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cs-470:review-for-midterm [2015/01/06 21:51] (current)
ryancha created
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 +==Agent Models==
  
 +*PEAS
 +*Types of Agents
 +*Types of Environments
 +*Decision Theoretic Model
 +
 +==Search==
 +
 +I may use n-queens, checkers, chess, 8-puzzle (sliding block), rubic'​s cube, backgammon, tic-tac-toe as the context in any question on the exam. If you are not familiar with these, you may want to learn the basic idea of how they work.
 +
 +All algorithms '''​as described in the book'''​
 +
 +Space complexity, Time complexity. You should be able to derive and discuss these. I prefer you not just memorize them.
 +
 +Optimality and Completeness. You should be able to prove and discuss these.
 +
 +You should know how all of the following algorithms work. This includes being able to simulate there execution for a small problem, and discuss their optimality, completeness,​ space complexity and time complexity.
 +
 +===Uninformed===
 +
 +Depth, Breadth, Uniform cost, Iterative Deepening, Bi-directional
 +
 +===Informed search===
 +
 +Greedy-best-first,​ A*, IDA*, Recursive Best-First Search, SMA*
 +
 +Heuristics, Admissibility,​ Consistency,​ Making Heuristics
 +
 +Tree vs. Graph search, Closed list.
 +
 +===Beyond Classic Search===
 +
 +On-line search
 +
 +Search in a continuous space
 +
 +Genetic Algorithms
 +
 +Simulated Annealing
 +
 +Particle swarm Optimization
 +
 +==Games==
 +
 +Ply, Minimax, Terminal test, Evaluation functions, Cut-off, Quiescence search, Horizon problem, and Complexity
 +
 +Min/Max search
 +
 +$\alpha \beta$ pruning
 +
 +$\alpha \beta$ pruning w/ random nodes no limits, that is -$\infty$ to $\infty$
 +
 +$\alpha \beta$ pruning w/ random nodes and limits
 +
 +== Probability ==
 +
 +* Axioms of Prob.
 +* Definition of Conditional Prob.
 +* Notation, including: P(a) means the probability P(A=True), P(A) means a vector of probabilities corresponding to all values (all two, in the binary case) of the random variable A.
 +* Marginalizing out variable by summing, i.e. $p(a)=\Sigma_b P(a,b)$
 +* Using the joint to compute arbitrary probabilities and arbitrary conditional probabilities
 +* Bayes Law
 +* Chain rule, using it both directions, that is to (1) split a joint probabilities '''​into'''​ conditionals and marginals, and (2) form joint probabilities '''​from'''​ conditionals and marginals.
cs-470/review-for-midterm.txt ยท Last modified: 2015/01/06 21:51 by ryancha
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