Researchers Use Bacteria as a
Model of Intelligent Behavior
Applications
Weizmann Institute of Science principal investigator Elad Schneidman
said the new approach would be
useful in areas such as collective
George Lawton
decision-making by robots or software
sraeli researchers have developed a computational machine-learning agents. For example, Ben-Jacob exmodel based on how bacteria communicate and act collectively. They say plained, the algorithm could control
small autonomous robots. A human
their algorithm could enable development of robots that could form smart
could specify a goal, such as finding
teams and also improve work in areas such as swarm computing, which an earthquake survivor, and the rostudies the collective behavior of nat- individual entities—such as robots bots could coordinate themselves and
ural or artificial decentralized, self- or software agents—that have lim- adapt their behavior to accomplish it.
ited computing, memory, and sensory
According to Shklarsh, the new model
organized systems.
would also be appropriate for applicaThe scientists built their model on capabilities.
Like bacteria, elements using the tions such as robots using information
the discovery by Tel Aviv University
from sensors to navigate complex terrain.
professor Eshel Ben-Jacob that bac- new computer model would
And, added Ben-Jacob, the algoteria have developed communication
and navigation tactics for finding food • decrease their influence on one an- rithm could be useful for building
other when they are acting success- robot systems that often must act
and avoiding harm that are superior to
fully to continue the current course autonomously because of communithose used by other swarming organof action, and
cations constraints with human conisms such as fish or amoebas.
• increase their mutual influence— trollers, as can occur on battlefields,
and thus their collective intelli- in space, or in hazardous repair situThe New Model
gence—when they are not mov- ations such as those in nuclear plants.
Bacteria demonstrate collective behaving effectively toward their desired
Also, by using groups of simple eleior in complex decision-making areas
goal.
ments, the model could help replace
such as finding food, avoiding harm,
existing robotic controlor arranging themselves
lers’ complex algorithms,
physically for moving
which consume a great deal
in groups to accomplish
of computing and memory
tasks (see Figure 2).
resources.
Bacteria’s approach is
If robotic controllers or
robust when dealing with
other systems using the
changes in their internal
computing model work with
state, sensory readings,
agents that are sufficiently
or environment, noted
inexpensive and small,
Tel Aviv University PhD
Shklarsh said, they could be
student Adi Shklarsh.
more affordable and compuMoreover, bacteria in a
tationally efficient than exswarm correct for the
isting algorithms for a numistakes of one memmerous applications.
ber, avoiding the probShklarsh said the most imlems that occur with
portant insight from her resome animals that consearch is the idea that known
tinue following a leader
adaptive biological mecharegardless.
nisms that rely only on simple
The scientists’ bacteria- Figure 2. Simulated interacting agents collectively navigate
towards a target. The agents act according to a computational
based computational model model developed by Israeli researchers based on the communication computational and memory
elements could greatly help
would enable the effec- and navigation tactics of bacteria. (Source: American Friends of
collective problem solving.
tive collective action of Tel Aviv University.)
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March/April 2012
www.computer.org/intelligent
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