ASU Scholarship Showcase

Human team members show a remarkable ability to infer the state of their partners and anticipate their needs and actions. Prior research demonstrates that an artificial system can make some predictions accurately concerning artificial agents. This study investigated whether an artificial system could generate a robust Theory of Mind of human teammates. An urban search and rescue (USAR) task environment was developed to elicit human teamwork and evaluate inference and prediction about team members by software agents and humans. The task varied team members’ roles and skills, types of task synchronization and interdependence, task risk and reward, completeness of mission planning, and information asymmetry. The task was implemented in MinecraftTM and applied in a study of 64 teams, each with three remotely distributed members. An evaluation of six Artificial Social Intelligences (ASI) and several human observers addressed the accuracy with which each predicted team performance, inferred experimentally manipulated knowledge of team members, and predicted member actions. All agents performed above chance; humans slightly outperformed ASI agents on some tasks and significantly outperformed ASI agents on others; no one ASI agent reliably outperformed the others; and the accuracy of ASI agents and human observers improved rapidly though modestly during the brief trials.

Summer daytime cooling efficiency of various land cover is investigated for the urban core of Phoenix, Arizona, using the Local-Scale Urban Meteorological Parameterization Scheme (LUMPS). We examined the urban energy balance for 2 summer days in 2005 to analyze the daytime cooling-water use tradeoff and the timing of sensible heat reversal at night. The plausibility of the LUMPS model results was tested using remotely sensed surface temperatures from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery and reference evapotranspiration values from a meteorological station. Cooling efficiency was derived from sensible and latent heat flux differences. The time when the sensible heat flux turns negative (sensible heat flux transition) was calculated from LUMPS simulated hourly fluxes. Results indicate that the time when the sensible heat flux changes direction at night is strongly influenced by the heat storage capacity of different land cover types and by the amount of vegetation. Higher heat storage delayed the transition up to 3 h in the study area, while vegetation expedited the sensible heat reversal by 2 h. Cooling efficiency index results suggest that overall, the Phoenix urban core is slightly more efficient at cooling than the desert, but efficiencies do not increase much with wet fractions higher than 20%. Industrial sites with high impervious surface cover and low wet fraction have negative cooling efficiencies. Findings indicate that drier neighborhoods with heterogeneous land uses are the most efficient landscapes in balancing cooling and water use in Phoenix. However, further factors such as energy use and human vulnerability to extreme heat have to be considered in the cooling-water use tradeoff, especially under the uncertainties of future climate change.

Hundreds of thousands of archaeological investigations in the United States conducted over the last several decades have documented a large portion of the recovered archaeological record in the United States. However, if we are to use this enormous corpus to achieve richer understandings of the past, it is essential that both CRM and academic archaeologists change how they manage their digital documents and data over the course of a project and how this information is preserved for future use. We explore the nature and scope of the problem and describe how it can be addressed. In particular, we argue that project workflows must ensure that the documents and data are fully documented and deposited in a publicly accessible, digital repository where they can be discovered, accessed, and reused to enable new insights and build cumulative knowledge.

Cientos de miles de investigaciones arqueológicas en los Estados Unidos realizado en las últimas décadas han documentado una gran parte del registro arqueológico recuperado en los Estados Unidos. Sin embargo, si vamos a utilizar este enorme corpus para lograr entendimientos más ricos del pasado, es esencial que CRM y los arqueólogos académicos cambian cómo administran sus documentos digitales y los datos en el transcurso de un proyecto y cómo se conserva esta información para uso en el futuro. Exploramos la naturaleza y el alcance del problema y describimos cómo se pueden abordarse. En particular, sostenemos que los flujos de trabajo de proyecto deben asegurarse que los documentos y datos son totalmente documentados y depositados en un repositorio digital de acceso público, donde puede ser descubiertos, acceder y reutilizados para activar nuevos conocimientos y construir conocimiento acumulativo.