Artificial intelligence, or AI, is the field that studies the synthesis and analysis of computational agents that act intelligently. Let us examine each part of this definition.

An agent is something that acts in an environment; it does something. Agents include worms, dogs, thermostats, airplanes, robots, humans, companies, and countries.

We are interested in what an agent does; that is, how it acts. We judge an agent by its actions.

An agent acts intelligently when

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  what it does is appropriate for its circumstances and its goals, taking into account the short-term and long-term consequences of its actions

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  it is flexible to changing environments and changing goals

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  it learns from experience

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  it makes appropriate choices given its perceptual and computational limitations

A computational agent is an agent whose decisions about its actions can be explained in terms of computation. That is, the decision can be broken down into primitive operations that can be implemented in a physical device. This computation can take many forms. In humans this computation is carried out in “wetware”; in computers it is carried out in “hardware.” Although there are some agents that are arguably not computational, such as the wind and rain eroding a landscape, it is an open question whether all intelligent agents are computational.

All agents are limited. No agents are omniscient or omnipotent. Agents can only observe everything about the world in very specialized domains, where “the world” is very constrained. Agents have finite memory. Agents in the real world do not have unlimited time to act.

The central scientific goal of AI is to understand the principles that make intelligent behavior possible in natural or artificial systems. This is done by

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  the analysis of natural and artificial agents

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  formulating and testing hypotheses about what it takes to construct intelligent agents and

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  designing, building, and experimenting with computational systems that perform tasks commonly viewed as requiring intelligence.

As part of science, researchers build empirical systems to test hypotheses or to explore the space of possible designs. These are quite distinct from applications that are built to be useful for an application domain.

The definition is not for intelligent thought alone. We are only interested in thinking intelligently insofar as it leads to more intelligent behavior. The role of thought is to affect action.

The central engineering goal of AI is the design and synthesis of useful, intelligent artifacts. We actually want to build agents that act intelligently. Such agents are useful in many applications.