Download Robot-to-Robot Nurturing: A Call to the

Robot-to-Robot Nurturing:
A Call to the Research Community
Mark Woehrer
Dean F. Hougen
Ingo Schlupp
Brent E. Eskridge
School of Computer Science
University of Oklahoma
Norman, OK 73019-1101
Email: [email protected]
School of Computer Science
University of Oklahoma
Norman, OK 73019-1101
Email: [email protected]
Department of Biology
University of Oklahoma
Norman, OK 73019-6121
Email: [email protected]
CSNE Department
Southern Nazarene University
Bethany, OK 73008-2605
Email: [email protected]
Machine
Behaves
Intelligently*
I. I NTRODUCTION
Nurturing is prevalent in the biological world and various
aspects of nurturing are extensively studied in biology, cognitive science, psychology, child development, sociology, and
education, among other disciplines. We believe that nurturing should likewise be considered a fundamental concept in
robotics and artificial life, with an active research community
built around it for both scientific and practical reasons.
In terms of fundamental science, there is much to be learned
about how and why nurturing evolves in the natural world, how
it could be evolved in the artificial realm, and how it is related to other concepts, such as learning, communication, and
emotion, in both natural and artificial systems. In particular,
there appears to be a virtuous cycle involving the evolution of
nurturing and the evolution of learning.
A virtuous cycle is a positive (self-reinforcing) feedback
loop with desirable outcomes. Here, those outcomes are the
evolution of nurturing and the evolution of learning. Learning
provides opportunities not available with fixed action patterns
but at a greater initial cost/investment to the learner. This can
be expressed in terms of the trade-off between exploration and
exploitation. An exploratory strategy, which is necessary for
learning, may eventually lead to high rewards once the environment is well understood yet is likely to lead to lower shortterm rewards than an exploitative strategy that maximizes
(instinctively) known rewards. Nurturing can cover the initial
costs of the learner and the resulting benefits of learning can
be paid forward to the next generation. In this way, nurturing
promotes the evolution of learning [1]. In turn, an organism
that is capable of learning may apply that faculty to learn better
nurturing skills. In this way, the evolution of learning leads to
greater nurturing, bringing the cycle back around for another
* In the Evolution of Nurturing & ML approach,
intelligent behavior includes intelligent nurturing.
†In the Evolution of Nurturing & ML approach,
learned behavior includes learned nurturing.
Machine Intelligence Achieved
Abstract—Nurturing behaviors comprise a fundamental class
of actions that can occur between individuals. They are vital
components of the behavioral repertoires of numerous biological
organisms and are objects of study in numerous disciplines.
In this call to the community, we consider what nurturing
means for biological and artificial systems, how robot-to-robot
(R2R) nurturing is related to developmental robotics including
epigenetic and morphogenetic robotics, why R2R nurturing
should be considered an important topic, and why we advocate
using an evolutionary framework to develop R2R nurturing. We
also suggest next steps for the research community.
Machine
Learns
Behavior†
Machine
Evolves
Learning
Machine
Evolves
Nurturing
Machine
Behaves
Intelligently*
Machine
Behaves
Intelligently
Machine
Learns
Behavior†
Machine
Behaves
Intelligently
Machine
Learns
Behavior
Machine
Evolves
Learning
Machine
Behaves
Intelligently
Machine
Learns
Behavior
Machine
Evolves
Learning
Machine
Evolves
Nurturing
People Code
Intelligent
Behaviors
People Code
Machine
Learning
People Code
Evolutionary
Rules
People Create
Evolutionary
Environment
Hard-Coded AI
Hard-Coded ML
Evolved ML
Evolution of
Nurturing & ML
Approach
Fig. 1. Because the evolution of robot-to-robot nurturing and the evolution
of learning involves a virtuous cycle, it builds on itself, leading to greater
nurturing and learning, and therefore greater machine intelligence.
turn. Given these relationships, understanding this cycle will
transform our understanding of biological intelligence and
nurturing and starting this cycle will radically transform the
field of robot intelligence, as illustrated in Fig. 1.
In terms of practical applications, robots with the capacity to
nurture one another will make extensive robot learning feasible
by providing supervision (in the everyday sense) to learning
robots and will greatly inform our efforts to create robots
capable of nurturing people or animals (e.g., robots for health
care or animal husbandry).
For all of these reasons, we call on the community to
embrace the study of robot-to-robot nurturing.
II. W HAT IS ROBOT- TO -ROBOT (R2R) N URTURING ?
We define nurturing as the contribution of time, energy, or
other resources by one individual to the expected physical,
mental, social, or other development of another individual
with which it has an ongoing relationship.1 Robot-to-robot
nurturing, then, is nurturing in which both the nurturer and
the individual being nurtured are robots.
R2R nurturing is related to developmental robotics [2] insofar as both are focused on robot development, particularly development of mental capacities for epigenetic robotics [3]. and
physical capacities for morphogenetic robotics [4]. However,
while epigenetic robotics research often stresses the social as
well as the physical environment in which development takes
place (e.g., [3]), research on developmental robotics does not
tend to focus on resource contributions from one individual to
another; indeed, developmental robotics typically stresses the
autonomous nature of the development (e.g., [5]). Nonetheless,
nurturing has occasionally been mentioned by developmental
roboticists as important to human development (e.g., [2]) and
some experiments involving robots learning from robots would
be considered to involve R2R nurturing if the relationship
between the robots is ongoing and the robot being learned
from is actively teaching (e.g., [6]; note that in this works the
nurturing was hard coded, rather than evolved). This suggests
that some R2R nurturing research could be seen as belonging
within developmental robotics.
We advocate an evolutionary approach to R2R nurturing,
which is similar to evolutionary methods for developmental
multi-robot systems (e.g., [7]) although we have not yet
found research on the evolution of R2R nurturing within
developmental robotics despite an extensive search.
We wish to stress here that what we are discussing is not
how social interactions facilitate individual learning—this is
an important and related topic but is quite distinct from the
question we are considering here. Rather, we are describing
how the evolution of particular kinds of social interactions
facilitate the evolution of individual learning. The evolutionary
component of this approach should not be overlooked.
Examples of R2R nurturing are found elsewhere in this
volume [1], [8].
III. W HAT S TEPS S HOULD THE C OMMUNITY TAKE ?
We have identified an important yet under-explored area of
research in artificial life, robotics, and multi-agent systems and
described its relationship to allied research areas. We conclude
by calling on the community to work with us to establish R2R
nurturing as an active research field and by setting forth four
concrete steps toward that goal.
First, recognizing that this field will be truly
transdisciplinary—creating a shared conceptual model
of the problem domain that integrates and transcends each of
the many disciplinary perspectives involved—one issue that
needs to be addressed is communication. We have attempted
to take a partial step in fostering communication by rigorously
defining terms such as nurturing in a cross-disciplinary way,
relating them to terms from biology and robotics, and
transmitting our ideas to the transdisciplinary development
1 While various aspects of nurturing are studied in various disciplines under
their own domain-specific terms, there is no cross-disciplinary term that
captures this concept—hence our need to rigorously define this term.
and learning community. However, these terms still need to
be related to terms from the many other disciplines involved
as well as operationalized for particular studies.
A second foundational step that needs to happen is to begin
to identify and catalog various possible nurturing types and
their relationships to one another. We do not imagine a single
cladogram as has been proposed for learning [9]—it is likely
that some nurturing types evolved independently of others—
but we do admire the spirit of that effort.
Thirdly, the community needs to establish metrics for nurturing. In biology, these would be based on factors including
energy gain, transfer, and loss; changes in reproductive success; and inclusive fitness. Other disciplines would necessarily
bring other factors into consideration.
Fourth, the community needs to identify fundamental questions regarding the relationship between environment and
the evolution of nurturing and begin studies based on these
questions. Again, we have attempted to take a partial step in
that direction by including some fundamental questions in this
document and by discussing two example studies.
IV. ACKNOWLEDGMENTS
This research was funded by the Potentially Transformative
Research, Scholarship and Creative Activity Program, Office
of the Vice President for Research and The Research Council,
University of Oklahoma.
R EFERENCES
[1] B. E. Eskridge and D. F. Hougen, “Nurturing promotes the evolution of
learning in uncertain environments,” in IEEE International Conference
on Development and Learning / EpiRob, 2012, accepted, this volume.
[2] M. Asada, K. Hosoda, Y. Kuniyoshi, H. Ishiguro, T. Inui, Y. Yoshikawa,
M. Ogino, and C. Yoshida, “Cognitive developmental robotics: A survey,”
Autonomous Mental Development, IEEE Transactions on, vol. 1, no. 1,
pp. 12–34, 2009.
[3] J. Zlatev and C. Balkenius, “Introduction: Why ”epigenetic robotics”?” in
Proceedings of the First International Workshop on Epigenetic Robotics:
Modeling Cognitive Development in Robotic Systems, ser. Lund University Cognitive Studies, C. Balkenius, J. Zlatev, H. Kozima, K. Dautenhahn, and C. Breazeal, Eds., vol. 85. Lund University Cognitive Studies,
2001, pp. 1–4.
[4] Y. Jin and Y. Meng, “Morphogenetic robotics: An emerging new field
in developmental robotics,” Systems, Man, and Cybernetics, Part C:
Applications and Reviews, IEEE Transactions on, vol. 41, no. 2, pp. 145
–160, march 2011.
[5] J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur,
and E. Thelen, “Autonomous mental development by robots and animals,”
Science, vol. 291, no. 5504, pp. 599–600, 2000.
[6] A. Billard and G. Hayes, “Drama, a connectionist architecture for control
and learning in autonomous robots,” Adaptive Behavior, vol. 7, no. 1,
pp. 35–63, Jan. 1999. [Online]. Available: http://cogprints.org/535/
[7] D. Floreano, S. Mitri, S. Magnenat, and L. Keller, “Evolutionary
conditions for the emergence of communication in robots,” Current
Biology, vol. 17, no. 6, pp. 514 – 519, 2007. [Online]. Available:
http://www.sciencedirect.com/science/article/pii/S0960982207009281
[8] A. Leonce, B. Hoke, and D. F. Hougen, “Evolution of robot-to-robot
nurturing and nurturability,” in IEEE International Conference on Development and Learning / Epigenetic Robotics, 2012, accepted, this volume.
[9] B. R. Moore, “The evolution of learning,” Biological Revue, vol. 79, pp.
301–335, 2004.