Once you have some requirements on the nature of a solution, you must represent the task so a computer can solve it.

Computers and human minds are examples of physical symbol systems. A symbol is a meaningful pattern that can be manipulated. Examples of symbols are written words, sentences, gestures, marks on paper, or sequences of bits. A symbol system creates, copies, modifies, and destroys symbols. Essentially, a symbol is one of the patterns manipulated as a unit by a symbol system. The term physical is used, because symbols in a physical symbol system are physical objects that are part of the real world, even though they may be internal to computers and brains. They may also need to physically affect action or motor control.

Much of AI rests on the physical symbol system hypothesis of Newell and Simon [1976]:

A physical symbol system has the necessary and sufficient means for general intelligent action.

This is a strong hypothesis. It means that any intelligent agent is necessarily a physical symbol system. It also means that a physical symbol system is all that is needed for intelligent action; there is no magic or an as-yet-to-be-discovered quantum phenomenon required. It does not imply that a physical symbol system does not need a body to sense and act in the world. There is some debate as to whether hidden variables, that have not been assigned a meaning, but are useful, can be considered as symbols. The physical symbol system hypothesis is an empirical hypothesis that, like other scientific hypotheses, is to be judged by how well it fits the evidence, and by what alternative hypotheses exist. Indeed, it could be false.

An intelligent agent can be seen as manipulating symbols to produce action. Many of these symbols are used to refer to things in the world. Other symbols may be useful concepts that may or may not have external meaning. Yet other symbols may refer to internal states of the agent.

An agent can use a physical symbol system to model the world. A model of a world is a representation of an agent’s beliefs about what is true in the world or how the world changes. The world does not have to be modeled at the most detailed level to be useful. All models are abstractions; they represent only part of the world and leave out many of the details. An agent can have a very simplistic model of the world, or it can have a very detailed model of the world. The level of abstraction provides a partial ordering of abstraction. A lower-level abstraction includes more details than a higher-level abstraction. An agent can have multiple, even contradictory, models of the world. Models are judged not by whether they are correct, but by whether they are useful.

Choosing an appropriate level of abstraction is difficult for the following reasons:

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  A high-level description is easier for a human to specify and understand.

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  A low-level description can be more accurate and more predictive. Often high-level descriptions abstract away details that may be important for actually solving the task.

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  The lower the level, the more difficult it is to reason with. This is because a solution at a lower level of detail involves more steps and many more possible courses of action exist from which to choose.

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  An agent may not know the information needed for a low-level description. For example, the delivery robot may not know what obstacles it will encounter or how slippery the floor will be at the time that it must decide what to do.

It is often a good idea to model an environment at multiple levels of abstraction. This issue is further discussed in Section 2.3.

Biological systems, and computers, can be described at multiple levels of abstraction. At successively lower levels of animals are the neuronal level, the biochemical level (what chemicals and what electrical potentials are being transmitted), the chemical level (what chemical reactions are being carried out), and the level of physics (in terms of forces on atoms and quantum phenomena). What levels above the neuronal level are needed to account for intelligence is still an open question. These levels of description are echoed in the hierarchical structure of science itself, where scientists are divided into physicists, chemists, biologists, psychologists, anthropologists, and so on. Although no level of description is more important than any other, we conjecture that you do not have to emulate every level of a human to build an AI agent but rather you can emulate the higher levels and build them on the foundation of modern computers. This conjecture is part of what AI studies.

The following are two levels that seem to be common to both biological and computational entities:

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  The knowledge level is the level of abstraction that considers what an agent knows and believes and what its goals are. The knowledge level considers what an agent knows, but not how it reasons. For example, the delivery agent’s behavior can be described in terms of whether it knows that a parcel has arrived or not and whether it knows where a particular person is or not. Both human and robotic agents are describable at the knowledge level. At this level, you do not specify how the solution will be computed or even which of the many possible strategies available to the agent will be used.

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  The symbol level is a level of description of an agent in terms of the reasoning it does. To implement the knowledge level, an agent manipulates symbols to produce answers. Many cognitive science experiments are designed to determine what symbol manipulation occurs during reasoning. Whereas the knowledge level is about what the agent believes about the external world and what its goals are in terms of the outside world, the symbol level is about what goes on inside an agent to reason about the external world.