AI is about practical reasoning: reasoning in order to do something. A coupling of perception, reasoning, and acting comprises an agent. An agent acts in an environment. An agent’s environment often includes other agents. An agent together with its environment is called a world.

An agent could be, for example, a coupling of a computational engine with physical sensors and actuators, called a robot, where the environment is a physical setting. An autonomous agent is one that acts in the world without human intervention. A semi-autonomous agent acts with a human-in-the-loop who may provide perceptual information and carry out the task. An agent could be a program that acts in a purely computational environment, a software agent, often called a bot.

Figure 1.4 shows a black-box view of an agent in terms of its inputs and outputs. At any time, what an agent does depends on:

• •

  prior knowledge about the agent and the environment

• •

  stimuli received from the environment, which can include observations about the environment (e.g., light, sound, keyboard commands, web requests) as well as actions that the environment imposes on the agent (e.g., bumping the agent)

• •

  past experiences, including history of interaction with the environment (its previous actions and stimuli) and other data, from which it can learn

• •

  goals that it must try to achieve or preferences over states of the world

• •

  abilities, the primitive actions the agent is capable of carrying out.

Inside the black box, an agent has a belief state that can encode beliefs about its environment, what it has learned, what it is trying to do, and what it intends to do. An agent updates this internal state based on stimuli. It uses the belief state and stimuli to decide on its actions. Much of this book is about what is inside this black box.

Purposive agents have preferences or goals. They prefer some states of the world to other states, and they act to try to achieve the states they prefer most. The non-purposive agents are grouped together and called nature. Whether or not an agent is purposive is a modeling assumption that may, or may not, be appropriate. For example, for some applications it may be appropriate to model a dog as purposive, such as drug-sniffing dogs, and for others it may suffice to model a dog as non-purposive, such as when they are just part of the environment.

If an agent does not have preferences, by definition it does not care what world state it ends up in, and so it does not matter to it what it does. The reason to design an agent is to instill preferences in it – to make it prefer some world states and try to achieve them. An agent does not have to know its preferences explicitly. For example, a thermostat for a heater is an agent that senses the world and turns the heater either on or off. There are preferences embedded in the thermostat, such as to keep the room at a pleasant temperature, even though the thermostat arguably does not know these are its preferences. The preferences of an agent are often the preferences of the designer of the agent, but sometimes an agent can acquire goals and preferences at run time.

This is an all-encompassing view of intelligent agents varying in complexity from a simple thermostat, to a diagnostic advising system whose perceptions and actions are mediated by human beings, to a team of mobile robots, to society itself. An agent does not have access to anything else; anything that does not affect one of these inputs cannot affect the agent’s action.