Search

Knowledge of intent is critical in high performing human teams. The fundamental question addressed by this research is, how should intent be integrated into future human‐artificial agent (AA) teams to improve coordination among team members? A brief review of the use of intent for improving performance within human‐human teams is conducted to provide a better understanding of this term. This review differentiates intent estimation from intent application, as well as the differentiation of "why," "what" and "how" based intent. A taxonomy of intent‐based systems is then developed through a review of existing examples in the literature. Together these reviews demonstrate that intent has been modeled in a variety of ways without a cohesive understanding of intent and its different forms. Based upon these reviews and our understanding of multi‐agent system architectures, we propose "operationalized intent" as a method of modeling intent regarding "how" the operators would like to execute the team's tasks. We propose including an Intent Agent (IA) dedicated to estimating intent of each operator and embedding knowledge of how to execute within the Functional Agents (FAs) of a multi‐agent system. The proposed Operationalized Intent Ontology provides a means of modeling human‐agent teams as an intent informed system. [ABSTRACT FROM AUTHOR]

We propose a Systems Modeling Language (SysML) extension, referred to as the "Human‐Agent Teaming Modeling Language," and a companion method, referred to as "Human‐Agent Teaming Design Method," which are useful in the design and specification of teams comprised of humans and artificial agents which interact within a system. The language and method support the analysis and design of human‐agent teams through a three‐step process. First, an existing system is analyzed to understand the goals to be achieved as well as the roles and the responsibilities necessary to support goal attainment. Simultaneously, a team of humans and artificial agents are synthesized wherein each entity has capabilities useful in fulfilling the responsibilities. Second, responsibilities are allocated to agents by matching agent capabilities to responsibilities. Finally, the design is vetted through the application of an extension to an existing method referred to as Interdependence Analysis. This step includes the specification of teaming behaviors between humans and artificial agents to improve system robustness. We posit that the proposed language and method can be useful in specifying requirements for artificial agents, human knowledge, and procedure requirements, as well as components of critical interaction between multiple humans and artificial agents comprising a human‐agent team. [ABSTRACT FROM AUTHOR]

Purpose Integrate statewide rankings of abuse across different drugs and/or signal detection systems to summarize prescription drug abuse in each state in 2007. Methods Four signal detection systems (Opioid Treatment Programs, Key Informants, Drug Diversion, and Poison Centers) that covered heterogeneous populations collected data on the abuse of nine opioids: hydrocodone, immediate-release oxycodone, tramadol, extended-release [ER] oxycodone, fentanyl, morphine, methadone, hydromorphone, and buprenorphine). We introduce here linearized maps which integrate nine drugs within each system; four systems for each drug; or all drugs and systems. Results When rankings were integrated across drugs, Rhode Island, New Hampshire, Maine, West Virginia, and Michigan were in the highest tertile of abuse in three systems. When rankings were integrated across signal detection systems, there was a geographic clustering of states with the highest rates for ER oxycodone (in Tennessee, Mississippi, Kentucky, Ohio, Indiana, Michigan, and in Massachusetts, New Hampshire, Maine, and Vermont) and methadone (Massachusetts, Rhode Island, New Hampshire, Maine, Vermont, Connecticut, and New Jersey). When rankings were integrated across both drugs and signal detection systems, states with 3-digit ZIP codes below 269 (i.e., from Massachusetts to West Virginia): Massachusetts, New Hampshire, Maine, Vermont, Washington DC, Virginia, and West Virginia were in the highest tertile and only Delaware was in the lowest tertile. Conclusions We have presented methods to integrate data on prescription opioid abuse collected by signal detection systems covering different populations. Linearized maps are effective graphical summaries that depict differences in the level of prescription opioid abuse at the state level. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The widely used Cox proportional hazards regression model for the analysis of censored survival data has limited utility when either hazard functions themselves are of primary interest, or when relative times instead of relative hazards are the relevant measures of association. Parametric regression models are an attractive option in situations such as this, although the choice of a particular model from the available families of distributions can be problematic. The generalized gamma (GG) distribution is an extensive family that contains nearly all of the most commonly used distributions, including the exponential, Weibull, log normal and gamma. More importantly, the GG family includes all four of the most common types of hazard function: monotonically increasing and decreasing, as well as bathtub and arc-shaped hazards. We present here a taxonomy of the hazard functions of the GG family, which includes various special distributions and allows depiction of effects of exposures on hazard functions. We applied the proposed taxonomy to study survival after a diagnosis of clinical AIDS during different eras of HIV therapy, where proportionality of hazard functions was clearly not fulfilled and flexibility in estimating hazards with very different shapes was needed. Comparisons of survival after AIDS in different eras of therapy are presented in terms of both relative times and relative hazards. Standard errors for these and other derived quantities are computed using the delta method and checked using the bootstrap. Description of standard statistical software (Stata, SAS and S-Plus) for the computations is included and available at http://statepi.jhsph.edu/software. [ABSTRACT FROM AUTHOR]

Summary 1.Because predators threaten the survival of endangered prey in many places, predator management is a widespread conservation tool. At the same time, the effects of predators on their prey are greatly influenced by landscape structure. Therefore, the management of landscapes could be an alternative to predator regulation. 2.A spatially explicit presence/absence model (a stochastic one-layer cellular automaton) was used to investigate two different predator–prey systems that were subject to changes in the number and size of habitat patches in a model landscape. 3.The first scenario included grey-sided voles Clethrionomys rufocanus , Norwegian lemmings Lemmus lemmus and small mustelids (stoats Mustela erminea and weasels M. nivalis ) interacting in a tundra landscape. In the second scenario, the effect of habitat perforation by human settlements with subsidized predators (house cats Felis silvestris catus ) on the dynamics of lemmings (as surrogate for endangered prey) was studied. 4.Both the total area of lemming habitat and the degree of fragmentation were important determinants of the population size and persistence of lemmings. A qualitative change in the effect of fragmentation was observed when the area of lemming habitat decreased from 70% (positive effect) to 50% (negative effect). When lemming habitat covered 50% or less of the landscape, fragmentation had a negative effect on lemming population size and viability, even though habitat area did not decrease. 5.The spatial configuration of settlements as predator sources was important. A few evenly spaced predator sources had less negative effect on lemming populations than the same proportion of predator habitat that was randomly distributed, which in turn had less effect than many evenly spaced patches. 6.Including predator management in the model did not decrease the predators’ negative impact on the population size and persistence of the endangered prey when settlements occurred in... [ABSTRACT FROM AUTHOR]