@inproceedings {9998, title = {Modelling the Social Buffering Hypothesis in an Artificial Life Environment}, booktitle = {Proceedings of the Artificial Life Conference 2020 (ALIFE 2020)}, year = {2020}, note = {Download (Open Access)}, month = {07/2020}, pages = {393{\textendash}401}, publisher = {MIT Press}, organization = {MIT Press}, address = {Montreal, Canada}, abstract = {In social species, individuals who form social bonds have been found to live longer, healthier lives. One hypothesised reason for this effect is that social support, mediated by oxytocin, "buffers" responses to stress in a number of ways, and is considered an important process of adaptation that facilitates long-term wellbeing in changing, stressful conditions. Using an artificial life model, we have investigated the role of one hypothesised stress-reducing effect of social support on the survival and social interactions of agents in a small society. We have investigated this effect using different types of social bonds and bond partner combinations across environmentally-challenging conditions. Our results have found that stress reduction through social support benefits the survival of agents with social bonds, and that this effect often extends to the wider society. We have also found that this effect is significantly affected by environmental and social contexts. Our findings suggest that these "social buffering" effects may not be universal, but dependent upon the degree of environmental challenges, the quality of affective relationships and the wider social context.}, doi = {10.1162/isal_a_00302}, url = {https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00302}, author = {Imran Khan and Lewis, Matthew and Lola Ca{\~n}amero}, editor = {Josh Bongard and Juniper Lovato and Laurent Hebert-Dufr{\'e}sne and Radhakrishna Dasari and Lisa Soros} } @article {2018, title = {Modelling Adaptation through Social Allostasis: Modulating the Effects of Social Touch with Oxytocin in Embodied Agents}, journal = {Multimodal Technologies and Interaction}, volume = {2}, year = {2018}, note = {Download (Open Access)}, publisher = {MDPI}, chapter = {67}, address = {Basel, Switzerland}, abstract = {Social allostasis is a mechanism of adaptation that permits individuals to dynamically adapt their physiology to changing physical and social conditions. Oxytocin (OT) is widely considered to be one of the hormones that drives and adapts social behaviours. While its precise effects remain unclear, two areas where OT may promote adaptation are by affecting social salience, and affecting internal responses of performing social behaviours. Working towards a model of dynamic adaptation through social allostasis in simulated embodied agents, and extending our previous work studying OT-inspired modulation of social salience, we present a model and experiments that investigate the effects and adaptive value of allostatic processes based on hormonal (OT) modulation of affective elements of a social behaviour. In particular, we investigate and test the effects and adaptive value of modulating the degree of satisfaction of tactile contact in a social motivation context in a small simulated agent society across different environmental challenges (related to availability of food) and effects of OT modulation of social salience as a motivational incentive. Our results show that the effects of these modulatory mechanisms have different (positive or negative) adaptive value across different groups and under different environmental circumstance in a way that supports the context-dependent nature of OT, put forward by the interactionist approach to OT modulation in biological agents. In terms of simulation models, this means that OT modulation of the mechanisms that we have described should be context-dependent in order to maximise viability of our socially adaptive agents, illustrating the relevance of social allostasis mechanisms.}, issn = {2414-4088}, doi = {10.3390/mti2040067}, url = {https://www.mdpi.com/2414-4088/2/4/67}, author = {Imran Khan and Lola Ca{\~n}amero} } @article {2016, title = {Making New "New AI" Friends: Designing a Social Robot for Diabetic Children from an Embodied AI Perspective}, journal = {International Journal of Social Robotics}, volume = {8}, year = {2016}, note = {Download (Open Access)}, pages = {523{\textendash}537}, publisher = {Springer}, abstract = {Robin is a cognitively and motivationally autonomous affective robot toddler with "robot diabetes" that we have developed to support perceived self-efficacy and emotional wellbeing in children with diabetes. Robin provides children with positive mastery experiences of diabetes management in a playful but realistic and natural interaction context. Underlying the design of Robin is an "Embodied" (formerly also known as "New") Artificial Intelligence (AI) approach to robotics. In this paper we discuss the rationale behind the design of Robin to meet the needs of our intended end users (both children and medical staff), and how "New AI" provides a suitable approach to developing a friendly companion that fulfills the therapeutic and affective requirements of our end users beyond other approaches commonly used in assistive robotics and child{\textendash}robot interaction. Finally, we discuss how our approach permitted our robot to interact with and provide suitable experiences of diabetes management to children with very different social interaction styles.}, issn = {1875-4805}, doi = {10.1007/s12369-016-0364-9}, url = {https://link.springer.com/article/10.1007\%2Fs12369-016-0364-9}, author = {Lola Ca{\~n}amero and Lewis, Matthew} } @inproceedings {2014, title = {Modulating Perception with Pleasure for Action Selection}, booktitle = {Proc. 5th Annual International Conference on Biologically-Inspired Cognitive Architectures (BICA 2014)}, year = {2014}, note = {
}, month = {11/2014}, address = {Cambridge, MA}, abstract = {Persistence and opportunism are two key features of cognitive action selection architectures. For an autonomous robot that has to satisfy multiple conflicting survival-related needs, it is crucial to persist in the execution of behaviors for long enough to get sufficient benefit. Persistence is important to avoid what is known as the "dithering" problem, which occurs when a robot keeps switching between trying to satisfy two needs without satisfying either of them enough to guarantee survival. Opportunism concerns the initiation of actions, and occurs when an agent chooses to consume a resource that might not satisfy its most pressing need, but which is available now and might not be available later. The degree to which a robot should show persistence and opportunism depends on multiple factors; we could generally say that persistence leads to a more "conservative" action selection behavior and opportunism to a more "risky" one.}, author = {Lewis, Matthew and Lola Ca{\~n}amero} } @article {2012, title = {Multimodal Child-Robot Interaction: Building Social Bonds}, journal = {Journal of Human-Robot Interaction}, volume = {1}, year = {2012}, note = {Download (Open Access)}, pages = {33{\textendash}53}, abstract = {For robots to interact effectively with human users they must be capable of coordinated, timely behavior in response to social context. The Adaptive Strategies for Sustainable Long-Term Social Interaction (ALIZ-E) project focuses on the design of long-term, adaptive social interaction between robots and child users in real-world settings. In this paper, we report on the iterative approach taken to scientific and technical developments toward this goal: advancing individual technical competencies and integrating them to form an autonomous robotic system for evaluation {\textquotedblleft}in the wild.{\textquotedblright} The first evaluation iterations have shown the potential of this methodology in terms of adaptation of the robot to the interactant and the resulting influences on engagement. This sets the foundation for an ongoing research program that seeks to develop technologies for social robot companions.}, doi = {10.5898/JHRI.1.2.Belpaeme}, url = {https://dl.acm.org/doi/10.5555/3109688.3109691}, author = {Tony Belpaeme and Paul E. Baxter and Robin Read and Rachel Wood and Cuay{\'a}huitl, Heriberto and Kiefer, Bernd and Racioppa, Stefania and Kruijff-Korbayov{\'a}, Ivana and Athanasopoulos, Georgios and Valentin Enescu and Rosemarijn Looije and Mark A. Neerincx and Yiannis Demiris and Raquel Ros-Espinoza and Aryel Beck and Lola Ca{\~n}amero and Lewis, Matthew and Baroni, Ilaria and Nalin, Marco and Cosi, Piero and Giulio Paci and Tesser, Fabio and Sommavilla, Giacomo and Remi Humbert} } @inproceedings {2006, title = {Modulation of Exploratory Behavior for Adaptation to the Context}, booktitle = {Proc. AISB 2006 Symposium on Biologically Inspired Robotics (Biro-net)}, year = {2006}, pages = {131{\textendash}137}, publisher = {AISB Press}, organization = {AISB Press}, address = {Bristol, UK}, abstract = {For autonomous agents (children, animals or robots), exploratory learning is essential as it allows them to take advantage of their past experiences in order to improve their reactions in any situation similar to a situation already experimented. We have already exposed in Blanchard and Canamero (2005) how a robot can learn which situations it should memorize and try to reach, but we expose here architectures allowing the robot to take initiatives and explore new situations by itself. However, exploring is a risky behavior and we propose to moderate this behavior using novelty and context based on observations of animals behaviors. After having implemented and tested these architectures, we present a very interesting emergent behavior which is low-level imitation modulated by context.}, url = {https://uhra.herts.ac.uk/handle/2299/9888}, author = {Arnaud J Blanchard and Lola Ca{\~n}amero}, editor = {J Burn and M Wilson} } @inproceedings {2005, title = {Motivation Driven Learning of Action Affordances}, booktitle = {Proceedings of the Symposium on Agents that Want and Like: Motivational and Emotional Roots of Cognition and Action (SSAISB{\textquoteright}05)}, year = {2005}, note = {Download symposium proceedings (pdf)}, pages = {33{\textendash}36}, publisher = {AISB}, organization = {AISB}, address = {Hatfield, UK}, abstract = {Survival in the animal realm often depends on the ability to elucidate the potentialities for action offered by every situation. This paper argues that affordance learning is a powerful ability for adaptive, embodied, situated agents, and presents a motivation-driven method for their learning. The method proposed considers the agent and its environment as a single unit, thus intrinsically relating agent{\textquoteright}s interactions to fluctuations of the agent{\textquoteright}s internal motivation. Being that the motivational state is an expression of the agent{\textquoteright}s physiology, the existing causality of interactions and their effect on the motivational state is exploited as a principle to learn object affordances. The hypothesis is tested in a Webots 4.0 simulator with a Khepera robot.}, isbn = {1-902956-41-7}, url = {https://aisb.org.uk/wp-content/uploads/2019/12/2_Agents_Final.pdf}, author = {Cos-Aguilera, Ignasi and Lola Ca{\~n}amero and Gillian M Hayes}, editor = {Lola Ca{\~n}amero} } @inproceedings {2003, title = {Motivation-driven learning of object affordances: First experiments using a simulated khepera robot}, booktitle = {Proc. 5th International Conference in Cognitive Modelling (ICCM{\textquoteright}03)}, year = {2003}, pages = {57{\textendash}62}, address = {Bamberg, Germany}, author = {Cos-Aguilera, Ignasi and Lola Ca{\~n}amero and Gillian M Hayes}, editor = {Detjer, Frank and D{\"o}rner, Dietrich and Harald Schaub} } @inproceedings {2002, title = {Meaningful Information, Sensor Evolution, and the Temporal Horizon of Embodied Organisms}, booktitle = {Artificial Life VIII: Proceedings of the Eighth International Conference on Artificial Life}, year = {2002}, pages = {345{\textendash}349}, publisher = {MIT Press}, organization = {MIT Press}, address = {Sydney, Australia}, abstract = {We survey and outline how an agent-centered, information-theoretic approach to meaningful information extending classical Shannon information theory by means of utility measures relevant for the goals of particular agents can be applied to sensor evolution for real and constructed organisms. Furthermore, we discuss the relationship of this approach to the programme of freeing artificial life and robotic systems from reactivity, by describing useful types of information with broader temporal horizon, for signaling, communication, affective grounding, two-process learning, individual learning, imitation and social learning, and episodic experiential information (memories, narrative, and culturally transmitted information).}, isbn = {9780262692816}, author = {Nehaniv, Chrystopher L and Daniel Polani and Kerstin Dautenhahn and Ren{\'e} te Boekhorst and Lola Ca{\~n}amero}, editor = {Russell Standish and Mark A Bedau and Hussein A Abbass} } @inproceedings {1997, title = {Modeling Motivations and Emotions as a Basis for Intelligent Behavior}, booktitle = {Proceedings of the First International Conference on Autonomous Agents (Agents{\textquoteright}97)}, year = {1997}, pages = {148{\textendash}155}, publisher = {The ACM Press}, organization = {The ACM Press}, address = {Marina del Rey, CA, USA}, isbn = {0-89791-877-0}, doi = {10.1145/267658.267688}, author = {D Ca{\~n}amero}, editor = {W Lewis Johnson} }