As you see in below there are twenty questions.
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• Chapter 1: Introduction
o What is AI?
o Foundations of AI
o A Brief History of AI
• Chapter 2: Intelligent Agents
o Definition of a Rational Agent
o TASK Environment: PEAS
o The Structure of an Agent
• Chapter 3: Solving Problems by Searching
o Problem Formulation
o Breadth-First Search
o Uniform-Cost Search
o Depth-First Search
o Iterative-Deepening Search
o Bi-Directional Search
o Heuritic Searches
Greedy Best-First Search
Iterative Deepening A* Search
Recursive Best-First Search
Simplified Memory-Bounded A* Search
Effective Branching Factor: b*
Generating a heuristic algorithm
1. What are the four definitions of AI?
2. What is the difference between Weak AI and Strong AI
3. The creator of the Chinese Room experiment was John Searle. Who is John Searle?
4. Artificial Intelligence draws its foundations from many different academic areas. How
many different area can you name?
5. A few of the early pioneers in Artificial Intelligence were the following: Warren
McCulloch and Walter Pitts, Alan Turing, John McCarthy, and Marvin Minsky. Can you
name any of their contributions to the field?
6. When and where was the name Artificial Intelligence created?
7. What is the difference between the neats and the scruffies?
8. What are the definitions of the following terms: agent, percept, agent function, agent
9. For what does the acronym PEAS stand? What does it describe?
10. In describing an environment, what is the difference between the following terms:
observable and partially observable, deterministic and stochastic, episodic and
sequential, static and dynamic, and discrete and continuous.
11. The textbook describes four basic types of agents in chapter 2: simple reflex agents,
model-based reflex agents, goal-based agents, utility-based agents. What can you say
about the difference between these types of agents?
12. A learning agent can be divided into four components: learning element, performance
element, critic, problem generator. What is the role of the critic? Why is the problem
generator important for a learning agent?
13. There are three different ways in which an agent can represent its environment: atomic,
factored, structured. How would describe the difference between these three techniques?
14. When describing a search problem, there are five components: initial state, actions,
transition functions, goal test, path cost. Describe what each of these componets
15. A breadth-first search expands the shallowest node in the search tree. Which node does a
uniform-cost search expand? These two algorithms also differ on when they test for a
goal node. Can you describe the difference?
16. What is the difference between a depth-first search and an iterative deepening depthfirst
17. What is a bi-directional search? Does it have any special advantage over other searches?
18. How would you describe a heuristic? What does it mean when a heuristic is admissible?
19. One of the best known heuristic search algorithms is the A* algorithm. It uses three
functions: g(n), f(n), and h(n). Explain how these functions are used in the A* algorithm
and what each function represents.
20. One measure of the quality of a heuristic function is the effective branching factor or b*.
What value should b* be close to for a well-designed heuristic function?
21. Creating heuristic functions is not easy. One way to create a heuristic function is to use a
relaxed problem. Explain how this technique works, and, if you can, give an example.
22. A second method for generating a heuristic is a technique called pattern database.
Explain how this technique works.
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