Last summer, the students in computer science professor Jim Boerkoel\u2019s Human Experience & Agent Teamwork Lab (HEATLab) studied several aspects of human-robot interaction and wrote up their findings. This summer, they will celebrate the publication of three papers at the International Conference on Automated Planning and Scheduling (ICAPS) in Berkeley, California.<\/p>\n\n\n\n
\u201cI really didn\u2019t expect this,\u201d says Boerkoel of the exciting news that all three papers were selected. \u201cIt\u2019s a testament to how awesome my students are.\u201d<\/p>\n\n\n\n
Shyan Akmal \u201919, Savana Ammons \u201920, Maggie Li \u201919 (\u201cQuantifying Degrees of Controllability in Temporal Networks with Uncertainty\u201d), Joon Lee \u201920, Viva Ojha \u201919 (\u201cMeasuring and Optimizing Durability Against Scheduling Disturbances\u201d) and Jordan Abrahams \u201919 (\u201cReducing the Computational and Communication Overhead of Robust Agent Rescheduling\u201d) were members of the summer 2018 HEATLab team.<\/p>\n\n\n\n
In addition to being pleasantly surprised at the number of accepted papers, Boerkoel is also impressed with how quickly these papers came together. \u201cIn this case, two of the three papers were started and finished during the 10-week summer session. Our students are just really well-versed at the skills required for effectively carrying out and communicating research. I\u2019d compare them to second- and third-year PhD students in terms of preparation and productivity.\u201d<\/p>\n\n\n\n
Here\u2019s a summary of the papers:<\/p>\n\n\n\n
\u201cQuantifying Degrees of Controllability in Temporal Networks with Uncertainty\u201d
Shyan Akmal \u201919, Savana Ammons \u201920, Maggie Li \u201919 launched a new student-led project that looks to deal with situations where scheduling uncertainty outstrips an agent\u2019s ability to control for it. They developed new analytical tools for assessing what they coined \u201cthe degree of controllability,\u201d which measures the likelihood that an agent (e.g., robot) can control for the presence of scheduling uncertainty (due to, e.g., slippage or localization error). In addition to these new analytical tools, they also developed and empirically validated approximate methods for finding scheduling strategies that maximize likelihood of success.<\/p>\n\n\n\n
\u201cMeasuring and Optimizing Durability Against Scheduling Disturbances\u201d
Joon Lee \u201920 and Viva Ojha \u201919 use geometric interpretations of scheduling problems to develop new ways to quantify a schedule\u2019s resilience to unexpected scheduling disturbances. In particular, they defined a new concept, \u201cdurability,\u201d which characterizes a temporal plan\u2019s resilience to disturbances. They also proposed several durability metrics and two new approaches for finding optimally durable schedules. An additional contribution was an empirical model for simulating realistic sources of schedule uncertainty, which they used to perform a systematic empirical evaluation of proposed metrics and approaches.<\/p>\n\n\n\n
\u201cReducing the Computational and Communication Overhead of Robust Agent Rescheduling\u201d
Jordan Abrahams \u201919 and co-author Jeremy Frank built on the work of the 2017\u20132018 NASA Ames Research\u2019s HMC Computer Science Clinic team to explore how they could adapt uncertainty-aware, dynamic scheduling advice to be more judicious in how often rescheduling, and, by extension, communication, was required in multi-agent settings. In particular, they introduced a single streamlined algorithm that trades robustness for various forms of computational overhead including multi-agent communication. In addition to these algorithmic advances, they contribute a systematic, thorough empirical evaluation that significantly improves the understanding of this space of tradeoffs.<\/p>\n","protected":false},"excerpt":{"rendered":"
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