Course plan
ARTIFICIAL INTELLIGENCE FUNDAMENTALS: DETAILED PLAN
Artificial Intelligence and agents (1)
1. 19/9 An introduction to the course
I - Constraint satisfaction (4)
2. 26/9 Problem formulation
3. 28/9 Problem reduction, consistency checking techniques
4. 3/10 Heuristic and efficient search, local repair methods; problem structure.
5. 5/10 I: exercise review and student’s presentations
II - Knowledge representation and reasoning (7)
6.
10/10 Characterization of Knowledge Based systems. Knowledge engineering.
7.
12/10 Reasoning about change; situation
calculus and the frame problem. Temporal reasoning.
8. 17/10 Non-monotonic reasoning.
9.
19/10 Reasoning about knowledge
and belief
10. 24/10 Semantic networks, object representations and frames
11. 26/10 Reasoning about ontologies and description logics (the basics)
12. 31/10 II: exercise review and student’s presentations of seminal papers
==== ONE WEEK BREAK ======================================
III - Reasoning under uncertainty (4)
13. 7/11 Representing uncertain knowledge and probabilistic reasoning.
14. 9/11 Belief networks and inference
15. 14/11 Reasoning over time
16. 16/11 III: exercise review and student’s presentations of seminal papers
IV - Planning (4)
17. 21/11 The planning problem, representation for actions. Planning as state-space search, regressive planning
18. 23/11 Partial order planning, planning graphs
19. 28/11 Planning in the real world: dealing with temporal and resource constraints, hierarchical planning, planning in non-deterministic domains, multi-agent planning.
20. 30/11 IV: exercise review and student’s presentations of seminal papers
V - Rule based systems (4)
21. 5/12 Logic programming and rule based production systems.
22. 7/12 Uncertainty in rule based systems. Efficient implementation.
23. 12/12 Constraint logic programming / abductive logic programming
24. 14/12 V: exercise review and student’s presentations of seminal papers
19 lectures + 5 exercise reviews = 48 hours