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Reactive Plans and Three-Layer Architectures

At roughly the same time as BBAI was emerging, so were reactive plans (16,20). Reactive plans are powerful plan representations that provide for robust execution. A single plan will work under many different contingencies given a sufficiently amenable context. An agent can store a number of such plans in a library, then use context-based preconditions to select one plan that should meet its current goals in the current environment. The alternative -- constructing a plan on demand -- is a form of search and consequently costly (12). In keeping with the goal of all reactive intelligence, reactive plans provide a way to avoid search during real-time execution.

A hybrid behavior-based system takes advantage of behaviors to give a planning system very powerful primitives. This in turn allows the plan to be relatively high-level and simple, a benefit to conventional planners as well as reactive plans (28). Most three-layer hybrid architectures (as described above) have a bottom layer of behaviors, which serve as primitives to a second layer of reactive plans. They may then optionally has a third `deliberative' (searching) layer either to create new plans or to choose between existing ones.

Consider this description of the ontology underlying three-layered architectures:

The three-layer architecture arises from the empirical observation that effective algorithms for controlling mobile robots tend to fall into three distinct categories:
  1. reactive control algorithms which map sensors directly onto actuators with little or no internal state;
  2. algorithms for governing routine sequences of activity which rely extensively on internal state but perform no search; and
  3. time-consuming (relative to the rate of change of the environment) search-based algorithms such as planners.

Gat (19, p. 209)

In this description, behaviors are the simple stateless algorithms and reactive plans serve as state- or context-keeping devices for ordering the activity of the behaviors. In Gat's own architecture, ATLANTIS, (18) the second, reactive-plan layer dominates the agent: it monitors the agent's goals and selects its actions. If the second layer becomes stuck or uncertain, it can reduce the agent's activity while consulting a third-level planner, while still monitoring the environment for indications of newer, more urgent goals.


next up previous
Next: Behavior-Oriented Design (BOD) Up: The Previous State of Previous: Behavior-Based Artificial Intelligence (BBAI)
Joanna J. Bryson 2005-07-08