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The study presents an adaptive technique that enables a humanoid robot to select appropriate actions to maintain the engagement level of users while they play a serious game for cognitive training. The goal is to design and develop an adaptation strategy for changing the robot's behaviour based on Reinforcement Learning (RL) to encourage the user to remain engaged. Initially, we trained the algorithm in a simulated environment before moving on to a real user experiment. Thus, we first design, develop, and validate the RL strategy in a simulated environment. Subsequently, we integrate the trained policy into the robotic system, allowing it to select the best actions based on the detected user state during real user test. The RL algorithm was designed and implemented to determine an effective adaptation strategy for the robot's actions, encompassing verbal and non-verbal interactions. The proposed solution was first trained in a simulated environment and then tested with 28 users in a mixed-method design study. [ABSTRACT FROM AUTHOR]

Within human-robot interaction (HRI), research on robot personality has largely drawn on trait theories and models, such as the Big Five and OCEAN. We argue that reliance on trait models in HRI has led to a limited understanding of robot personality as a question of stable traits that can be designed into a robot plus how humans with certain traits respond to particular robots. However, trait-based approaches exist alongside other ways of understanding personality, including approaches focusing on more dynamic constructs such as adaptations and narratives. We suggest that a deep understanding of robot personality is only possible through a cross-disciplinary effort to integrate these different approaches. We propose an Integrative Framework for Robot Personality Research (IF), wherein robot personality is defined not as a property of the robot, nor of the human perceiving the robot, but as a complex assemblage of components at the intersection of robot design and human factors. With the IF, we aim to establish a common theoretical grounding for robot personality research that incorporates personality constructs beyond traits and treats these constructs as complementary and fundamentally interdependent. [ABSTRACT FROM AUTHOR]

In this article, we aim to evaluate the role of robots' personality-driven behavioural patterns on users' intention to use in an entertainment scenario. Toward such a goal, we designed two personalities: one introverted with an empathic and self-comparative interaction style, and the other extroverted with a provocative and other-comparative interaction style. To evaluate the proposed technology acceptance model, we conducted an experiment (N = 209) at a public venue where users were requested to play a game with the support of the TIAGo robot. Our findings show that the robot personality affects the acceptance model and three relevant drivers: perceived enjoyment, perceived usefulness, and social influence. The extroverted robot was perceived as more useful than the introverted, and participants who interacted with it were faster at solving the game. On the other hand, the introverted robot was perceived as more enjoyable but less useful than the extroverted, and participants who interacted with it made fewer mistakes. Taken together, these findings support the importance of designing proper robot personalities in influencing users' acceptance, featuring that a given style can elicit a different driver of acceptance. [ABSTRACT FROM AUTHOR]

In the last years, museums around the world began using museum guide robots as an alternative to audio guides. Thus, it is important to identify how these new museum guides can optimally interact with visitors and whether they are more efficient that conventional audio guides. In this paper, we present the results from an experimental study which compared conventional audio guides with robots with different 'personality'. These results demonstrate that people remember significantly more information when they are guided by a cheerful robot than when their guide is a serious one or a conventional audio system. Importantly, we also introduce the idea of two collaborative tour guide robots. This idea was inspired by cognitive studies showing that people remember more when they receive information from two different human speakers. Interestingly, our participants also liked more our collaborative robots, than any version of a single robot and/or audio system. [ABSTRACT FROM AUTHOR]

Robot personalities are today painstakingly hand-crafted by content creators who specify motions, facial animations, and scripts in order to convey a unifying sense of character via interactions. While user preferences can be incorporated by modifying these behaviors to some degree, such as by inserting a user’s name or preference into a script, by and large new content must be continually created in order to maintain the robot’s ‘freshness.’ This content treadmill represents a long tail of work that diverts resources away from improving the underlying capabilities of the robot. Here we outline a novel method for defining robot personality that allows each robot to have and express a unique personality through varying behaviors that reduces the need for content creation. [ABSTRACT FROM AUTHOR]

Abstract: The genetic robot has many configurable genes that contribute to defining the robot’s personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot’s personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes MBTI-EAGRP. It is a fully autonomic gene-generative algorithm for a genetic robot’s personality in a mobile phone. After grasping the user preferences through MBTI assessment using the neural network algorithm, the evolutionary algorithm generates and evolves a gene pool that customizes the robot’s genome so that it closely matches a simplified set of personality features preferred by the user. Finally, an evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios and real MBTI measurements. [Copyright &y& Elsevier]

Abstract: This paper proposes a new concept of the genetic robot characterized by its own robot genome in which each chromosome consists of many genes that contribute to defining the robot’s personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot’s personality while manually initializing values for the individual genes. To overcome this difficulty, this paper also proposes an evolutionary algorithm for evolving a genetic robot’s personality (EAGRP) in a mobile phone. EAGRP evolves a gene pool that customizes the robot’s genome so that it closely matches a simplified set of personality features desired by the user. It acts on a two-dimensional individual, composed of artificial chromosomes, upon which a new masking method, the Eliza–Meme scheme, is used to derive a plausible individual given the restricted preference settings. This paper also proposes a crossover method that enables reproduction for the two-dimensional genome. Finally, an evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios. [ABSTRACT FROM AUTHOR]

Future service robots mass-produced for practical applications may benefit from having personalities. To engineer robot personalities in significant quantities for practical applications, we need first to identify the personality dimensions on which personality traits can be effectively optimised by minimising the distances between engineering targets and the corresponding robots under construction, since not all personality dimensions are applicable and equally prominent. Whether optimisation is possible on a personality dimension depends on how specific users consider the personalities of a type of robot, especially whether they can provide effective feedback to guide the optimisation of certain traits on a personality dimension. The dimensions may vary from user group to user group since not all people consider a type of trait to be relevant to a type of robot, which our results corroborate. Therefore, we had proposed a test procedure as an engineering tool to identify, with the help of a user group, personality dimensions for engineering robot personalities out of a type of robot knowing its typical usage. It applies to robots that can imitate human behaviour and small user groups with at least eight people. We confirmed its effectiveness in limited-scope tests. [ABSTRACT FROM AUTHOR]