AI Homework #1
2006/3/21
2.1 Define in your own words the following terms: agent, agent function, agent
program, rationality, autonomy, reflex agent, model-based agent, goal-based agent,
utility-based agent, learning agent.
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Agent: an entity that acts according to what it perceives.
Agent function: a function that maps every possible percept sequence into an
action in response.
Agent program: a program that implements an agent function and runs on a
physical machine architecture.
Rationality: a rational agent is one that acts so as to achieve the best outcome or,
when there is uncertainty, the best expected outcome.
Autonomy: a property of agents that learn what it can to compensate for partial or
incorrect prior knowledge.
Reflex agent: an agent who acts solely on its current percept.
Model-based agent: an agent that updates its internal model of current world
state over time and acts according to this internal state.
Goal-based agent: an agent that acts in order to achieve or maximize its
designated goals.
Utility-based agent: an agent that acts in order to maximize the expected utility
of the new state after its action.
Learning agent: an agent that learns and improves its performance based on its
experience over time
2.5 For each of the following agents, develop a PEAS description of the task
environment:
a. Robot soccer player; b. Internet book-shopping agent; c. Autonomous Mars rover;
d. Mathematician’s theorem-proving assistant.
Task
Robot soccer
player
Internet
book-shopping
agent
Performance
Measure
Score of the
team or the
competitor,
winning
game
Minimizing
cost,
information
about
interesting
books
Environment
Actuators
Ball, team
members,
competitors,
a sport
ground
The robot
devices, such
as legs for
running and
kicking
The Internet,
browsers
Add a new
order, retrieve
existing order
information,
display
information to
user
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Sensors
Video camera,
communication
links among
team members,
orientation
sensors, touch
sensors
Web pages,
buttons or
hyperlinks
clicked by
users
Autonomous Mars Collect,
rover
analyze and
explore
samples on
Mars
Theorem-proving Time
assistant
requirement,
degree of
correction
Mars, vehicle
The theorem
to prove,
existing
axioms
Collection ,
analysis, and
motion
devices, radio
transmitter
Accept the
right theorem,
reject the
wrong
theorem, infer
based on
axioms and
facts
Video camera,
audio
receivers,
communication
links
Input device
that reads the
theorem to
prove
2.10 Consider a modified version of the vacuum environment in Exercise 2.7, in
which the geography of the environment – its extent, boundaries, and obstacles – is
unknown, as is the initial dirt configuration. (The agent can go Up and Down as well
as Left and Right.)
a. Can a simple reflex agent be perfectly rational for this environment? Explain.
b. Can s simple reflex agent with a randomized agent function outperform a simple
reflex agent? Design such an agent and measure its performance on several
environments.
c. Can you design an environment in which your randomized agent will perform very
poorly? Show your results.
d. Can a reflex agent with state outperform a simple reflex agent? Design such an
agent and measure its performance on several environments. Can you design a
rational agent of this type?
a. Because a simple reflex agent does not maintain a model about the geography and
only perceives location and local dirt. When it tries to move to a location that is
blocked by a wall, it will get stuck forever.
b. One possible design is as follows:
if (Dirty)
Suck;
else
Randomly choose a direction to move;
This simple agent works well in normal, compact environments; but needs a long time
to cover all squares if the environments contain long connecting passages, such as the
one in c.
c. The above randomized agent will perform poorly in environments like the
following one. It will need a lot of time to get through the long passage because of the
random walk.
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d. A reflex agent with state can first explore the environment thoroughly and build a
map of this environment. This agent can do much better that the simple reflex agent
because it maintains the map of the environment and can choose action based on not
only the current percept, but also current location inside the map.
2.12 The vacuum environments in the preceding exercises have all been deterministic.
Discuss possible agent programs for each of the following stochastic versions:
a. Murphy’s law: twenty-five percent of the time, the Suck action fails to clean the
floor if it is dirty and deposits dirt onto the floor if the floor is clean. How is your
agent program affected if the dirt sensor gives the wrong answer 10% of the time?
b. Small children: At each time step, each clean square has a 10% chance of becoming
dirty. Can you come up with a rational agent design for this case?
a. The failure of Suck action doesn’t cause any problem at all as long as we replace
the reflex agent’s ‘Suck’ action by ‘Suck until clean’.
If the dirt sensor gives wrong answers from time to time, the agent might just leave
the dirty location and maybe will clean this location when it tours back to this location
again, or might stay on the location for several steps to get a more reliable
measurement before leaving. Both strategies have their own advantages and
disadvantages. The first one might leave a dirty location and never return. The latter
might wait too long in each location.
b. In this case, the agent must keep touring and cleaning the environment forever
because a cleaned square might become dirty in near future.
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