Introduction to AI & AI Principles (Semester 1) REVISION WEEK 1 (2008/09)

1. Introduction to AI &AI Principles (Semester 1)REVISION WEEK 1(2008/09) John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham, UK

2. TODAY (Tuesday) • Nature of exam (refining the info given in Week 11) • Review of material (extending the review in Week 11) • Questions • NB Office Hours: • Friday 1 May, 3:00-4:00 • Friday 8 May, 4:00-5:00

3. Natureof the Examination

4. Format of AI Principles Exam • Three hours long. • First half (1.5 hours): almost exactly the same as the Intro-AI exam (see next slide). • Second half: to be explained by Dean Petters in Revision Week 2. • Use of material: • First half can be done on the basis entirely of my material. • Second half can be done on the basis entirely of Dean’s material. • But you’re free to use his material in my half or mine in his half as appropriate.

5. Format of AI Intro Exam • One and a half hours long. • Do 5 out of 6 questions. • Most question parts: broadly similar in style to exercises you did during Semester 1. • One question is essay-like and allows considerable latitude as to what aspects of AI you address and what material you bring to bear. • The rest are mostly on specific technical things, with a couple of free-wheeling aspects here and there.

6. AI-Intro Material • My own lecture material, • with some exclusions (see Week 11 part of Slides page) • Answers / additional notes for Exercises. • Andrea Arcuri’s lecture on learning, with some exclusions. • Bullinaria slides (again with some exclusions): • Semantic Networks (and my own notes on these slides) • Production Systems (and my own notes on these slides) • Expert Systems • Textbook chapters (or chapter parts) in the Weekly Reading Assignments on module webpage.

7. AI-Intro Material, contd • Don't be spooked by previous examinations, especially those from before 06-07!! • There have been a lot of changes. Also, quite a few since last year. • Knowledge of textbook chapters or chapter parts other than those I've listed ISN’Texpected. • Knowledge of Bullinaria slides other than those I point to from my list of weekly lecture slides ISN’T expected. • Knowledge of fine technical details in book chapters ISN’Texpected. (I’m only expecting the main concepts and overall grasp of main examples.) • But of course knowledge of all the above types could be helpful and impressive.

8. REVIEWof the material

9. Main Topics Covered • Representation and reasoning, in • logic • production systems • semantic networks. • What we need to represent: entities (incl. situations, feelings, …), properties, relationships, propositional structure, quantification, … • Planning (a type of reasoning). • Search. • Natural Language difficulties as illustration of why AI is difficult. • Knowledge and reasoning needed in natural language understanding and operating in practical scenarios such as Hot Drinks and Shopping Trip. • Learning.

10. Main Detailed Techniques • Expressing information in logic. • Expressing information in semantic networks. • Applying production system rules (forwards or backwards, but fine detail only expected in forwards case). • Doing simple logical proofs. • Search (fine detail not expected for best-first and A*). • Search as applied to route-finding. • Search as applied to planning delivery of a drink.

11. General Themes in AI • Why everyday AI is difficult. • Language processing, vision, planning, common-sense reasoning, etc. • “Intelligence” and its connection to “stupidity”. • What looks like stupidity is often the understandably-incorrect application of efficient heuristics (rules of thumb) without which we and our AI cousins would be in a mess. • Contd. ……

12. General Themes in AI, contd. • Uncertain, vague, conflicting, missing, or diverse info. • Huge amounts of info, of varying relevance. • Hence: search, satisficing, graceful degradation, heuristics. • Context-sensitivity; incl. relativity to agents’ purposes (e.g., in vision and language interpretation). • Task variability, learning, adaptation, repair (e.g., of plans). • Declarative/procedural trade-off. • Goal-directedness (backwards chaining) in reasoning and search.

13. A General Theme in AI • Uncertain, vague, conflicting, missing, diverse, extensive info: • Amply shown by Hot Drinks, Shopping Trip and Crime scenarios, • and by natural language examples. • Use of default rules and conflict resolution in PSs • Use of defaults and exceptions in SNs. • Contributes to need for search. Non-optimality (satisficing) in (some) search. • Use of heuristics in search. • Need for learning. • Graceful degradation in (e.g.) neural networks.

14. A General Theme in AI • Search: • In planning (incl. route-finding, game-playing, …) • In deduction • In operation of Production Systems • In reasoning in Semantic Networks • In learning, particularly • genetic algorithms • automatically finding good weights for a neural network

15. General Theme: Heuristics • PS rules that leave out details and complications, and that are at best DEFAULTS • Conflict resolution methods in PSs. • The information attached to actions in planning about what changes (or doesn’t change) is typically defeasible. • On what doesn’t change: see the Planning 1 chapter in Callan about the important frame problem. • In search in general: • Pruning • Action ordering in depth-first search • Evaluation functions in best-first search, incl. • Heuristic functions in A* search. • Choice of search strategy, incl. backwards vs. forwards.

16. Rough Sequence of Topics • Introduction: • what AI is • why we do it • how it differs from ordinary CS • application areas • expert versus everyday AI.

17. Topic Sequence contd: Challenge of AI • Introductory examples from language. • CAUTION CHILDREN • “John got to his front door but realized he didn’t have his key.” • Context-sensitivity of language; knowledge and reasoning needed. • Knowledge and reasoning needed in Hot Drinks, Shopping Trip and Crime scenarios. • Knowledge variety, uncertainty, vagueness, missing info, … • Vision and movement. • Context-sensitivity, purpose-sensitivity, ambiguity, …

18. Sequence of Topics, contd. • Detailed planning of delivery of one drink. • Search, forwards versus backwards chaining, goal-directedness • Knowledge needed about preconditions and (non-)effects of actions • Search: general nature, example applications. • Introduction to logic representation. • Reasoning about a static situation using Production Systems. • CAUTION: different from planning, = reasoning about moving between different possible situations.

19. Sequence of Topics, contd. • More on natural language difficulties. • vagueness • quantification subtleties • context-sensitivity • syntactic ambiguity, incl. PP attachment • some advanced topics: speech acts, mental states, metonymy, metaphor

20. Sequence of Topics, contd. • Search detail (in route finding for Shopping Trip) • Different search strategies: depth-first, breadth-first, best-first, A* • Optimality or otherwise • Ordering and pruning heuristics • Evaluation/heuristic functions.

21. Sequence of Topics, contd. • More on logic representation. • Logical deduction. • Inference rules in deduction versus production systems • Soundness • Fiddling around needed in deduction • Reduction of fiddling around by using Resolution • Reasoning by contradiction • Declarative/procedural trade-off. • Logical deduction versus using production systems. • Reasoning as search.

22. Sequence of Topics, contd. • Representation and reasoning in Semantic Networks • Localization of info at nodes • Different types of link • Taxonomy (instances and subtypes) • Defaults and exceptions • Intersection search • More on Production Systems. • Rule instantiations • Conflict resolution • Expert Systems

23. Sequence of Topics, contd. • Learning (Andrea Arcuri lecture in Week 9). • General characteristics • Neural Networks • Evolutionary Computation and Genetic Programming • Naïve Bayes Classifiers (not expected for exam)

24. Questions