Search for: All records

Despite an increasing number of successful interventions designed to broaden participation in computing research, there is still significant attrition among historically marginalized groups in the computing research pipeline. This experience report describes a first-of-its-kind Undergraduate Consortium (UC; https://aaai-uc.github.io/about) that addresses this challenge by empowering students with a culmination of their undergraduate research in a conference setting. The UC, conducted at the AAAI Conference on Artificial Intelligence (AAAI), aims to broaden participation in the AI research community by recruiting students, particularly those from historically marginalized groups, supporting them with mentorship, advising, and networking as an accelerator toward graduate school, AI research, and their scientific identity. This paper presents our program design, inspired by a rich set of evidence-based practices, and a preliminary evaluation of the first years that points to the UC achieving many of its desired outcomes. We conclude by discussing insights to improve our program and expand to other computing communities. 

Fluency---described as the ``coordinated meshing of joint activities between members of a well-synchronized team''---is essential to human-robot team success. Human teams achieve fluency through rich, often mostly implicit, communication. A key challenge in bridging the gap between industry and academia is understanding what influences human perception of a fluent team experience to better optimize human-robot fluency in industrial environments. This paper addresses this challenge by developing an online experiment featuring videos that vary the timing of human and robot actions to influence perceived team fluency. Our results support three broad conclusions. First, we did not see differences across most subjective fluency measures. Second, people report interactions as more fluent as teammates stay more active. Third, reducing delays when humans' tasks depend on robots increases perceived team fluency. 

When communication between teammates is limited to observations of each other's actions, agents may need to improvise to stay coordinated. Unfortunately, current methods inadequately capture the uncertainty introduced by a lack of direct communication. This paper augments existing frameworks to introduce Simple Temporal Networks for Improvisational Teamwork (STN-IT)—a formulation that captures both the temporal dependencies and uncertainties between agents who need to coordinate but lack reliable communication. We define the notion of strong controllability for STN-ITs, which establishes a static scheduling strategy for controllable agents that produces a consistent team schedule, as long as non-communicative teammates act within known problem constraints. We provide both an exact and approximate approach for finding strongly controllable schedules, empirically demonstrate the trade-offs between these approaches on benchmarks of STN-ITs, and show analytically that the exact method is correct. 

Automated scheduling is potentially a very useful tool for facilitating efficient, intuitive interactions between a robot and a human teammate. However, a current gap in automated scheduling is that it is not well understood how to best represent the timing uncertainty that human teammates introduce. This paper attempts to address this gap by designing an online human-robot collaborative packaging game that we use to build a model of human timing uncertainty from a population of crowdworkers. We conclude that heavy-tailed distributions are the best models of human temporal uncertainty, with a Log-Normal distribution achieving the best fit to our experimental data. We discuss how these results along with our collaborative online game will inform and facilitate future explorations into scheduling for improved human-robot fluency. 

Flexibility is generally agreed to be a desirable feature of a Simple Temporal Network (STN). However, exactly what flexibility attempts to measure has varied, making it difficult to objectively evaluate flexibility metrics. Further, past metrics tend to lose information or exhibit other undesirable properties when aggregating the flexibility measures of individual events across an entire STN. Our work is driven by the realization that the solution space of an STN is a convex polyhedron whose geometric properties convey useful information about the STN. These geometric inspirations lead to measures of an STN solution space and also motivate a set of desiderata for general flexibility metrics. We also put forth two new geometrically-inspired flexibility metrics that have some theoretical advantages over existing metrics. Finally, we comprehensively evaluate both new and existing flexibility metrics against our proposed desiderata.