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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2019)

As robots become more capable and commonplace, it becomes increasingly important that they are transparent to humans. People need to have accurate mental models of a robot, so that they can anticipate what it will do, know when and where not to rely it, and understand why it failed. This helps engineers ensure safety and robustness of the robot systems they develop, and enables human end-users to interact more safely and seamlessly with robots.

This thesis introduces a framework for producing robot behavior that increases transparency. Our key insight is that a robot's actions do not just influence the physical world; they also inevitably influence a human observer's mental model of the robot. We attempt to model the latter---how humans might make inferences about a robot's objectives, policy, and capabilities from observations of its behavior---so that we can then present examples of robot behavior that optimally bring the human's understanding closer to the true robot model. In this way, our framework casts transparency as an optimization problem.

Part I introduces our framework of optimizing for robot transparency, and applies it in three ways: communicating a robot's objectives, which situations it can handle, and why it is incapable of performing a task. Part II investigates how transparency is useful not just for safe and seamless interaction, but also for learning. When humans teach a robot, giving human teachers transparency regarding what the robot has learned so far makes it easier for them to select informative teaching examples.

    Cover page: Optimizing for Robot Transparency
    • Article
    • Peer Reviewed
    Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 44 (2022)
    Human children are proficient explorers, using causal information to great benefit. In contrast, typical AI agents do not consider underlying causal structures during exploration. To improve our understanding of the differences between children and agents—and ultimately to improve AI agents’ performance—we designed a virtual Blicket experiment to test childrens’ ability to leverage causal information while exploring a novel environment. This experiment doubles as an RL environment with a controllable causal structure, allowing us to evaluate exploration strategies used by both agents and children. Our results demonstrate that there are significant differences between information-gain optimal RL exploration and the exploration of children: in particular, children appear to consider a wide range of creative overhypotheses, including stochasticity, total weight, object ordering, and more. We leverage this new insight to lay the groundwork for future research into efficient exploration and disambiguation of causal structures for RL algorithms.
      Cover page: Learning Causal Overhypotheses through Exploration in Children and Computational ModelsCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 45 (2023)
      Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. One of the key challenges for current machine learning algorithms is modeling and understanding causal overhypotheses: transferable abstract hypotheses about sets of causal relationships. In contrast, even young children spontaneously learn causal overhypotheses, and use these to guide their exploration or to generalize to new situations. This has been demonstrated in a variety of cognitive science experiments using the “blicket detector” environment. We present a causal learning benchmark adapting the “blicket" environment for machine learning agents and evaluate a range of state-of-the-art methods in this environment. We find that although most agents have no problem learning causal structures seen during training, they are unable to learn causal overhypotheses from these experiences, and thus cannot generalize to new settings.
        Cover page: Towards Understanding How Machines Can Learn Causal OverhypothesesCreative Commons 'BY' version 4.0 license
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