Human-agent trust relationships in a real-time collaborative game

Collaborative virtual agents are often deployed to help users make decisions in real-time. For this collaboration to work, users must adequately trust the agents that they are interacting with. In my research, I use a game where human-agent interactions are recorded via a logging system and survey instruments in order to explore this trust relationship. I then study the impact that different agents have on reliance, performance, cognitive load and trust. I seek to understand which aspects of an agent influence the development of trust the most. With this research, I hope to pave the way for trust-aware agents, capable of adapting their behaviours to users in real-time

Impact of agent reliability and predictability on trust in real time human-agent collaboration

Trust is a prerequisite for effective human-agent collaboration. While past work has studied how trust relates to an agent's reliability, it has been mainly carried out in turn based scenarios, rather than during real-time ones. Previous research identified the performance of an agent as a key factor influencing trust. In this work, we posit that an agent's predictability also plays an important role in the trust relationship, which may be observed based on users' interactions. We designed a 2x2 within-groups experiment with two baseline conditions: (1) no agent (users' individual performance), and (2) near-flawless agent (upper bound). Participants took part in an interactive aiming task where they had to collaborate with different agents that varied in terms of their predictability, and were controlled in terms of their performance. Our results show that agents whose behaviours are easier to predict have a more positive impact on task performance, reliance and trust while reducing cognitive workload. In addition, we modelled the human-agent trust relationship and demonstrated that it is possible to reliably predict users' trust ratings using real-time interaction data. This work seeks to pave the way for the development of trust-aware agents capable of adapting and responding more appropriately to users

Learning Existing Social Conventions via Observationally Augmented Self-Play

In order for artificial agents to coordinate effectively with people, they must act consistently with existing conventions (e.g. how to navigate in traffic, which language to speak, or how to coordinate with teammates). A group's conventions can be viewed as a choice of equilibrium in a coordination game. We consider the problem of an agent learning a policy for a coordination game in a simulated environment and then using this policy when it enters an existing group. When there are multiple possible conventions we show that learning a policy via multi-agent reinforcement learning (MARL) is likely to find policies which achieve high payoffs at training time but fail to coordinate with the real group into which the agent enters. We assume access to a small number of samples of behavior from the true convention and show that we can augment the MARL objective to help it find policies consistent with the real group's convention. In three environments from the literature - traffic, communication, and team coordination - we observe that augmenting MARL with a small amount of imitation learning greatly increases the probability that the strategy found by MARL fits well with the existing social convention. We show that this works even in an environment where standard training methods very rarely find the true convention of the agent's partners.Comment: Published in AAAI-AIES2019 - Best Pape

Resource sharing in technologically defined social networks

Technologically enabled sharing-economy networks are changing the way humans trade and collaborate. Here, using a novel ?Wi-Fi sharing? game, we explored determinants of human sharing strategy. Subjects (N = 1,950) participated in a networked game in which they could choose how to allocate a limited, but personally not usable, resource (representing unused Wi-Fi bandwidth) to immediate network neighbors. We first embedded N = 600 subjects into 30 networks, experimentally manipulating the range over which subjects could connect. We find that denser networks decrease any wealth inequality, but that this effect saturates. Individuals? benefit is shaped by their network position, with having many partners who in turn have few partners being especially beneficial. We propose a new, simplified ?sharing centrality? metric for quantifying this. Further experiments (N = 1,200) confirm the robustness of the effect of network structure on sharing behavior. Our findings suggest the possibility of interventions to help more evenly distribute shared resources over networks

Quality versus quantity of social ties in experimental cooperative networks.

Recent studies suggest that allowing individuals to choose their partners can help to maintain cooperation in human social networks; this behaviour can supplement behavioural reciprocity, whereby humans are influenced to cooperate by peer pressure. However, it is unknown how the rate of forming and breaking social ties affects our capacity to cooperate. Here we use a series of online experiments involving 1,529 unique participants embedded in 90 experimental networks, to show that there is a 'Goldilocks' effect of network dynamism on cooperation. When the rate of change in social ties is too low, subjects choose to have many ties, even if they attach to defectors. When the rate is too high, cooperators cannot detach from defectors as much as defectors re-attach and, hence, subjects resort to behavioural reciprocity and switch their behaviour to defection. Optimal levels of cooperation are achieved at intermediate levels of change in social ties

Rethinking Safe Control in the Presence of Self-Seeking Humans

Safe control methods are often designed to behave safely even in worst-case human uncertainties. Such design can cause more aggressive human behaviors that exploit its conservatism and result in greater risk for everyone. However, this issue has not been systematically investigated previously. This paper uses an interaction-based payoff structure from evolutionary game theory to model humans’ short-sighted, self-seeking behaviors. The model captures how prior human-machine interaction experience causes behavioral and strategic changes in humans in the long term. We then show that deterministic worst-case safe control techniques and equilibrium-based stochastic methods can have worse safety and performance trade-offs than a basic method that mediates human strategic changes. This finding suggests an urgent need to fundamentally rethink the safe control framework used in human-technology interaction in pursuit of greater safety for all

Reliability and validity of a novel quality of life questionnaire for female patients with adolescent idiopathic scoliosis: Scoliosis Japanese Questionnaire-27: a multicenter, cross-sectional study

Abstract Background A progressive deformity associated with adolescent idiopathic scoliosis (AIS) negatively affects a patient’s health-related quality of life (HRQOL). Although the Scoliosis Research Society-22 (SRS-22) is the standard measurement tool for assessing HRQOL in patients with AIS, it is partially suboptimal for evaluating HRQOL in Japanese patients with AIS because of cultural differences. The purpose of this study was to develop a novel patient-reported outcome measure for Japanese female patients with AIS and to evaluate the reliability and validity of this questionnaire in comparison with the SRS-22 tool. Methods We developed 27 questions based on the psychosocial problems in the daily life of young female patients with AIS in Japan, the Scoliosis Japanese Questionnaire-27 (SJ-27). To evaluate its reliability, the internal consistency was assessed using Cronbach’s alpha coefficient. Concurrent validity was evaluated using Spearman’s correlation coefficient between the SJ-27 and the SRS-22. To investigate the construct validity of the SJ-27, the correlation between the SJ-27 questions was assessed using Akaike’s information criterion (AIC). Results We analyzed 384 female patients with AIS. Cronbach’s alpha coefficients were 0.914 and 0.829 for the SJ-27 and the SRS-22, respectively. Spearman’s correlation coefficient between the SJ-27 and the SRS-22 was 0.692 (p < 0.001). The AIC analysis indicated that the SJ-27 items are divided into five domains, indicating that the SJ-27 covered a wide range of health-related problems among female patients with AIS. Conclusions The results suggest that the SJ-27 is a reliable and valid patient-reported outcome measure for evaluating HRQOL in female patients with AIS in Japan