University of Chicago Artificial Intelligence Lab

The RAP System


A robot acting in the real world must use flexible plans. Actions will sometimes fail to produce their desired effects and unexpected events will sometimes demand the robot shift its attention from one task to another. A plan is usually construed as a list of primitive robot actions to be executed one after another but in a complex domain a plan must be structured to cope effectively with the myriad unpredictable details it will encounter during execution. However, adding structure to a plan involves more than augmenting the primitive plan representation; it requires adopting a situation-driven model of interaction with the world. Situation-driven execution assumes that a plan consists of tasks with three major components: a satisfaction test, a window of activity, and a set of execution methods that are appropriate in different circumstances. Execution of such a plan proceeds by selecting an unsatisfied task and choosing a method to achieve it based on the current world state. A task may be executed as many times as necessary to keep it satisfied while it is active.

The RAP system proposes a plan and task representation based on program-like reactive action packages, or RAPs. A plan consists of RAP-defined goals, or tasks, at a variety of different levels of abstraction and the RAP system attempts to carry out each task in turn using different methods in different situations and dealing with common problems and simple interruptions. Within the system, execution monitoring becomes an intrinsic part of the execution algorithm, and the need for separate replanning on failure disappears. RAPs are more than just programs that run at execution time, however, they are also hierarchical building blocks for plan construction. The RAP representation is structured to make a task's expected behavior evident for use in planning as well as in execution. The RAP execution system described includes a sensor memory, representation language and interpreter. Examples and experiments demonstrate a wide range of adaptive system behavior.

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CS Home AI Home AI Projects Chip Firby
The RAP System / R. James Firby / firby@cs.uchicago.edu
March, 1995