Abstract: Crowdsourcing has been successfully applied in
many domains including astronomy, cryptography and biology.
In order to test its potential for useful application in a Smart Grid
context, this paper investigates the extent to which a crowd can
contribute predictive hypotheses to a model of residential electric
energy consumption. In this experiment, the crowd generated
hypotheses about factors that make one home different from
another in terms of monthly energy usage. To implement this
concept, we deployed a web-based system within which 627
residential electricity customers posed 632 questions that they
thought predictive of energy usage. While this occurred, the
same group provided 110,573 answers to these questions as they
accumulated. Thus users both suggested the hypotheses that drive
a predictive model and provided the data upon which the model
is built. We used the resulting question and answer data to
build a predictive model of monthly electric energy consumption,
using random forest regression. Because of the sparse nature of
the answer data, careful statistical work was needed to ensure
that these models are valid. The results indicate that the crowd
can generate useful hypotheses, despite the sparse nature of the
Social contribution in the design of adaptive machines on the web.
Mark Wagy, Joshua Bongard.
Proceedings of the 15th International Conference on the Synthesis and Simulation of Living Systems (ALife 2016), , , 2016.
Abstract: The Web has created new opportunities for interactive problem
solving and design by large groups. In the context of
robotics, we have shown recently that a crowd of non-experts
are capable of designing adaptive machines over the Web.
However, determining the degree to which collective contribution
plays a part in these tasks requires further investigation.
We hypothesize that there exist subtle yet measurable
social dynamics that occur during the collaborative design of
robots on the Web. To test this, we enabled a crowd to rapidly
design and train simulated, web-embedded robots1
. We compared
the robots designed by a socially-interacting group of
individuals to another group whose members were isolated
from one another. We found that there exists a latent quality
in the robots designed by the social group that was significantly
less prevalent in the robots designed by individuals
working alone. Thus, there must exist synergies in the former
group that facilitate this design task. We also show that this
latent quantity correlates with the desired design outcome,
which was fast forward locomotion. However, the quantity –
when distilled into its component parts – is not more prevalent
in one group than another. This finding demonstrates that
there are indeed traces left behind in the machines designed
by the crowd that betray the social dynamics that gave rise to
them. Demonstrating the existence of such quantities and the
methodology for extracting them presents opportunities for
crafting interfaces to magnify these synergies and thus improve
collective design of robots over the web in particular,
and crowd design activities in general.
Abstract: Rather than replacing human labor, there is growing evidence that networked computers create opportunities for collaborations of people and algorithms to solve problems beyond either of them. In this study, we demonstrate the conditions under which such synergy can arise. We show that, for a design task, three elements are sufficient: humans apply intuitions to the problem, algorithms automatically determine and report back on the quality of designs, and humans observe and innovate on others’ designs to focus creative and computational effort on good designs. This study suggests how such collaborations should be composed for other domains, as well as how social and computational dynamics mutually influence one another during collaborative problem solving.
Abstract: Crowdsourcing is a popular technique for distributing tasks to a group of anonymous workers over the web. Similarly, crowdseeding is any mechanism that extracts knowledge from the crowd, and then uses that knowledge to guide an automated process. Here we demonstrate a method that automatically distills features from a set of robot body plans designed by the crowd, and then uses those features to guide the automated design of robot body plans and controllers. This approach outperforms past work in which one feature was detected and distilled manually. This provides evidence that the crowd collectively possesses intuitions about the biomechanical advantages of certain body plans; we hypothesize that these intuitions derive from their experiences with biological organisms.
Abstract: Crowdsourcing is a well-known method in which intelligence tasks are completed by an anonymous group of human participants. These are tasks that cannot yet be adequately performed by computers. Rather than performing an intelligence task outright, one crowdsourcing strategy is to use human intelligence to complement machine intelligence. A key point in determining the potential of such a strategy is understanding the ways that human abilities most effectively complement the strengths of machine intelligence. We shed light on this relationship by 'crowdseeding' robot design: we find morphological features common to human-generated robot designs and incorporate them as an additional fitness objective in an evolutionary algorithm that searches over the same space of designs. We demonstrate that this approach outperforms the same evolutionary algorithm that is not crowdseeded in this way.
Abstract: It has been shown that the collective action of non-experts
can compete favorably with an individual expert or an optimization
method on a given problem. However, the best
method for organizing collective problem solving is still an
open question. Using the domain of robotics, we examine
whether cooperative search for design strategies is superior
to individual search. We use a web-based robot simulation to
determine whether groups of human users can leverage design
intuition from others to focus search on relevant parts of
a complex design space. We show that individuals that work
cooperatively with the aid of a simple optimization algorithm
are better able to improve the design of robot locomotion than
if they were to work individually with the aid of the optimization
algorithm. This result suggests that groups of designers
may more effectively work in tandem with optimization algorithms
than individuals working in isolation.