1. Artificial Intelligence Programming Spring 2016
Problems on Chapter 2
Artificial Intelligence Programming
Spring 2016

2. Question 1
Which of the following is true regarding a ‘Rational agent’?
A rational agent selects an action that maximizes its performance measure given the percept sequence and the built-in knowledge
A rational agent selects a percept sequence that maximizes its performance measure given the action and the built-in knowledge
A rational agent selects an action and a sequence that maximizes its performance measure given the built-in knowledge
A rational agent selects an action that maximizes its performance measure given the percept sequence only

3. Question 1
Which of the following is true regarding a ‘Rational agent’?
A rational agent selects an action that maximizes its performance measure given the percept sequence and the built-in knowledge
A rational agent selects a percept sequence that maximizes its performance measure given the action and the built-in knowledge
A rational agent selects an action and a sequence that maximizes its performance measure given the built-in knowledge
A rational agent selects an action that maximizes its performance measure given the percept sequence only
Answer (a)

4. Question 2 Consider the following table:
Which of the following is the correct match given the above table?
A-I, B-II, C-III, D-IV
A-III, B-IV, C-I, D-II
A-IV, B-III, C-I, D-II
A-III, B-IV, C-II, D
Agent
Role
A) Goal Based Agent
i) Uses condition-action rules
B) Utility Based Agent
ii) Stores representation of the percept history
C) Simple Reflex Agent
iii) Uses search and planning
D) Model-Based Reflex Agent
iv) Generates high-quality behavior

5. Question 2 Consider the following table:
Which of the following is the correct match given the above table?
A-I, B-II, C-III, D-IV
A-III, B-IV, C-I, D-II
A-IV, B-III, C-I, D-II
A-III, B-IV, C-II, D
Answer(b)
Agent
Role
A) Goal Based Agent
i) Uses condition-action rules
B) Utility Based Agent
ii) Stores representation of the percept history
C) Simple Reflex Agent
iii) Uses search and planning
D) Model-Based Reflex Agent
iv) Generates high-quality behavior

6. Figure 2.3 Examples of agent types and their PAGE descriptions.
Question 3
Agent Type
Percepts
Actions
Goals
Environment
Architecture
Medical diagnosis system
Symptoms, findings, patient's answers
Questions, tests, treatments
Healthy patient, minimize costs
Patient, hospital ?
Satellite image analysis system
Pixels of varying intensity, color
Print a categorization of scene
Correct categorization
Images from orbiting satellite
Part-picking robot
Pixels of varying intensity
Pick up parts and sort into bins
Place parts in correct bins
Conveyor belt with parts
Refinery controller
Temperature, pressure readings
Open, close valves; adjust temperature
Maximize purity, yield, safety
Refinery
Interactive English tutor
Typed words
Print exercises, suggestions, corrections
Maximize student's score on test
Set of students
Figure 2.3 Examples of agent types and their PAGE descriptions.
(Artificial Intelligence A Modern Approach by Stuart Russell and Peter Norvig. 1st Edition, Prentice Hall, 1995)
Question : For each of the environments in Figure 2.3, determine what type of agent architecture is most appropriate (table lookup, simple reflex, goal-based or utility-based).

7. Figure 2.3 Examples of agent types and their PAGE descriptions.
Question 3
Agent Type
Percepts
Actions
Goals
Environment
Architecture
Medical diagnosis system
Symptoms, findings, patient's answers
Questions, tests, treatments
Healthy patient, minimize costs
Patient, hospital
Satellite image analysis system
Pixels of varying intensity, color
Print a categorization of scene
Correct categorization
Images from orbiting satellite
Part-picking robot
Pixels of varying intensity
Pick up parts and sort into bins
Place parts in correct bins
Conveyor belt with parts
Refinery controller
Temperature, pressure readings
Open, close valves; adjust temperature
Maximize purity, yield, safety
Refinery
Interactive English tutor
Typed words
Print exercises, suggestions, corrections
Maximize student's score on test
Set of students
Figure 2.3 Examples of agent types and their PAGE descriptions.
(Artificial Intelligence A Modern Approach by Stuart Russell and Peter Norvig. 1st Edition, Prentice Hall, 1995)
Question : For each of the environments in Figure 2.3, determine what type of agent architecture is most appropriate (table lookup, simple reflex, goal-based or utility-based).

8. Figure 2.13 Examples of environments and their characteristics
Question 4
Environment
Observable
Deterministic
Episodic
Static
Discrete
Agents
Crossword Puzzle ?
Chess without a clock
Taxi driving
Medical diagnosis system
Image-analysis system
Part-picking robot
Refinery controller
Interactive English tutor
Figure 2.13 Examples of environments and their characteristics
(Artificial Intelligence A Modern Approach by Stuart Russell and Peter Norvig. 3rd Edition, Prentice Hall, 2010)
Question : For each of the environments, determine what type characteristics it exhibits.
(Fully/Partially Observable, Deterministic/Stochastic, Episodic/Sequential, Static/Dynamic/Semi, Discrete/Continuous , Single/Multi-Agents)

9. Figure 2.13 Examples of environments and their characteristics
Question 4
Environment
Observable
Deterministic
Static
Discrete
Agents
Crossword Puzzle
Fully
Single
Chess without a clock
Multi
Taxi driving
Partially
Stochastic
Dynamic
Continuous
Medical diagnosis system
Image-analysis system
Semi
Part-picking robot
Refinery controller
Interactive English tutor
Figure 2.13 Examples of environments and their characteristics
(Artificial Intelligence A Modern Approach by Stuart Russell and Peter Norvig. 3rd Edition, Prentice Hall, 2010)
Question : For each of the environments, determine what type of characteristics it exhibits.
(Fully/Partially Observable, Deterministic/Stochastic, Static/Dynamic/Semi, Discrete/Continuous , Single/Multi-Agents)

10. Question 5
Explain (briefly) the difference between goal-based agents and utility-based agents. [Spring 2015]

11. Question 5
Explain (briefly) the difference between goal-based agents and utility-based agents. [Spring 2015]
Answer :
Goal-based agent: Needs some goal pointing to desirable situations. We need to incorporate tasks like search and planning.
Utility-based agent: It is not just about reaching a goal, but an optimal goal. Generate high-quality behavior. We need a utility function mapping states to a real number.