Symbolické strojové učení

  • Stránky předmětu: Symbolické strojové učení
  • Přednášející: Jiří Kléma, Filip Železný
  • Cvičící: Jáchym Barvínek, Ondřej Hubáček, Petr Ryšavý, Martin Svatoš

Cvičení

Zkouška

06.06.2018

  1. (5 pnts) Difference between PAC-learning agent and mistake-bound agent.
    • (2 pnts) What does it mean when an agent in both frameworks learns?
    • (3 pnts) What does it mean when it learns efficiently? Online?
  2. (10 pnts) Space-version agent. There are given two agent with different hypotheses spaces. First is all possible 3-conjunctions (non-negative) of n variables. Second is all n-conjunctions of positive and negative literals.
    • (3 pnts) For each agent: does it learn online?
    • (3 pnts) For each agent: does it learn efficiently?
    • (4 pnts) For the first agent: given the first negative observation (0,1,1,1,…,1), what will be the agent's decision on the next observation (0,1,0,1,…)?
  3. (15 pnts) Relative Least General Generalization (rlgg). Given background knowledge B = {half(4,2), half(2,1), int(2), int(1)}. What will be the rlgg of o1 = even(4) and o2 = even(2) relative to the background?
    • (10 pnts) Apply algorithm, draw tables, theta functions.
    • (5 pnts) Make a reduction step relative to B. Why is it needed?
  4. (10 pnts) Bayesian networks.
    • (2 pnts) Find optimal, efficient, complete network (something like Season → Temperature → (two children: → Ice Cream Sales, → Heart Attack Rate)).
    • (2 pnts) Then compute CPT (conditional probability tables).
    • (3 pnts) Compute Pr(Spring|Good Ice Cream Sales, No Heart Attack)
    • (3 pnts) Compute Pr(Heart Attack|Winter, Bad Sales).
  5. (5 pnts) Q-learning. Given 5 small questions, response True/False and provide your reasoning.
    • (1 pnt) Can Q-learning be extended to infinite states or action space? How would it handle this?
    • (1 pnt) Does Q-learning use on-policy update? What is the difference from off-policy update?
    • (1 pnt) Does Q-learning always converge? If so, is it conditioned by anything? By what?
    • (1 pnt) Is Q-learning just an instance of temporal difference learning? If not, what is different?
    • (1 pnt) What is the difference between Q-learning and direct utility estimation or adaptive dynamic programming? What is better?
  6. (5 pnts) Q-learning representation.
    • There is a robot moving in a swimming pool, which can move in either of 3 dimensions and it has exactly one propeller for each dimension. It can also move with two different speeds. There is a treasure at a specific place and a specific depth. There are mines at some places as well. If the robot hits a mine or the wall, it restarts at a random position.
    • (3 pnts) Describe states, actions, rewards of a specific game. You may provide two different representations.
    • (2 pnts) Describe Q-learning representation, the update rule, gamma, alpha value. How are Q values defined?

Ukázkové příklady z roku 2019 + řešení

courses/b4m36smu.txt · Poslední úprava: 2019/06/03 10:22 autor: votrumar
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