Search

As the power of Artificial Intelligence (AI) continues to advance, there is increased interest in how best to combine AI-based agents with humans to achieve mission effectiveness. Three perspectives have emerged. The first stems from more conventional human factors traditions and views these entities as highly capable tools that humans can use to accomplish increasingly sophisticated tasks. The second "camp" believes that as the sophistication of these entities increases, it becomes increasingly appropriate to talk about them as "teammates" and use the research on human teams as a foundation for further exploration. The third perspective is emerging and finds both the "tools" and "teammate" metaphors flawed and limiting. This perspective emphasizes "joint activity," "joint cognitive activity," or something similar. In this article, we briefly review these three perspectives.

Sonalysts is working on an initiative to expand our current expertise in teaming to Human-Artificial Intelligence (AI) teams by developing original research in this area. To provide a foundation for that research, Sonalysts is investigating the development of a Synthetic Task Environment (STE). In a previous report, we documented the findings of a recent outreach effort in which we asked military Subject Matter Experts (SMEs) and other researchers in the Human-AI teaming domain to identify the qualities that they most valued in a testbed. A surprising finding from that outreach was that several respondents recommended that our team look into existing human-AI teaming testbeds, rather than creating something new. Based on that recommendation, we conducted a systematic investigation of the associated landscape. In this report, we describe the results of that investigation. Building on the survey results, we developed testbed evaluation criteria, identified potential testbeds, and conducted qualitative and quantitative evaluations of candidate testbeds. The evaluation process led to five candidate testbeds for the research team to consider. In the coming months, we will assess the viability of the various alternatives and begin to execute our program of research., Comment: June 2023, Drs. Asiala and McCarthy are affiliated with Sonalysts, Dr. Huang is affiliated with Arizona State University

A consensus report produced for the Air Force Research Laboratory (AFRL) by the National Academies of Sciences, Engineering, and Mathematics documented a prevalent and increasing desire to support human-Artificial Intelligence (AI) teaming across military service branches. Sonalysts has begun an internal initiative to explore the training of Human-AI teams. The first step in this effort is to develop a Synthetic Task Environment (STE) that is capable of facilitating research on Human-AI teams. Our goal is to create a STE that offers a task environment that could support the breadth of research that stakeholders plan to perform within this domain. As a result, we wanted to sample the priorities of the relevant research community broadly, and the effort documented in this report is our initial attempt to do so. We created a survey that featured two types of questions. The first asked respondents to report their agreement with STE features that we anticipated might be important. The second represented open-ended questions that asked respondents to specify their priorities within several dimensions of the anticipated STE. The research team invited nineteen researchers from academic and Government labs to participate, and 11 were able to complete the survey. The team analyzed their responses to identify themes that emerged and topics that would benefit from further analysis. The most significant finding of the survey was that a number of researchers felt that various open-source STEs that would meet our needs already exist. Researchers also emphasized the need for automated transcription and coding tools to ease the burden of assessing inter-team communications; the importance of robust data capture and export capabilities; and the desirability of extensive flexibility across many aspects of the tool., Comment: March 2023

A consensus report produced for the Air Force Research Laboratory by the National Academies of Sciences, Engineering, and Mathematics documented a prevalent and increasing desire to support human-Artificial Intelligence (AI) teaming across military service branches. Sonalysts has begun an internal initiative to explore the training of human-AI teams. The first step in this effort is to develop a Synthetic Task Environment (STE) that is capable of facilitating research on human-AI teams. We decided to use Joint All-Domain Command and Control (JADC2) as a focus point for developing the STE because the volume of sensor inputs and decision options within the JADC2 concept likely requires the use of AI systems to enable timely decisions. Given this focus, we engaged a number of Subject-Matter Experts (SMEs) with Command and Control experience to gain insight into developing a STE that embodied the teaming challenges associated with JADC2. This report documents our initial engagement with those stakeholders. The research team identified thirteen Sonalysts employees with military backgrounds and Command and Control experience, and invited them to participate. Twelve respondents completed the survey. The team then analyzed the responses to identify themes that emerged and topics that would benefit from further analysis. The results indicated that our SMEs were amenable to research using tasks that were analogous to those encountered in military environments, as long as they required teams to process a great deal of incoming data to arrive at complex decisions. The SMEs felt that the testbed should support 'teams of teams" that represented a matrixed organization, and that it should support a robust array to spoken, text-based, and face-to-face communications., Comment: March 2023

Electronic performance support systems (EPSS) help users accomplish tasks, using computational technologies. Scaffolding is the process through which efforts are supported while engaging a learning or performance task. A number of different types of scaffolds are possible, including conceptual, metacognitive, procedural, and strategic. Each of these types of scaffolding is defined in this paper, and a case study involving the application of different scaffolding approaches is presented. The Tactical Readiness Instruction, Authoring, and Delivery (TRIAD) project is developing a set of authoring and delivery tools that will enhance the quality of tactical guidance disseminated through the United States Navy. The bulk of requisite knowledge and skill is developed through experience and personal study of tactical publications (including Tactical Memoranda, or TACMEMOs) and combat system doctrine. TRIAD is a PC-based system being designed and developed to improve the coherence and usability of TACMEMOs. TRIAD will provide authors with an integrated tool set to enable them to create tactical documentation using a variety of multimedia presentation techniques, and to create associated interactive multimedia instruction to support the documented tactic/doctrine. In turn, readers will receive a multimedia tactical documentation "product set" that supports tactic/doctrine presentation and briefing, instruction, quick reference, and facilitation of electronic feedback regarding tactic/doctrine evaluation. TRIAD's online author interview scaffolding of TACMEMOs is emphasized. (Contains 13 references.) (AEF)

R45451 April 18, 2019 Review and rollback of Clean Air Act rules to regulate greenhouse gas (GHG) emissions from power plants, cars and trucks, and the oil and gas sector [...]

Updated August 13, 2018 Contents Introduction EPA's Greenhouse Gas Regulations Standards for Motor Vehicles Standards for Power Plants (Clean Power Plan) Standards for the Oil and Gas Industry Air Quality [...]

Updated December 19, 2017 Contents Background Q: Why did EPA promulgate the Clean Power Plan? Q: How much progress has the United States made in reducing GHG emissions and meeting [...]

Updated October 25, 2017 Contents Background Executive Order 13783 Administrative Procedures for Revising or Repealing a Rule What EPA Proposed Going Forward Figures Figure 1. GHG Emissions from Electric Power [...]

Updated February 8, 2017 Contents Introduction Massachusetts v. EPA and Its Effects Motor Vehicle GHG Emissions Light-Duty Motor Vehicles MY2012-MY2016 Standards MY2017-MY2025 Standards and Mid-Term Evaluation Medium and Heavy-Duty Trucks [...]

Updated December 30, 2016 Contents Introduction Has the Obama Administration's EPA Overreached? Conflicting Views What This Report Does A Few Caveats Regarding Timing Congressional Activity Conclusions Organization of the Remainder [...]

Updated December 21, 2016 Contents Introduction EPA's Greenhouse Gas Regulations GHG Emission Standards for Motor Vehicles GHG Emission Standards for Power Plants Legislative and Judicial Actions Emissions of Other Pollutants [...]

November 2, 2016 In 1977, major amendments to the Clean Air Act (CAA) included provisions for the prevention of significant deterioration (PSD) of air quality. The PSD provisions were enacted [...]

Updated November 18, 2016 Contents Introduction Issues for Congress Legislative Initiatives Administrative Initiatives The Obama Administration's Strategy to Reduce Methane Emissions Methane: A Primer Emissions Historical Trends Source Sectors and [...]

Updated September 27, 2016 Contents Introduction Final Rule Highlights State Plan Requirements and Options Federal Implementation Plan Timing Requirements for State Targets National Performance Standards State-Specific Targets EPA's Methodology National [...]

Pollution Prevention Act of 1990 (63) The Pollution Prevention Act of 1990 requires the Environmental Protection Agency to establish an Office of Pollution Prevention, develop and coordinate a pollution prevention [...]

Contents Introduction EPA's Greenhouse Gas Regulations Emissions of Other Pollutants from Power Plants Cross-State Air Pollution / Clean Air Interstate Rule (CAIR) North Carolina v. EPA EPA's CAIR Replacement: The [...]