CSCE 5043 – Artificial Intelligence –Exam I Review

Intro, History, Foundations – class notes, Chapters 1 & 2

  • What is artificial intelligence? Scope: Weak and strong methods, agents, computational X where X is {physics, …., archaeology}
  • Prehistory: Aristotle, Boole, Babbage, Turing, Turing test, HAL, …
  • Foundations: philosophy, linguistics, psychology, biology, programming languages, video games, virtual worlds, …
  • Languages: Lisp, Prolog, Protégé, …

Pervasive Computing, Agents – class notes, Chapter 19, links on syllabus

  • Everything is Alive vision = pervasive computing, managing 1000s of network objects (smart devices)
  • X agent where X = intelligent, mobile, information, reactive, autonomous, collaborative, competitive
  • Goal-based agents that use search or planning
  • Multiagent system
  • Belief, desire, intention architecture

RFID Agent Middleware – class notes, links on syllabus

  • Abstract classes like agent, message
  • TagCentric classes like reader, printer, DBMS, GUI

Second Life

  • Editing your avatar, navigating, flying, teleporting;building, scripting, communicating

AI Languages – Lisp – class notes, links on syllabus

  • McCarthy, parenthesis, data, functions, atoms, lists, eval
  • Read/Eval/Print loop, debugging, trace
  • Garbage collection and how it works

AI Languages – Prolog - – class notes, links on syllabus

  • Predicate, arity
  • Clause – Facts, Rules
  • Queries – simple and compound
  • Control structure - depth first, backtracking, cut

Natural Language – class notes, Chapter 20, links on syllabus re MBNLI

  • Information retrieval, dictation, generation, question answering
  • Natural language interfaces, habitability, menu-based natural language interfaces (MBNLI)
  • Natural language vs. formal language
  • Phonology, morphology, syntax, semantics, pragmatics
  • Grammars, BNF, terminal, non-terminal, start symbol, rewrite rules
  • Verb, noun, adjective, adverb, conjunction, pronoun, article, noun phrase, verb phrase
  • Chomsky hierarchy of languages: regular, context-free, context sensitive, recursively enumerable
  • Parsing, derivation tree, bottom up chart parsing
  • Transition networks
  • Lexical ambiguity, syntactic ambiguity
  • Precision, recall, false positives, false negatives

Machine Vision – class notes, Chapter 21

  • Human vision, rods, cones, visual cortex, camera obscura, stereoscopic vision
  • Image capture
  • Edge detection, camouflage, discontinuities – depth, orientation, illumination, convolution, Canny edge detector
  • Segmentation/regions
  • Classifying edges in line drawings – convex (+), concave (-), occluding (). Trihedral vertices – 16, Waltz labeling, ambiguous labeling
  • Texture – identifying textures via co-occurrence matrices, texels – shape and orientation
  • Motion – motion field of vectors
  • Model – invariant properties, arches, near miss examples

Search – class notes, Chapter 4, 5

  • Control strategies, game trees, brute force, blind search, informed search, heuristic evaluation function
  • Properties: complexity in time or memory, completeness, optimality, irrevocability
  • Depth first search, breadth first search, bi-directional search, hill climbing, beam search, A*, uniform cost, greedy, simulated annealing, genetic algorithms

Game Theory – class notes, Chapter 6

  • Game trees, evaluation function
  • Assumptions: rationality, zero sum
  • Minimax, Alpha-Beta
  • Tic-tac-toe, checkers, chess, go