5,222 research outputs found
Evolution of Swarm Robotics Systems with Novelty Search
Novelty search is a recent artificial evolution technique that challenges
traditional evolutionary approaches. In novelty search, solutions are rewarded
based on their novelty, rather than their quality with respect to a predefined
objective. The lack of a predefined objective precludes premature convergence
caused by a deceptive fitness function. In this paper, we apply novelty search
combined with NEAT to the evolution of neural controllers for homogeneous
swarms of robots. Our empirical study is conducted in simulation, and we use a
common swarm robotics task - aggregation, and a more challenging task - sharing
of an energy recharging station. Our results show that novelty search is
unaffected by deception, is notably effective in bootstrapping the evolution,
can find solutions with lower complexity than fitness-based evolution, and can
find a broad diversity of solutions for the same task. Even in non-deceptive
setups, novelty search achieves solution qualities similar to those obtained in
traditional fitness-based evolution. Our study also encompasses variants of
novelty search that work in concert with fitness-based evolution to combine the
exploratory character of novelty search with the exploitatory character of
objective-based evolution. We show that these variants can further improve the
performance of novelty search. Overall, our study shows that novelty search is
a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final
publication will be available at link.springer.co
Cooperation and Contagion in Web-Based, Networked Public Goods Experiments
A longstanding idea in the literature on human cooperation is that
cooperation should be reinforced when conditional cooperators are more likely
to interact. In the context of social networks, this idea implies that
cooperation should fare better in highly clustered networks such as cliques
than in networks with low clustering such as random networks. To test this
hypothesis, we conducted a series of web-based experiments, in which 24
individuals played a local public goods game arranged on one of five network
topologies that varied between disconnected cliques and a random regular graph.
In contrast with previous theoretical work, we found that network topology had
no significant effect on average contributions. This result implies either that
individuals are not conditional cooperators, or else that cooperation does not
benefit from positive reinforcement between connected neighbors. We then tested
both of these possibilities in two subsequent series of experiments in which
artificial seed players were introduced, making either full or zero
contributions. First, we found that although players did generally behave like
conditional cooperators, they were as likely to decrease their contributions in
response to low contributing neighbors as they were to increase their
contributions in response to high contributing neighbors. Second, we found that
positive effects of cooperation were contagious only to direct neighbors in the
network. In total we report on 113 human subjects experiments, highlighting the
speed, flexibility, and cost-effectiveness of web-based experiments over those
conducted in physical labs
Exploring Evolved Multicellular Life Histories in a Open-Ended Digital Evolution System
Evolutionary transitions occur when previously-independent replicating
entities unite to form more complex individuals. Such transitions have
profoundly shaped natural evolutionary history and occur in two forms:
fraternal transitions involve lower-level entities that are kin (e.g.,
transitions to multicellularity or to eusocial colonies), while egalitarian
transitions involve unrelated individuals (e.g., the origins of mitochondria).
The necessary conditions and evolutionary mechanisms for these transitions to
arise continue to be fruitful targets of scientific interest. Here, we examine
a range of fraternal transitions in populations of open-ended self-replicating
computer programs. These digital cells were allowed to form and replicate kin
groups by selectively adjoining or expelling daughter cells. The capability to
recognize kin-group membership enabled preferential communication and
cooperation between cells. We repeatedly observed group-level traits that are
characteristic of a fraternal transition. These included reproductive division
of labor, resource sharing within kin groups, resource investment in offspring
groups, asymmetrical behaviors mediated by messaging, morphological patterning,
and adaptive apoptosis. We report eight case studies from replicates where
transitions occurred and explore the diverse range of adaptive evolved
multicellular strategies
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