Module 11353 (2002)
Syllabus page 2002/2003
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.)
Relevant Links
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 New Synthesis | Nilsson N | 1998 |
| Artificial Intelligence, A Modern Approach | Russell S & Norvig P | 1995 |
| Artificial Intelligence (3rd edn) | Winston P H | 1992 |
| Artificial Intelligence (2nd edn) | Rich E & Knight K | 1991 |
Detailed Syllabus
- Informed Search: Uniform cost, hill climbing, best first, A*.
- Informed Search: admissibility, monotonicity, completeness, informedness, time and space complexity.
- Planning: goal regression, AND/OR trees, recursive evaluation of AND/OR trees, the frame problem, Sussman anomaly and non-linear planning.
- Social agents: game playing, minimax and heuristic pruning.
- Social agents: natural language processing, syntax, semantics and pragmatics; parsing.
- Reasoning: overview of problems in reasoning.
- Reasoning: First Order Predicate Calculus: quantification and connectives; translation to and from natural language.
- Reasoning: resolution in propositional and predicate calculus; proof strategy; disjunctive normal form, unification.
- Reasoning: Weaknesses of FOPC.
Last updated: 29 July 2001
Source file: /internal/modules/COMSCI/2002/xml/11353.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus