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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.