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Artificial Intelligence: A Modern Approach, 4th Edition by Peter Norvig and Stuart Russell, Chapters 1 – 28
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SOLUTIONS & INSTRUCTOR MANUAL Artificial Intelligence: A Modern Approach, 4th Edition by Peter Norvig and Stuart Russell, Chapters 1 – 28
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Class notes Computer science Artificial Intelligence
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www.EnggTree.comUNIT I PROBLEM SOLVING
Introduction to AI - AI Applications - Problem solving agents – search algorithms –
uninformed search strategies – Heuristic search strategies – Local search and optimization
problems – adversarial search – constraint satisfaction problems (CSP)
Part A
1. What is Artificial Intelligence?
Artificial Intelligence is the study of how to make computers do things which
at the moment people do better.
2. What is an agent?
An agent is anything that can be viewed as perceiving its environment through
sensors and acting upon that environment through actuators.
3. What are the different types of agents?
A human agent has eyes, ears, and other organs for sensors and hands, legs,
mouth, and other body parts for actuators.
A robotic agent might have cameras and infrared range finders for sensors
and various motors for actuators.
A software agent receives keystrokes, file contents, and network packets as
sensory inputs and acts on the environment by displaying on the screen, writing files,
and sending network packets.
Generic agent - A general structure of an agent who interacts with the
environment.
4. Define rational agent.
For each possible percept sequence, a rational agent should select an action that is
expected to maximize its performance measure, given the evidence provided by the
percept sequence and whatever built-in knowledge the agent has. A rational agent
should be autonomous.
5. List down the characteristics of intelligent agent.
Internal characteristics are
Learning/reasoning: an agent has the ability to learn from previous
experience and to successively adapt its own behaviour to the environment.
Reactivity: an agent must be capable of reacting appropriately to influences or
information from its environment.
Autonomy: an agent must have both control over its actions and internal
states. The degree of the agent’s autonomy can be specified. There may need
intervention from the user only for important decisions.
Goal-oriented: an agent has well-defined goals and gradually influence its
environment and so achieve its own goals.
External characteristics are
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Communication: an agent often requires an interaction with its environment
to fulfil its tasks, such as human, other agents, and arbitrary information sources.
Cooperation: cooperation of several agents permits faster and better solutions
for complex tasks that exceed the capabilities of a single agent.
Mobility: an agent may navigate within electronic communication networks.
Character: like human, an agent may demonstrate an external behaviour with
many human characters as possible.
6. What are various applications of AI? or What can AI do today?
Robotic vehicles
Speech recognition
Autonomous planning and scheduling
Game playing
Spam fighting
Logistics planning
Robotics
Machine Translation
7. Are reflex actions (such as flinching from a hot stove) rational? Are they
intelligent?
Reflex actions can be considered rational. If the body is performing the action,
then it can be argued that reflex actions are rational because of evolutionary
adaptation. Flinching from a hot stove is a normal reaction, because the body wants to
keep itself out of danger and getting away from something hot is a way to do that.
Reflex actions are also intelligent. Intelligence suggests that there is reasoning
and logic involved in the action itself.
8. Is AI a science, or is it engineering? Or neither or both? Explain.
AI is both science and engineering. Observing and experimenting, which are
at the core of any science, allows us to study artificial intelligence. From what we
learn by observation and experimentation, we are able to engineer new systems that
encompass what we learn and that may even be capable of learning themselves.
9. What are the various agent programs in intelligent systems?
Simple reflex agents
Model-based reflex agents
Goal-based agents
Utility-based agents
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10. Define the problem solving agent.
A Problem solving agent is a goal-based agent. It decides what to do by
finding sequence of actions that lead to desirable states. The agent can adopt a goal
and aim at satisfying it. Goal formulation is the first step in problem solving.
11. Define the terms goal formulation and problem formulation.
Goal formulation based on the current situation and the agent’s performance
measure is the first step in problem solving. The agent’s task is to find out which
sequence of actions will get to a goal state.
Problem formulation is the process of deciding what actions and states to
consider given a goal.
12. List the steps involved in simple problem solving agent.
(i) Goal formulation
(ii) Problem formulation
(iii) Search
(iv) Search Algorithm
(v) Execution phase
13. What are the components of well-defined problems? (or)
What are the four components to define a problem? Define them?
The four components to define a problem are,
1) Initial state – it is the state in which agent starts in.
2) A description of possible actions – it is the description of possible actions
which are available to the agent.
3) The goal test – it is the test that determines whether a given state is goal
(final) state.
4) A path cost function – it is the function that assigns a numeric cost (value)
to each path.
The problem-solving agent is expected to choose a cost function that reflects
its own performance measure.
14. Differentiate toy problems and real world problems?
A toy problem is intended to illustrate various problem solving methods. It can
be easily used by different researchers to compare the performance of algorithms. A
real world problem is one whose solutions people actually care about.
15. Give example for real world end toy problems.
Real world problem examples:
Airline travel problem.
Touring problem.
Traveling salesman problem
VLSI Layout problem
Robot navigation
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