Page 312 - Introduction to AI Robotics
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8.1 Overview
It is not clear when communication is needed between agents, and what to say.
Many animals operate in flocks, maintaining formation without explicit
communication (e.g., songs in birds, signals like a deer raising its tail to
display white, speaking). Formation control is often done simply by per-
ceiving the proximity to or actions of other agents; for example, school-
ing fish try to remain equally close to fish on either side. But robots and
modern telecommunications technology make it possible for all agents
in a team to literally know whatever is in the mind of the other robots,
though at a computational and hardware cost. How can this unparalleled
ability be exploited? What happens if the telecommunications link goes
bad? Cell phones aren’t 100% reliable, even though there is tremendous
consumer pressure on cell phones, so it is safe to assume that robot com-
munications will be less reliable. Is there a language for multi-agents that
can abstract the important information and minimize explicit communi-
cation?
The “right” level of individuality and autonomy is usually not obvious in a prob-
lem domain. Agents with a high degree of individual autonomy may cre-
ate more interference with the group goals, even to the point of seeming
“autistic.” 113 But agents with more autonomy may be better able to deal
with the open world.
The first question in the above list essentially asks what are the architectures
for multi-agents? The answer to that question at this time is unclear. Individ-
ual members of multi-agent teams are usually programmed with behaviors,
following either the Reactive (Ch. 4) or Hybrid Deliberative/Reactive (Ch. 7)
paradigms. Recall that under the Reactive Paradigm, the multiple behaviors
acting concurrently in a robot led to an emergent behavior. For example, a ro-
bot might respond to a set of obstacles in a way not explicitly programmed
in. Likewise in multi-agents, the concurrent but independent actions of each
EMERGENT SOCIAL robot leads to an emergent social behavior. The group behavior can be different
BEHAVIOR from the individual behavior, emulating “group dynamics” or possibly “mob
psychology.” As will be seen in this chapter, fairly complex team actions such
as flocking or forming a line to go through a door emerge naturally from re-
active robots with little or no communication between each other. But as
with emergent behavior in individual robots, emergent social behavior is of-
ten hard to predict. Complete architectures for designing teams of robots are
still under development; Lynne Parker’s ALLIANCE architecture 114 is possi-
bly the most comprehensive system to date. The whole field of multi-agents
is so new that there is no consensus on what are the important dimensions,