agents evolve purpose
Technology Research News
Behavior has always been a touchy subject,
especially when academics delve into just how much of what we do is conscious
choice and how much of it is instinct born of evolution.
Researchers from the Keldysh Institute of Applied Mathematics in Russia
have shown that purposeful behavior, or motivation can emerge naturally
in a software simulation that has simple software beings, or agents, evolving
over many generations.
The researchers' simulation showed that a system that uses motivations
to control simple reflexes can emerge in an evolutionary process, said
Mikhail Burtsev, a researcher at the M. V. Keldysh Institute for Applied
Mathematics at the Russian Academy of Science in Russia.
Having motivation was an advantage likely to be passed on to subsequent
generations of the agents, he said. "The population of agents with
motivations had obvious selective advantages compared with the population
of agents without motivations," he said.
The research is ultimately pointed toward better understanding cognition
in order to develop more realistic agents, Burtsev said.
The simulation provided the software agents with a simple world of 900
discrete pens where patches of grass grow. The agents lived in the pens
and ate the grass.
With the energy they gained from the grass, the agents could do the following
things: move to a neighboring cell on the left or right or jump over several
cells in a random direction, eat, and mate. The agents could also choose
to rest, saving energy.
When an agent ran out of energy, however, it died. Mating sometimes resulted
in the birth of new agents.
The actions of each agent were determined by it's neural
network, a set of logical neurons with connections to other neurons.
The strength of these connections determined which action an agent took
in a given situation. As a population of agents evolved, the strengths
of the neural connections changed, modifying the behavior of the agents,
The agents had several initial instincts: if an agent saw grass in its
own cell it ate the grass, if an agent saw grass in a neighboring cell
it moved into that cell, and if an agent saw another agent in one of the
neighboring cells it tried to mate with its neighbor. If things got crowded
and an agent saw two agents in both neighboring cells, it jumped to a
The researchers populated the pen world with 200 identical agents. During
the simulations, which took the agents through several thousand generations,
their number varied from several dozen to as many as 800.
The agent population as a whole had one goal -- survival. This goal required
individuals to push toward two basic subgoals -- to replenish energy,
and to reproduce, said Burtsev. The agents evolved to seek out grass and
"The most important thing here is that we didn't force agents to follow
these needs. The needs were prescribed explicitly by [the] environment,
and only agents that had these two needs could successfully undergo selection
pressure," said Burtsev.
The researchers concluded that the environment was the cause of the motivation
-- at least in this case. "In the context of our model we can say that
purposeful behavior is behavior directed to get those subgoals," he said.
"These goals may seem trivial, but they're important features of all living
creatures," he said.
Because the agents evolved this type of system, it must be more effective
than behavior governed only by simple reflexes, Burtsev added.
The researchers also found that the agents evolved more intelligent behavior
as time went on. Initially, the main action of the agents was eating.
Later generations rested more, however. "This means that the agents became
able to do only the right actions at the right time. We didn't foresee
such intelligent strategy, and this was surprising," Burtsev said.
The work combines and improves several other artificial life approaches,
and shows positive results regarding the usefulness of motivation, said
Marcus Hutter, a researcher at the Research Institute for Artificial Intelligence
However, the researchers' conclusions about motivation are tenuous, he
said. "I think the motivated system performs better just because its neural
net has access to additional useful information. It is useful to verify
that this works, but the interpretation of the authors [that this is due
to] additional motivation inputs is not convincing," he said.
The researchers plan next to create a more complex model that would force
the agents to develop a hierarchy of goals, said Burtsev.
The model could be adapted to provide agents that could be used on the
Web within five to seven years, Burtsev said.
Burtsev's research colleagues were Vladimir G Red'ko and Roman V. Gusarev
of the Keldysh Institute of Applied Mathematics. The research was funded
by the Russian government.
Timeline: 2-3 years, 5-7 years
TRN Categories: Artificial Life; Evolutionary Computing;
Story Type: News
Related Elements: Technical paper, "A Life Model of Evolutionary
Emergence of Purposeful Adaptive Behavior," posted on CORR at http://xxx.lanl.gov/abs/cs.NE/0110021.
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