# Symbolické strojové učení

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

## 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?

31.5.2021

1. (5 pnts) Probabilities
• (1 pnt) Mathematically describe conditional independence.
• (2 pnts) If P(X,Y|W) and P(Y,Z|W) are conditionally independent, does it imply conditional independence P(X,Z|W)?
• (1 pnt) Can bayesian graph contain a cycle?
• (1 pnt) Is it true that if parents of X are given, X is independent of all other variables in graph? If no, correct this statement.
2. (5 pnts) Factors computation - given some graph and compute conditional probability
3. (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,…)?
4. (15 pnts) RLGG relative to B
• (10 pnts) Compute RLGG relative to B: RLGG(B→X1, B→X2).
• (5 pnts) Make a reduction of previous result.
5. (5 pnts) Some theoretical question, if concept learning agent learns, does the pac agent learn? Something in this sense…
6. (10 pnts) Reinforcement learning
• (5 * 1 pnt) Small questions asking about ADP and TD learning, shortly describe them, compare convergence, how fast they are, how do they compute utility…
• (4 pnts) 3 same grids given with different optimal policies, 2 terminal states, one with reward +1 and one with reward -1, other states have negative rewards r_a in grid A, r_b in grid B, r_c in grid C, determine based on the policies which absolute value of these rewards is the biggest and the smallest, justify your answer.
• (1 pnt) In the grid agent behaves according to the optimal policy. However, in two different episodes it makes different sequences of states, why is it possible?
courses/b4m36smu.txt · Poslední úprava: 2021/06/03 10:22 autor: mravciak
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