foundations of computational agents
The following are the main points you should have learned from this chapter:
Artificial intelligence is the study of computational agents that act intelligently.
An agent acts in an environment and only has access to its abilities, its prior knowledge, its history of stimuli, and its goals and preferences.
A physical symbol system manipulates symbols to determine what to do.
A designer of an intelligent agent should be concerned about modularity, how to describe the world, how far ahead to plan, uncertainty in both perception and the effects of actions, the structure of goals or preferences, other agents, how to learn from experience, how the agent can reason while interacting with the environment, and the fact that all real agents have limited computational resources.
To solve a task by computer, the computer must have an effective representation with which to reason.
To know when it has solved a task, an agent must have a definition of what constitutes an adequate solution, such as whether it has to be optimal, approximately optimal, or almost always optimal, or whether a satisficing solution is adequate.
In choosing a representation, an agent designer should find a representation that is as close as possible to the task, so that it is easy to determine what is represented and so it can be checked for correctness and be able to be maintained. Often, users want an explanation of why they should believe the answer.