Chapter 2

Q1. What is Rational Agent?

Q2. What are the 4 things on which a Rational Agent depends?

Q3. Define Actuators and Sensors. Give examples.

Q4. Differentiate between the following types of tasks with examples:

(i) Fully observable vs Partially observable

(ii) Single Agent vs Multi-agent

(iii) Deterministic vs Stochastic

(iv) Episodic vs Sequential

(v) Static vs Dynamic

(vi) Discrete vs Continuous

(vii) Known vs Unknown

Q5.  Define, draw block diagram and give examples for the following types of agents:

(i) Table Driven agent

(ii) Simple reflex agents

(iii) Model-based reflex agents

(iv) Goal-based agents

(v) Utility-based agents

Q6. Consider the vacuum cleaner agent function described in figure 2.3.

a. Show that this function is indeed rational under the assumptions listed on page 36.

b. Describe a rational agent function for the modified performance measure that deducts one point for each movement. Does the corresponding agent program require internal state?

c. Discuss possible agent designs for the cases in which clean squares can become dirty and the geography of the environment is unknown. Does it make sense for the agent to learn from its experience in these cases? If so, what should it learn?        

Q7. Consider a modified version of the vacuum environment (from question 2), 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 a simple reflex agent with a randomized agent function outperform a simple reflex agent? Explain. Describe the design of your randomized agent.

c. Describe an environment in which your randomized agent will perform very poorly. Explain why this environment is bad for your randomized agent.

Q8. The vacuum environments above have all been deterministic. Discuss possible agent programs for each of the following stochastic versions:

d. Murphy’s law: 25% 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?

e. Small children: At each time step, each clean square has a 10% chance of becoming dirty. Describe a rational agent design for this case.