Reactive planning

In artificial intelligence, reactive planning denotes a group of techniques for action selection by autonomous agents. These techniques differ from classical planning in two aspects. First, they operate in a timely fashion and hence can cope with highly dynamic and unpredictable environments. Second, they compute just one next action in every instant, based on the current context. Reactive planners often (but not always) exploit reactive plans, which are stored structures describing the agent's priorities and behaviour.

Although the term reactive planning goes back to at least 1988, the term "" has now become a pejorative used as an antonym for proactive. Since nearly all agents using reactive planning are proactive, some researchers have begun referring to reactive planning as dynamic planning.

There are several ways to represent a reactive plan. All require a basic representational unit and a means to compose these units into plans.

A condition action rule, or if-then rule, is a rule in the form: if condition then action. These rules are called productions. The meaning of the rule is as follows: if the condition holds, perform the action. The action can be either external (e.g., pick something up and move it), or internal (e.g., write a fact into the internal memory, or evaluate a new set of rules). Conditions are normally boolean and the action either can be performed, or not.

Production rules may be organized in relatively flat structures, but more often are organized into a hierarchy of some kind. For example, subsumption architecture consists of layers of interconnected behaviors, each actually a finite state machine which acts in response to an appropriate input. These layers are then organized into a simple stack, with higher layers subsuming the goals of the lower ones. Other systems may use trees, or may include special mechanisms for changing which goal / rule subset is currently most important. Flat structures are relatively easy to build, but allow only for description of simple behavior, or require immensely complicated conditions to compensate for the lacking structure.

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