Anticipatory system

In artificial intelligence (AI), anticipation is the concept of an agent making decisions based on predictions, expectations, or beliefs about the future. It is widely considered that anticipation is a vital component of complex natural cognitive systems. As a branch of AI, anticipatory systems is a specialization still echoing the debates from the 1980s about the necessity for AI for an internal model.

Elementary forms of artificial intelligence can be constructed using a policy based on simple if-then rules. An example of such a system would be an agent following the rules

A system such as the one defined above might be viewed as inherently reactive because the decision making is based on the current state of the environment with no explicit regard to the future. An agent employing anticipation would try to predict the future state of the environment (weather in this case) and make use of the predictions in the decision making. For example,

These rules appear more proactive, because they explicitly take into account possible future events. Notice though that in terms of representation and reasoning, these two rule sets are identical, both behave in response to existing conditions. Note too that both systems assume the agent is proactively

In practice, systems incorporating reactive planning tend to be autonomous systems proactively pursuing at least one, and often many, goals. What defines anticipation in an AI model is the explicit existence of an inner model of the environment for the anticipatory system (sometimes including the system itself). For example, if the phrase it will probably rain were computed on line in real time, the system would be seen as anticipatory.

In 1985, Robert Rosen defined an anticipatory system as follows:

In Rosen's work, analysis of the example : "It's raining outside, therefore take the umbrella" does involve a prediction. It involves the prediction that "If it is raining, I will get wet out there unless I have my umbrella". In that sense, even though it is already raining outside, the decision to take an umbrella is not a purely reactive thing. It involves the use of predictive models which tell us what will happen if we don't take the umbrella, when it is already raining outside.

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