Learning Partnerships, Unit Catalogue 2009/10 |
AS20321: Knowledge systems |
 Credits: |
5 |
| Level: |
Intermediate |
| Period: | This unit is available in... |
| Semester 1 at City of Bath College | |
| Semester 1 at Wiltshire College |
| Assessment: |
CW80EX20 |
| Supplementary Assessment: | Like-for-like reassessment (where allowed by programme regulations) |
| Requisites: |
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| Description: | Aims: To enable the student to: * investigate current topics in Artificial Intelligence research; * develop programs in an artificial intelligence language, such as Prolog or LISP; * select and use expert system and artificial intelligence tools to meet commercial needs. Learning Outcomes: At the completion of the unit learners should be able to: * Describe and analyse some current topics in artificial intelligence research. * Discuss the purpose, strengths and weaknesses of an artificial intelligence language, and show how it can be employed by developing an application. * Develop an application using a knowledge-based system shell, using a structured design methodology. Evaluate the strengths and weaknesses of the methodology and application. Skills: Practical skills - program design skills, coding skills - taught and assessed. Personal skills - time management, personal organisation, problem solving - facilitated and assessed. Communication skills - demonstrations, working in a team - facilitated and assessed. Content: Artificial intelligence: definition, current techniques and applications, e.g. vision, learning, natural language processing, robotics, games, searching and search spaces, knowledge representation, intelligent agents. Programming in an artificial intelligence language: development of appropriate skills for a current AI language (e.g. Prolog or LISP): syntax, semantics, program design, application development, debugging, testing and verification, documentation. Expert Systems: definition, purpose and scope, choice of suitable domains, techniques for knowledge capture, knowledge representation (rules, frames), inference methods (forwards and backwards chaining, case-based reasoning), design methodology, expert system shells, verification and validation. These will be presented in the context of an appropriate expert systems framework. |