Instructor’s Solution Manual
Artificial Intelligence
A Modern Approach
Fourth Edition
Stuart J. Russell and Peter Norvig
with contributions from
Nalin Chhibber, Ernest Davis, Nicholas J. Hay, Jared Moore, Alex Rudn
...
Instructor’s Solution Manual
Artificial Intelligence
A Modern Approach
Fourth Edition
Stuart J. Russell and Peter Norvig
with contributions from
Nalin Chhibber, Ernest Davis, Nicholas J. Hay, Jared Moore, Alex Rudnick,
Mehran Sahami, Xiaocheng Mesut Yang, and Albert Yu
Exercise 1.1.#DEFA
Define in your own words: (a) intelligence, (b) artificial intelligence, (c) agent, (d) rationality, (e) logical reasoning.
Exercise 2.1.#DFAG
Define in your own words the following terms: agent, environment, sensor, actuator,
percept, agent function, agent program.
Exercise 3.1.#FORM
Explain why problem formulation must follow goal formulation.
Exercise 4.1.#ASTF
For each of the following assertions, say whether it is true or false and support your
answer with examples or counterexamples where appropriate.
Exercise 5.1.#ORAC
Suppose you have an oracle, OM(s), that correctly predicts the opponent’s move in any
state. Using this, formulate the definition of a game as a (single-agent) search problem.
Describe an algorithm for finding the optimal move.
Exercise 6.1.#AUSM
How many solutions are there for the three-color map-coloring problem in Figure 6.1?
How many solutions if four colors are allowed? Two colors?
Exercise 17.1.#MDPX
For the 4 × 3 world shown in Figure 17.1, calculate which squares can be reached from
(1,1) by the action sequence [Right; Right; Right; Up; Up] and with what probabilities. Explain how this computation is related to the prediction task (see Section 14.2.1) for a hidden
Markov model
23.1 Language Models
Exercise 23.1.#TXUN
Read the following text once for understanding, and remember as much of it as you can.
There will be a test later
26.4 Robotic Perception
Exercise 26.4.#MCLB
Monte Carlo localization is biased for any finite sample size—i.e., the expected value of
the location computed by the algorithm differs from the true expected value—because of the
way particle filtering works. In this question, you are asked to quantify this bias.
To simplify, consider a world with four possible robot locations: X = fx1; x2; x3; x4g.
Initially, we draw N ≥ 1 samples uniformly from among those locations. As usual, it is
perfectly acceptable if more than one sample is generated for any of the locations X. Let Z
be a Boolean sensor variable characterized by the following conditional probabilities:
28.1 AI Components
Exercise 28.1.#FUTP
Compare the price of various component technologies used in AI, and create charts for
how those prices have changed over the last twenty years.
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