Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior

By Been Kim, Caleb M. Chacha, Julie Arnold Shah. 2013

18 citations recorded by the source corpus.

We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans. This hybrid approach enables us to overcome the challenge of performing inference over the large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentation and show we are able to infer a human team's final plan with 83% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work that integrates a logical planning technique within a generative model to perform plan inference.

Subfields: Artificial Intelligence

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Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, methodology): “In [86], a ML model is given a document containing the conversation of humans making a plan.”
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018, background): “Contexts studied include aiding medical experts in diagnosis, increasing test performance of users of personalised learning environments, or enabling network security analysts to correctly identify incidents (e.g. [34, 83, 21]).”
Interactive and interpretable machine learning models for human machine collaboration (2015, methodology): “The preliminary version of this work [80] applied the following distribution:”
A Roadmap for a Rigorous Science of Interpretability (2017, background): “…under severe time constraints, one might create abstract tasks in which other factors—such as the overall task complexity—can be controlled [Kim et al., 2013, Lakkaraju et al., 2016] The key question, of course, is how we can evaluate the quality of an explanation without a specific…”
Towards A Rigorous Science of Interpretable Machine Learning (2017, background): “…under severe time constraints, one might create abstract tasks in which other factors—such as the overall task complexity—can be controlled [Kim et al., 2013, Lakkaraju et al., 2016] The key question, of course, is how we can evaluate the quality of an explanation without a speciﬁc…”