AI Problem Solving and Graph Representation

What is the starting configuration in an AI problem referred to as?

What term describes the collection of all possible actions applied to a state in an AI problem?

How can problem solving in AI be visualized?

In the context of search problems, what does a path consist of?

What type of graph does not imply direction for its edges?

What term is used when a graph contains paths that lead back to the original node?

What is a weighted graph characterized by?

What defines a graph as being connected?

AI Problem

An AI problem has an initial configuration, called the initial state (s°), which is a starting point.
The goal of an AI problem is to reach a desired state, called the goal state (G).
To solve the problem, you apply actions to the initial state (s°) step-by-step, creating new states until the goal state (G) is achieved.

Problem Solving in AI

Problem solving aims to reach the goal state (G) starting from the initial state (s°).
A search space is formed by all possible actions applied to any state (si) in the problem, leading to new states (sj).

Problem Solving in AI - Graph Representation

Problem solving can be represented as a graph where nodes represent states and edges represent actions.
All nodes in this graph collectively define the search space, which represents every possible state of the problem.

Representation of Search Problems - Graphs and Trees

A search problem can be represented using a directed graph where:
- Nodes represent the states.
- Arcs represent the allowed actions.
An undirected graph implies that the edges do not have a direction, they connect two nodes without a specific direction.
A weighted graph has edges with weights (costs) associated with them, representing the cost of applying a specific action.
A path is a sequence of edges or nodes.
A loop, also known as a cycle, exists where a sequence of edges lead back to the original node.
A graph is considered connected if there is a path between any two nodes.

Explore the fundamental concepts of problem-solving in artificial intelligence, focusing on transitions from the initial state to the goal state. This quiz covers the representation of search problems using graphs and trees, emphasizing state and action relationships. Test your understanding of how AI navigates through possible states to achieve desired outcomes.