Task 1. Performed a literature review on the effects of SD on higher-level cognition (Dr. Billman). This literature review found that the impact of SD on higher-level cognitive processes and tasks is less studied, less consistently found, and harder to compare across studies. Our research should improve knowledge about the nature of user performance and performance degradation on supervisory tasks under SD. This review also indicates that sleep-deprived participants experience degraded capacity for divergent or flexible thought, which can affect the ability to plan robotic tasks. In consequence, we broadened our investigation of SD countermeasures to include technology for adaptive task planning. Task 2. Developed a testbed for supervisory control of robots (Ms. Schreckenghost). We modified the NASA R2 robot simulation, from Co-I Dr. Kimberly Hambuchen, to include a power system with solar panels to charge batteries, and power units called DDCUs to distribute battery power to habitat systems. The robot turns knobs on a control panel to connect batteries to solar panels or DDCUs. This simulation was integrated with TRACLabs's PRIDE procedure automation software to provide procedures for autonomous execution by R2 to i) connect batteries to solar panels to recharge them, ii) connect batteries to power distribution, or iii) disconnect batteries. The participant decides which components should be connected and plans the connection sequences. Task 3. Designed supervisory control tasks for experiment 1 (Dr. Billman and Ms. Schreckenghost). These tasks were designed to make it difficult for the sleep-deprived participant i) to self-regulate switching among tasks in accord with allowed choices i.e., perseveration; and ii) to change strategy to suit conditions or to inhibit a dominant strategy, i.e., mental set rigidity. Task 4. Conduct Experiment 1 at BWH (Dr. Klerman). Experiment 1 is ongoing at the time of this report. Participants are selected based on education or work experience in engineering or science. They receive training one week prior to the experiment. The inpatient protocol lasts four calendar days, with five 2-hour sessions using the robotic testbed. Additionally, the participant takes PVT, and saliva and urine are collected. Task 5. Develop designs for technology countermeasures (Ms. Schreckenghost). We identify adaptive alerting as a promising automation countermeasure to the cognitive effects of SD. Adaptive alerting software detects situations with increased potential for errors around attentional lapses and notifies users about them to mitigate their impact. The literature review suggests a second candidate automation countermeasure that seeks to mitigate the anticipated effect of SD on divergent or novel thinking, particularly associated with task planning. For this research, automated task planning refers to selecting and ordering robotic tasks to be efficient while complying with operational constraints.