James C. Boerkoel

Using hybrid scheduling for the semi-autonomous formation of expert teams

Selecting and scheduling human experts to cooperatively solve a problem can be a highly complex task, given various constraints (such as what expertise is needed and when) and preferences (such as which expertise an expert most prefers to exercise). Computational agents can thus greatly help users form and schedule expert teams. This paper introduces a new formulation of the team formation and scheduling problem as a Hybrid Scheduling Problem (HSP) and compares the performance of an agent using the HSP formulation to a prior agent-based approach. We empirically demonstrate the promise of the HSP formulation and highlight how the application of HSP techniques to this problem has led us to identify important modifications to mechanisms that improve HSP solving. Finally, we summarize how the HSP formulation can support human-agent collaboration during the process of forming and scheduling expert teams.

Towards control and sensing for an autonomous mobile robotic assistant navigating assembly lines

There exists an increasing demand to incorporate mobile interactive robots to assist humans in repetitive, non-value added tasks in the manufacturing domain. Our aim is to develop a mobile robotic assistant for fetch-and-deliver tasks in human-oriented assembly line environments. Assembly lines present a niche yet novel challenge for mobile robots; the robot must precisely control its position on a surface which may be either stationary, moving, or split (e.g. in the case that the robot straddles the moving assembly line and remains partially on the stationary surface). In this paper we present a control and sensing solution for a mobile robotic assistant as it traverses a moving-floor assembly line. Solutions readily exist for control of wheeled mobile robots on static surfaces; we build on the open-source Robot Operating System (ROS) software architecture and generalize the algorithms for the moving line environment. Off-the-shelf sensors and localization algorithms are explored to sense the moving surface, and a customized solution is presented using PX4Flow optic flow sensors and a laser scanner-based localization algorithm. Validation of the control and sensing system is carried out both in simulation and in hardware experiments on a customized treadmill. Initial demonstrations of the hardware system yield promising results; the robot successfully maintains its position while on, and while straddling, the moving line.

Comparing techniques for the semi-autonomous formation of expert teams

Selecting and scheduling human experts can be a highly complex task, given various constraints (such as on what expertise is needed and when) and preferences (such as which expertise an expert most prefers to exercise). A system of multiple computational agents can thus greatly help users form and schedule expert teams. This paper compares two alternative implementations of such agents, highlighting the strengths and limitations of each. In particular, we describe a new formulation of the team formation and scheduling problem as a Hybrid Scheduling Problem (HSP), empirically demonstrate the promise of this formulation, and highlight how the application of HSP to this problem has led us to identify important modifications to mechanisms that improve HSP solving. Finally, we summarize how the HSP formulation can support a high degree of human-agent collaboration during the process of forming and scheduling expert teams.

Artificial Intelligence Education: Editorial Introduction

This issue of AI Magazine include five articles covering subjects of current concern to the AI education community. This editorial introduces those five articles.

Mobile Robots for Moving-Floor Assembly Lines: Design, Evaluation, and Deployment

Robots that operate alongside or cooperatively with humans are envisioned as the next generation of robotics. Toward this vision, we present the first mobile robot system designed for and capable of operating on the moving floors of automotive final assembly lines (AFALs). AFALs represent a distinct challenge for mobile robots in the form of dynamic surfaces: the conveyor belts that transport cars throughout the factory during final assembly.

Solving the multiagent selection and scheduling problem

Consider Ann’s morning scheduling problem. Ann is a graduate student, who, among many other objectives, would like to both exercise and work on research during her morning. I summarize possible morning activities in Table 1. Even for these two simple objectives, selecting a feasible schedule from the many possible schedules (run then generate experimental results, write then bike, swim then read some related work, etc.) may be non-trivial. However, suppose Ann wants to run with her friend Bill, who notoriously oversleeps his alarm. Additionally, suppose also that Ann must coordinate the use of her lab’s computational cluster with her lab mate Claire. Without further information from Bill and Claire, it is impossible for Ann to determine which candidate schedules will successfully achieve her morning goals. One option for Ann would be to myopically select her schedule anyway, with the risk that her attempt to run with Bill or to use the computational cluster could result in a failed goal. As another option, Ann could also volunteer to collect the scheduling constraints of both Bill and Claire and generate a single joint morning schedule. However, this puts additional scheduling burden on Ann while requiring both Bill and Claire to reveal other scheduling commitments they may prefer to keep private. Even if Ann employed a centralized computational agent to solve this global scheduling problem, the resulting combinatorics may limit the scalability of such a centralized approach. Instead, the pervasiveness of personal computational devices, coupled with desires for scalability and privacy, argue for decentrally solving such problems using multiagent algorithms. My thesis focuses on providing scalable, multiagent algorithms for solving rich, complex multiagent activity scheduling and selection problems, while retaining as much privacy as possible on behalf of the human users. My approach is distinct from other recent multiagent scheduling approaches (Hunsberger 2002; Smith et al. 2007; Shah, Conrad, and Williams 2009) in that it uses a constraint-based representation of selection (finite-domain) aspects of scheduling problems in addition to the scheduling aspects. I proceed by introducing the Multiagent Selection and Scheduling Problem