%

Assignment                                       15%

End of semester examination         70%

Total                                                  100%

 

Reading List:

1.    Stuart Russell and Peter Norvig, (2009). Artificial Intelligence: A modern approach 3rd Edition

2.    Yuan Wang and David J. DeWitt. Computing Page Rank in a Distributed Internet Search Engine System. In Proceedings of the 13th International Conference on Very Large Data Bases, 2004.( Presentation)

3. Middleton, S. E., Shad bolt, N. R. and De RoureUser Profiling in Recommender Systems ACM Transactions on Information Systems (TOIS) 22(1) pp. 54-88.,2004.( Presentation)
4. Sieg A., MobasherB., Burke R.,Prabu G., Lytinen S. Representing User Information Context with Ontologies In proceedings of HCI international conference 2005.