Sort the following numbers 13, 4, 8 and show all steps using non deterministic algorithm approach
Answers
Explanation:
In which we see how an agent can find a sequence of actions that achieves its
goals, when no single action will do.
The simplest agents discussed in Chapter 2 were the reflex agents, which base their actions on
a direct mapping from states to actions. Such agents cannot operate well in environments for
which this mapping would be too large to store and would take too long to learn. Goal-based
agents, on the other hand, can succeed by considering future actions and the desirability of
their outcomes.
This chapter describes one kind of goal-based agent called a problem-solving agent.
PROBLEM-SOLVING
AGENT
Problem-solving agents think about the world using atomic representations, as described in
Section 2.4.7—that is, states of the world are considered as wholes, with no internal structure
visible to the problem-solving algorithms. Goal-based agents that use more advanced fac-
tored or structured representations are usually called planning agents and are discussed in
Chapter 7 and 11.
We start our discussion of problem solving by defining precisely the elements that con-
stitute a “problem” and its “solution,” and give several examples to illustrate these definitions.
We then describe several general-purpose search algorithms that can be used to solve these
problems. We will see several uninformed search algorithms—algorithms that are given no
information about the problem other than its definition. Although some of these algorithms
can solve any solvable problem, none of them can do so efficiently. Informed search al-
gorithms, on the other hand, can often do quite well given some idea of where to look for
solutions.
In this chapter, we limit ourselves to the simplest kind of task environment, for which
the solution to a problem is always a fixed sequence of actions. The more general case—where
the agent’s future actions may vary depending on future percepts—is handled in Chapter 4.
This chapter uses concepts from the analysis of algorithms. Readers unfamiliar with
the concepts of asymptotic complexity (that is, O() notation) and NP-completeness should
consult Appendix A.
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Chapters 3 and 4 from Russell/Norvig, Artificial Intelligence, 3e, ISBN: 0136042597 ©2010 DRAFT - For preview purposes only. Content is subject to change before final publication.
©2010 Pearson Education, Inc. Upper Saddle River, NJ 07458. All Rights Reserved.