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Florida Institute for Human and Machine Cognition (IHMC)
Human-Centered Cognitive Orthoses: Artificial Intelligence for, Rather than Instead of, the People
Neuhaus, Peter (Florida Institute for Human and Machine Cognition (IHMC)) | Raj, Anil (Florida Institute for Human and Machine Cognition (IHMC)) | Clancey, William J. (Florida Institute for Human and Machine Cognition (IHMC))
This issue of AI Magazine includes six articles on cognitive orthoses, which we broadly conceive as technological approaches that amplify or enhance individual or team cognition across a wide range of goals and activities. The articles are grouped by how they relate to orthoses enhanced socio-technical team intelligence at three different cognitive levels--sensorimotor physical, professional learning, and networked knowledge.
Cognitive Orthoses: Toward Human-Centered AI
Ford, Kenneth M. (Florida Institute for Human and Machine Cognition (IHMC)) | Hayes, Patrick J. (Florida Institute for Human and Machine Cognition (IHMC)) | Glymour, Clark (Florida Institute for Human and Machine Cognition (IHMC)) | Allen, James (Florida Institute for Human and Machine Cognition (IHMC))
This introduction focuses on how human-centered computing (HCC) is changing the way that people think about information technology. The AI perspective views this HCC framework as embodying a systems view, in which human thought and action are linked and equally important in terms of analysis, design, and evaluation. This emerging technology provides a new research outlook for AI applications, with new research goals and agendas.
Human-Centered Cognitive Orthoses: Artificial Intelligence for, Rather than Instead of, the People
Neuhaus, Peter (Florida Institute for Human and Machine Cognition (IHMC)) | Raj, Anil (Florida Institute for Human and Machine Cognition (IHMC)) | Clancey, William J. (Florida Institute for Human and Machine Cognition (IHMC))
This issue of AI Magazine includes six articles on cognitive orthoses, which we broadly conceive as technological approaches that amplify or enhance individual or team cognition across a wide range of goals and activities. The articles are grouped by how they relate to orthoses enhanced socio-technical team intelligence at three different cognitive levels—sensorimotor physical, professional learning, and networked knowledge.
PIM: A Novel Architecture for Coordinating Behavior of Distributed Systems
Ford, Kenneth M. (Florida Institute for Human and Machine Cognition (IHMC)) | Allen, James (Florida Institute for Human and Machine Cognition (IHMC)) | Suri, Niranjan (Florida Institute for Human and Machine Cognition (IHMC)) | Hayes, Patrick J. (Florida Institute for Human and Machine Cognition (IHMC)) | Morris, Robert (Nasa Ames Research Center)
PIM: A Novel Architecture for Coordinating Behavior of Distributed Systems
Ford, Kenneth M. (Florida Institute for Human and Machine Cognition (IHMC)) | Allen, James (Florida Institute for Human and Machine Cognition (IHMC)) | Suri, Niranjan (Florida Institute for Human and Machine Cognition (IHMC)) | Hayes, Patrick J. (Florida Institute for Human and Machine Cognition (IHMC)) | Morris, Robert (Nasa Ames Research Center)
Process integrated mechanisms (PIM) offer a new approach to the problem of coordinating the activity of physically distributed systems or devices. Current approaches to coordination all have well-recognized strengths and weaknesses. We propose a novel architecture to add to the mix, called the Process Integrated Mechanism (PIM), which enjoys the advantages of having a single controlling authority while avoiding the structural difficulties that have traditionally led to its rejection in many complex settings. In many situations, PIMs improve on previous models with regard to coordination, security, ease of software development, robustness and communication overhead. In the PIM architecture, the components are conceived as parts of a single mechanism, even when they are physically separated and operate asynchronously. The PIM models offers promise as an effective infrastructure for handling tasks that require a high degree of time-sensitive coordination between the components, as well as a clean mechanism for coordinating the high-level goals of loosely coupled systems. PIM models enable coordination without the fragility and high communication overhead of centralized control, but also without the uncertainty associated with the system-level behavior of a MAS.The PIM model provides an ease of programming with advantages over both multi-agent sys-tems and centralized architectures. It has the robustness of a multi-agent system without the significant complexity and overhead required for inter-agent communication and negotiation. In contrast to centralized approaches, it does not require managing the large amounts of data that the coordinating process needs to compute a global view. In a PIM, the process moves to the data and may perform computations on the components where the data is locally available, sharing only the information needed for coordination of the other components. While there are many remaining research issues to be addressed, we believe that PIMs offer an important and novel tech-nique for the control of distributed systems.