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CSC 550: Introduction to Artificial Intelligence

Fall 2008

Midterm Review

Overview
history of AI, Turing test
philosophical extremes: neats vs. scruffies, GOFAI vs. emergents, weak vs. strong
Scheme and AI programming
S-expressions (atoms & lists), functional expressions
primitive functions
+ - * / quotient remainder max min abs expt floor round
= < <= > >= exact->inexact inexact->exact
car cdr cons list list-ref length member reverse append equal?
set! let display begin read
programming tools
function definitions, if, cond
full recursion vs. tail-recursion
first-class functions, map, apply
AI programming example
Eliza: knowledge representation, pattern-matching
AI as search
state spaces
search control strategies
uninformed+bold:
STUPID
uninformed+tentative:
depth first search (DFS), DFS with cycle checking
no guarantee to find shortest (or any) solution; low memory requirements
breadth first search (BFS), BFS with cycle checking
guaranteed to find shortest solution; high memory requirements
iterative deepening
guaranteed to find shortest but repeats search; low memory requirements
informed+bold:
hill-climbing
uses heuristics, potential pitfalls (e.g., foothills, plateaus)
low memory requirements
informed+tentative:
best first search
uses heuristics, does not guarantee shortest
high memory requirements, better than BFS if heuristic is good
optimization problems
find minimal cost path (based on some measure)
algorithm A
basically, best first using hybrid cost function (f = g + h)
g: actual cost so far along path; h: expected cost remaining
admissibility, algorithm A*
game playing
game characteristics: two-player, perfect information, zero-sum
minimax theorem
game trees
minimax search, alpha-beta pruning
case studies: chess, checkers, connect-4
Knowledge Representation and AI
foundational knowledge representation schemes
semantic nets
graphical representation, info retrieval as graph search, inheritance
conceptual dependency representation
defined richer set of links for networks, stores underlying concepts
frames
slot-filler model, inheritance, defaults, constraints, procedures
scripts
frame structure for representing events, utilized conceptual dependencies
expert systems
rule-based reasoning
common characteristics
expert-level performance, focused on a particular (usually narrow) domain,
must deal with uncertainty, must provide facilities for explanation, ...
layout: user interface, inference engine, knowledge base, case-specific data
rule application: depth vs. breadth, best vs. all, forward vs. backward, ...
uncertainty: probabilities, certainty factors, fuzzy logic, ...