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The Hybrid Deliberative/Reactive Paradigm
to its environment. This meant that a robot could not plan optimal trajecto-
ries (path planning), make maps, monitor its own performance, or even se-
lect the best behaviors to use to accomplish a task (general planning). Notice
that not all of these functions involve planning per se; map making involves
handling uncertainty, while performance monitoring (and the implied objec-
tive of what to do about degraded performance) involves problem solving
and learning. In order to differentiate these more cognitively oriented func-
DELIBERATIVE tions from path planning, the term deliberative was coined.
The Reactive Paradigm also suffered somewhat because most people found
that designing behaviors so that the desired overall behavior would emerge
was an art, not a science. Techniques for sequencing or assembling behav-
iors to produce a system capable of achieving a series of sub-goals also relied
heavily on the designer. Couldn’t the robot be made to be smart enough to
select the necessary behaviors for a particular task and generate how they
should be sequenced over time?
Therefore, the new challenge for AI robotics at the beginning of the 1990’s
was how to put the planning, and deliberation, back into robots, but without
disrupting the success of the reactive behavioral control. The consensus was
that behavioral control was the “correct” way to do low level control, because
of its pragmatic success, and its elegance as a computational theory for both
biological and machine intelligence. As early as 1988, Ron Arkin was pub-
lishing work on how to add more cognitive functions to a behavioral system
in the form of the Autonomous Robot Architecture (AuRA). Many roboticists
looked at adding layers of higher, more cognitive functions to their behav-
ioral systems, emulating the evolution of intelligence. This chapter will cover
five examples of architectures which illustrate this bottom-up, layering ap-
proach: AuRA, Sensor Fusion Effects (SFX), 3T, Saphira, and TCA. Other robot
systems which do not strongly adhere to an architectural style, such as Rhino
and Minerva, will be discussed in later chapters.
During the 1990’s, members of the general AI community had become ex-
posed to the principles of reactive robots. The concept of considering an
intelligent system, or agent, as being situated in its environment, combined
with the existence proof that detailed, Shakey-like world representations are
not always necessary, led to a new style of planning. This change in planning
REACTIVE PLANNING was called reactive planning. Many researchers who had worked in traditional
AI became involved in robotics. One type of reactive planner for robots,
Jim Firby’s reactive-action packages (RAPs), 53 was integrated as a layer within
the 3T architecture. 21 Architectures stemming from the planning community
roots showed their traditional AI roots. They use a more top-down, hierar-