1. Introduction to AI & AI Principles (Semester 1) WEEK 11 (Wed) – Wrap-Up (2008/09) John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham, UK

2. Today uSome miscellaneous points uNature of exam uReview of material uQuestions, if time uEvaluation forms

3. Miscellaneous Points uThe IF-HAVE … AND-HAVE … THEN-HAVE notation in PS rule is my own invention: IF…AND…THEN is more standard, but many other notations have been used, e.g.  l is-person(x)  drunk(x)  sad(x)  l sad(x)  is-person(x)  drunk(x) ….. l Those are perhaps too logic-like …..  uI switched to IF-HAVE…THEN-HAVE in PS rules, instead of IF…THEN or , to emphasize the procedural quality and the reference to what’s in the WM as opposed to what’s true in the world.  uI switched to AND(-HAVE) in PS rules, instead of , for similar reasons, and because individual rule conditions nay themselves contain conjunction.

4. Miscellaneous Points, contd uThe sort of predicate logic we have seen has severe expressive limitations, e.g.: uFormulas (logic expressions that make statements) cannot be arguments to predicate symbols or function symbols, so you can’t write things like the following (with the offending subformulas underlined), if arrest, DNA-logged, want and hurt are predicate symbols: l caused(arrest(Police, Perp), DNA-logged(Perp) ) l believes(Vic,  want(Perp, hurt (Perp, Vic)) ) uOne solution proposed is to let events/states themselves be objects in the world (as in one SN technique):     a, d (arrest-event(a, Police, P)  DNA-log-event(d, P)  caused(a, d)).

5. Misc points contd: Why Logic Has Been Proposed in AI uDesire to capture human rationality. uDesire for general-purpose representation/reasoning approach. l General purpose in terms of both subject matter and role in cognition (info from vision, sentence meanings, internal memory, …)  Desire for common format for explaining what is going on in other representation/reasoning approaches.

6. “General-Purpose” Aim  Reaction to: completely ad hoc, special-purpose representations, and representation styles, created for specific tasks, specific types of task or specific types of information. Consequence of such special-purpose representations: Duplication of representational design effort when approaching a new problem. Difficulty of learning transferrable lessons about representational design. Need for creating tailored reasoning methods to cope with the specialized representations.  A single AI system may need to deal with a wide variety of tasks and types of information, perhaps all mixed up together. Having disparate representation styles for different types of information causes problems.........

7. “General-Purpose” Aim: Caveats  But this doesn't mean that AI systems should not use specialized approaches at all, or that you can't have mixes of styles. Could well be a good idea. NB: human use of different representational styles for different things: natural language, specialized (e.g., technical) forms of natural language, mathematical notation, diagrams, pictures, musical notation,..., and we're quite used to mixing these with each other … even mixing different natural languages.  Logic has quite severe limitations as regards both representation and reasoning, and is more suited to some things than others. So, its being “general purpose” is merely an aspiration.  Special purpose representations can be better (more effective or efficient) for the reasoning they are designed to support.

8. “Common Format” Aim  The variety of proposed special-purpose representations, and the complications in some of them,  make it convenient to have a relatively simple, relatively standard language into which to (theoretically) translate them, in order to see how well conceived they are compare their advantages and disadvantages find, possibly, a quicker route to developing meaning principles, reasoning schemes and mathematical results about them.  Having a standard representation/reasoning style eases communication between researchers.

9. Nature of the Examination

10. Format uPlease see the refined description in the slides for Revision Week 1

11. Material, 1 uMy own lecture material, with some exclusions. uAndrea Arcuri’s lecture on learning, with some exclusions. uBullinaria slides, again with some exclusions: l Semantic Networks (and my own notes on these slides) l Production Systems (and my own notes on these slides) l Expert Systems uChapters (or chapter parts) in the Weekly Reading Assignments on module webpage. uAnswers / additional notes for Exercises.

12. Material, 2 uDon't be spooked by previous examinations, especially those from before 06-07!! There have been a lot of changes. uKnowledge of textbook chapters or chapter parts other than those I've listed ISN’T expected. uKnowledge of Bullinaria slides other than those I point to from my list of weekly lecture slides ISN’T expected. uKnowledge of fine technical details in book chapters ISN’T expected. (I’m only expecting the main concepts and overall grasp of main examples.) uBut of course knowledge of all the above types could be helpful and impressive.

13. REVIEW of the material (see extended treatment in Revision Week 1 slides)