Module 11353 (2003)
Syllabus page 2003/2004
06-11353
AI Techniques B
Level 1/C
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus
The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)
Changes and updates
Detailed Syllabus and Relevant Links updated.
Relevant Links
EXTRA INFORMATION and MATERIAL.
Outline
This module will extend the concepts and techniques covered in the linked module and is in particular concerned with knowledge representation and search applied to problem solving. Algorithms for solving certain classes of problems are presented. Particular search algorithms are introduced and analysed that are well-suited for these problem solving tasks. Certain data structures are ubiquitous in AI (e.g. trees) and these will be studied in various contexts. The topic of automated deduction in logic will also be introduced.
Aims
The aims of this module are to:
- develop an understanding of important AI techniques and their applications
- study search algorithms, trees (as generic data structures) and logic in AI
Learning Outcomes
| On successful completion of this module, the student should be able to: | Assessed by: | |
| 1 | apply a variety of informed search, planning, reasoning algorithms to example problems | Continuous assessment, examination |
| 2 | discuss the properties of these algorithms. | Continuous assessment, examination |
| 3 | show awareness of, and be able to discuss, basic problems in natural language processing and reasoning | Continuous assessment, examination |
| 4 | be able to translate sentences between natural language and first order predicate calculus. | Continuous assessment, examination |
| 5 | be able to perform resolution in first order predicate calculus | Continuous assessment, examination |
| 6 | describe the uses and limitations of logic in AI | Continuous assessment, examination |
Restrictions, Prerequisites and Corequisites
Restrictions:
None
Prerequisites:
None
Co-requisites:
06-11352 (AI Techniques A) (linked module), 06-11351 (AI Programming B), (06-08764 (Mathematics & Logic B) OR 06-02316 (Logic))
Teaching
Teaching Methods:
3 hrs/week lectures/exercises
Contact Hours:
Assessment
- Supplementary (where allowed): As the sessional assessment
- 3 hr examination (80%), continuous assessment (20%), divided equally between this module and 06-11352 (AI Techniques A). Resit by examination only.
Recommended Books
| Title | Author(s) | Publisher, Date |
| Artificial Intelligence, A Modern Approach (2nd edn) | Russell S & Norvig P | 2003 |
| Artificial Intelligence (2nd edn) | Rich E & Knight K | 1991 |
| Artificial Intelligence, A New Synthesis | Nilsson N | 1998 |
| Artificial Intelligence (3rd edn) | Winston P H | 1992 |
Detailed Syllabus
- Informed Search: incl. hill climbing, best first, A*, AND/OR search, game playing.
- Search and Constraint Satisfaction.
- Expressing Things in Internal Representations (Logic mainly), incl. relationship of logic to natural language.
- Weaknesses of (first-order) logic as representation tool.
- Reasoning in First Order Predicate Logic, including Resolution theorem-proving.
- Planning: an introduction to issues and techniques.
- Handling Uncertainty: Default Logic.
- Handling Uncertainty: Truth Maintenance Systems.
- Handling Uncertainty: Case-Based Reasoning.
Last updated: 12 January 2004
Source file: /internal/modules/COMSCI/2003/xml/11353.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus