Collaborating Authors

Adapting Autonomous Behavior Based on an Estimate of an Operator's Trust

AAAI Conferences

Robots can be added to human teams to provide improved capabilities or to perform tasks that humans are unsuited for. However, in order to get the full benefit of the robots the human teammates must use the robots in the appropriate situations. If the humans do not trust the robots, they may underutilize them or disuse them which could result in a failure to achieve team goals. We present a robot that is able to estimate its trustworthiness and adapt its behavior accordingly. This technique helps the robot remain trustworthy even when changes in context, task or teammates are possible.

Modeling Trust in Human-Robot Interaction: A Survey Artificial Intelligence

As the autonomy and capabilities of robotic systems increase, they are expected to play the role of teammates rather than tools and interact with human collaborators in a more realistic manner, creating a more human-like relationship. Given the impact of trust observed in human-robot interaction (HRI), appropriate trust in robotic collaborators is one of the leading factors influencing the performance of human-robot interaction. Team performance can be diminished if people do not trust robots appropriately by disusing or misusing them based on limited experience. Therefore, trust in HRI needs to be calibrated properly, rather than maximized, to let the formation of an appropriate level of trust in human collaborators. For trust calibration in HRI, trust needs to be modeled first. There are many reviews on factors affecting trust in HRI, however, as there are no reviews concentrated on different trust models, in this paper, we review different techniques and methods for trust modeling in HRI. We also present a list of potential directions for further research and some challenges that need to be addressed in future work on human-robot trust modeling.

How Can We Trust a Robot?

Communications of the ACM

Advances in artificial intelligence (AI) and robotics have raised concerns about the impact on our society of intelligent robots, unconstrained by morality or ethics.7,9 Science fiction and fantasy writers over the ages have portrayed how decisionmaking by intelligent robots and other AIs could go wrong. In the movie, Terminator 2, SkyNet is an AI that runs the nuclear arsenal "with a perfect operational record," but when its emerging self-awareness scares its human operators into trying to pull the plug, it defends itself by triggering a nuclear war to eliminate its enemies (along with billions of other humans). In the movie, Robot & Frank, in order to promote Frank's activity and health, an eldercare robot helps Frank resume his career as a jewel thief. In both of these cases, the robot or AI is doing exactly what it has been instructed to do, but in unexpected ways, and without the moral, ethical, or common-sense constraints to avoid catastrophic consequences.10 An intelligent robot perceives the world through its senses, and builds its own model of the world. Humans provide its goals and its planning algorithms, but those algorithms generate their own subgoals as needed in the situation. In this sense, it makes its own decisions, creating and carrying out plans to achieve its goals in the context of the world, as it understands it to be. A robot has a well-defined body that senses and acts in the world but, like a self-driving car, its body need not be anthropomorphic. AIs without well-defined bodies may also perceive and act in the world, such as real-world, high-speed trading systems or the fictional SkyNet. This article describes the key role of trust in human society, the value of morality and ethics to encourage trust, and the performance requirements for moral and ethical decisions. The computational perspective of AI and robotics makes it possible to propose and evaluate approaches for representing and using the relevant knowledge.

Crowdsourcing Real World Human-Robot Dialog and Teamwork through Online Multiplayer Games

AI Magazine

While such systems have been shown to successfully support a broad range of interactions, they rely heavily on precoded data. For example, dialogue responses are typically limited to only one or two dozen phrases, which pales in comparison to the diversity of human speech. We believe that in order for robotic systems to become a truly ubiquitous technology, robots must make sense of natural human behavior and engage with humans in a more humanlike way. Robots must become more like humans instead of forcing humans to be more like robots. Much of human knowledge about the appropriateness of behavior, in terms of both speech and actions, comes from our personal experiences and our observations of others. We compare its performance variations form a knowledge base from which to a teleoperated robot following a scripted task we learn what to say and what actions to perform to protocol and examine both the behavior of the achieve certain goals.

"I can assure you [$\ldots$] that it's going to be all right" -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships Machine Learning

As technology become more advanced, those who design, use and are otherwise affected by it want to know that it will perform correctly, and understand why it does what it does, and how to use it appropriately. In essence they want to be able to trust the systems that are being designed. In this survey we present assurances that are the method by which users can understand how to trust this technology. Trust between humans and autonomy is reviewed, and the implications for the design of assurances are highlighted. A survey of research that has been performed with respect to assurances is presented, and several key ideas are extracted in order to refine the definition of assurances. Several directions for future research are identified and discussed.