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The Hybrid Deliberative/Reactive Paradigm
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agents. The dominant agent is called the Mission Planner agent. This agent
serves to interact with the human and specify the mission constraints to the
other agents in the deliberative layer. The agents in the deliberative layer
attempt to find (and maintain) a set of behaviors which can accomplish the
mission while meeting the constraints. The software agents in each deliber-
ative layer are peers; just as with behaviors, they operate independently of
each other. But the nature of deliberation suggests that they have to negoti-
ate with other peers to find a satisfactory set of behaviors to accomplish the
current task. One way to think of this partitioning is that the Mission Plan-
ner acts as a president or CEO in a large company, giving directions, while
the behaviors are the workers. The agents in the lower deliberative layer are
middle-management, planning how to organize the workers’ assignments,
monitoring productivity, and adapting assignments if necessary.
Within the deliberative layer, the Task Manager, Sensing Manager, and Ef-
fector Manager serve as the resource managers. The resource manager func-
tions are divided across managers because the types of knowledge and algo-
rithms are different for managing sensors and actions. The managers use AI
planning, scheduling, and problem solving techniques to determine the best
allocation of effector and sensing resources given the set of motor and per-
ceptual schemas for a behavior. They are not allowed to relax any constraints
specified by the Mission Planner, so they essentially know what the robot is
supposed to do, but only the Mission Planner knows why. The advantage of
this middle-management layer is that it simplifies the AI techniques needed
for behavioral management.
The Sensing Manager in SFX is particularly noteworthy because of its ex-
plicit commitment to performance monitoring and problem solving. It has
two software agents for monitoring both the task performance and whether
the habitat has changed (if so, a performance failure is likely to occur). If
a behavior fails or a perceptual schema detects that sensor values are not
consistent or reasonable, the Sensing Manager is alerted. It can then identify
alternative perceptual schemas, or even behaviors, to replace the problematic
behavior immediately. Imagine a mobile robot in a convoy of robots hauling
food to refugees. If the robot had a glitch in a sensor, it shouldn’t suddenly
just stop and think about the problem. Instead, it should immediately switch
to a back-up plan or even begin to smoothly slow down while it identifies
a back-up plan. Otherwise, the whole convoy would stop, there might be
wrecks, etc. Then working in a background mode, the Sensing Manager can
attempt to diagnose the cause of the problem and correct it. In one demon-
stration, a robot using SFX resorted to shaking its camera to shake off a T-