1 citations · 1 across the 3 of their papers we have counts for
5 papers
Validating a Cortisol-Inspired Framework for Human-Robot Interaction with a Replication of the Still Face Paradigm
Sara Mongile, Ana Tanevska, Francesco Rea +1
When interacting with others in our everyday life, we prefer the company of those who share with us the same desire of closeness and intimacy (or lack thereof), since this determin…
Incorporating Rivalry in Reinforcement Learning for a Competitive Game
Pablo Barros, Ana Tanevska, Ozge Yalcin +1
Recent advances in reinforcement learning with social agents have allowed us to achieve human-level performance on some interaction tasks. However, most interactive scenarios do no…
Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents
Pablo Barros, Ana Tanevska, Francisco Cruz +1
Designing the decision-making processes of artificial agents that are involved in competitive interactions is a challenging task. In a competitive scenario, the agent does not only…
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game
Pablo Barros, Ana Tanevska, Alessandra Sciutti
Learning how to adapt to complex and dynamic environments is one of the most important factors that contribute to our intelligence. Endowing artificial agents with this ability is…
A Socially Adaptable Framework for Human-Robot Interaction
Ana Tanevska, Francesco Rea, Giulio Sandini +2
In our everyday lives we are accustomed to partake in complex, personalized, adaptive interactions with our peers. For a social robot to be able to recreate this same kind of rich,…