Autonomous Causally-Driven Explanation of Actions

Katz, Garrett E.; Dullnig, Dale; Davis, Gregory P.; Gentili, Rodolphe J.; Reggia, James A.

doi:10.1109/csci.2017.133

Cited by 1 publication

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“…Such an ability is critical to making the simulated reasoning mechanisms of robots and other autonomous systems transparent to people, and this transparency is often an important aspect of machine trustworthiness. We have recently introduced methods by which CERIL can justify its actions to a human observer based on “causal plan graphs” (Katz et al, 2017c ). Figure 2 gives an example of this action sequence justification ability in its current form for a simple device maintenance task.…”

Section: A Cognitive Humanoid Robot That Learns By Imitatingmentioning

confidence: 99%

Humanoid Cognitive Robots That Learn by Imitating: Implications for Consciousness Studies

2018

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While the concept of a conscious machine is intriguing, producing such a machine remains controversial and challenging. Here, we describe how our work on creating a humanoid cognitive robot that learns to perform tasks via imitation learning relates to this issue. Our discussion is divided into three parts. First, we summarize our previous framework for advancing the understanding of the nature of phenomenal consciousness. This framework is based on identifying computational correlates of consciousness. Second, we describe a cognitive robotic system that we recently developed that learns to perform tasks by imitating human-provided demonstrations. This humanoid robot uses cause-effect reasoning to infer a demonstrator's intentions in performing a task, rather than just imitating the observed actions verbatim. In particular, its cognitive components center on top-down control of a working memory that retains the explanatory interpretations that the robot constructs during learning. Finally, we describe our ongoing work that is focused on converting our robot's imitation learning cognitive system into purely neurocomputational form, including both its low-level cognitive neuromotor components, its use of working memory, and its causal reasoning mechanisms. Based on our initial results, we argue that the top-down cognitive control of working memory, and in particular its gating mechanisms, is an important potential computational correlate of consciousness in humanoid robots. We conclude that developing high-level neurocognitive control systems for cognitive robots and using them to search for computational correlates of consciousness provides an important approach to advancing our understanding of consciousness, and that it provides a credible and achievable route to ultimately developing a phenomenally conscious machine.

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Section: A Cognitive Humanoid Robot That Learns By Imitatingmentioning

confidence: 99%