Artificial Intelligence: Modern Approaches (4th Edition) Chapter 1 Reading Analysis

Artificial Intelligence: A Modern Approach (4th Edition)#

Artificial Intelligence (AI) refers to the field of study that focuses on how to make computers capable of performing tasks that typically require human intelligence. Core technologies involved in AI include machine learning, natural language processing, computer vision, knowledge representation, reasoning, and planning.

Explosive growth in data volume: The internet, sensor technology, and other advancements have brought about massive amounts of data, providing a foundation for AI technologies.
Rapid improvement in computational power for data processing: Advances in computational power have enabled more efficient data processing, more complex model training, and faster response capabilities.
Widespread availability of open-source software and hardware tools: The popularity of open-source technology has lowered the cost of AI and also facilitated research and development in AI technologies.
Improvements in machine learning algorithms: Machine learning is one of the core technologies in AI, and recent algorithmic advancements have been driving the development of AI.

Speech recognition: Converting speech signals into text.
Image recognition: Automatically identifying objects, scenes, and other information from images.
Natural language processing: Enabling computers to understand and process human language.
Machine translation: Language translation performed by computers.
Gaming: AI applications in games such as chess and poker have surpassed top human players.
Autonomous driving: AI technology is being used for self-driving vehicles in automotive, aviation, and maritime transportation.
Finance: AI applications in the finance industry include risk management, investment decisions, and customer service.

An intelligent agent refers to a program or robot that can autonomously perform tasks. It interacts with the environment through basic modules such as perception, reasoning, and action to achieve goals.

Perception: An intelligent agent perceives the state of the environment through sensors.
Reasoning: An intelligent agent makes inferences and judgments based on the perceived information.
Action: An intelligent agent changes the environment by executing actions.

Simple reflex agents: They directly execute actions based on current perception.
Model-based agents: They build an internal model from observations of the environment to better perform actions.
Learning agents: They improve performance through learning, including model-based learning and model-free learning.
Autonomous agents: They can set goals, plan, self-assess, and adjust themselves.

Problem solving refers to the process of finding solutions and answers to a problem. It typically involves steps such as problem modeling, searching the solution space, and evaluating results.

Depth-first search: Starts from the initial state and follows a path until it cannot continue, then backtracks and tries another path.
Breadth-first search: Expands all unexplored states in breadth-first order until the goal state is found.
Uniform-cost search: Searches for the minimum cost path in the state space based on a cost function.
A* search: Considers both the cost from the current state to the goal state and the cost already incurred to choose the next direction, aiming to find the optimal solution.

Search is a problem-solving method that aims to find solutions in the solution space that satisfy specific goals. Search algorithms typically start from an initial state and use a set of rules or methods to explore the target state.

Depth-first search: Starts from the initial state and follows a path until it cannot continue, then backtracks and tries another path.
Breadth-first search: Expands all unexplored states in breadth-first order until the goal state is found.
Uniform-cost search: Searches for the minimum cost path in the state space based on a cost function.
A* search: Considers both the cost from the current state to the goal state and the cost already incurred to choose the next direction, aiming to find the optimal solution.