Introduction to AI & AI Principles (Semester 1) REVISION LECTURES (Term 3) John Barnden Professor of Artificial Intelligence School of Computer Science University of Birmingham, UK

TODAY: Nature of Exam; Review of the term TOMORROW: Question/answer session.

Nature of the Examination

Format uOne and a half hours (for Intro to AI). (Half of AI Principles exam.) uFour questions, one containing choice. uSuggestion: minutes for initial read-through and thinking, then up to about 15 minutes for answering each question, leaving about 15 minutes for final checking/refining. uSome questions have several parts. uSome questions broadly be similar in style to some questions in formative Exercise Set Exercise, though simpler/briefer. uOne or two brief essay style questions/parts requiring you to recall concepts, issues, examples, etc. from module material. uSome questions/parts quite technical, others not.

Material, 1 uMy own lecture material. uBullinaria slides pointed to from my list of weekly slides. NB: This now includes his slides on Neural Networks (ask me if you need any help understanding them). uMaterial from all Guest Lectures. uChapters in the Weekly Reading Assignments on module webpage. uAnswers to the formative Exercise Set in Term 1.

Material, 2 uDon't be spooked by previous examinations!! My coverage of material is new. uKnowledge of textbook chapters other than those I've listed isn't expected. Knowledge 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 guest lectures and book chapters won't be expected. (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.

REVIEW of the material (refinement of part of a Week 11 lecture)

Review, 1 uNature of AI: aims, applications, branches, issues. Difference from CS in general. u“Intelligence” and its connection to “stupidity”. uExpert AI versus Everyday (“Common-Sense”) AI. uWhy everyday AI is difficult. l Language processing, vision, planning, common-sense reasoning, etc.

Review, 2 uWhy planning, common-sense reasoning, language processing, etc. may need representation. uWhy natural language is problematic for this … while also having many strengths. uWhat we need to represent: entities (incl. situations, feelings, …), properties, relationships, groups, propositional structure, generalization/quantification, … uTypes of reasoning we need to do.

Review, 3 u Taster of logic. l Captures entities, properties, relations, extreme forms of quantification, basic forms of propositional structure. Can also handle groups of entities. l Aims of logic: clarity and simplicity compared to NL; systematic, sound reasoning; general applicability; common format for comparison. uIntro to semantic networks (and frames). uProduction systems.

Review: Guest Lectures uChess, Computer games (NB: similarities, differences) uLearning, Neural networks uEvolutionary computing uVision uRobotics, Agents uPhilosophy

Review: General Themes in AI uUncertainty, vagueness, conflict, missing info, diversity of info. uHence: satisficing, graceful degradation, heuristic processing (i.e., using rules of thumb). uContext-sensitivity; relativity to agents’ purposes. uTask variability, learning, adaptation, repair (e.g., of plans). uRepresentation. uReasoning. uSearch.