Florida Institute for Human and Machine Cognition IHMC

Sewall's Point, FL, United States

Florida Institute for Human and Machine Cognition IHMC

Sewall's Point, FL, United States
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Boy G.A.,Florida Institute of Technology | Bradshaw J.M.,Florida Institute for Human and Machine Cognition IHMC | Yi S.,International Space University
Conference on Human Factors in Computing Systems - Proceedings | Year: 2015

This storytelling course will bring, in meaningful terms, insightful concepts, methods and tools that are used in the air and space domains. HCI for complex engineered systems challenges conventional HCI solutions to propose new kinds of approaches that turn out to be very useful for solving HCI complex problems. Participants will design devices usable on Earth using accumulated knowledge and tips from aerospace experience. Creativity, in the sense of synthesis and integration, and design thinking will be at the center of this course, where participants will learn how to state and solve a complex design problem, and deliver the resulting product.


Smets N.J.J.M.,TNO | Van Diggelen J.,TNO | Neerincx M.A.,Technical University of Delft | Bradshaw J.M.,Florida Institute for Human and Machine Cognition IHMC | And 5 more authors.
IEEE Intelligent Systems | Year: 2010

Running computer simulations of work practices early on lets researchers test humanagent teams in dangerous, complex environments by incrementally increasing fidelity, adding realistic features, and incorporating human participants. © 2010 IEEE.


Suri N.,Florida Institute for Human and Machine Cognition IHMC | Suri N.,U.S. Army | Scott A.C.,Lancaster University
Proceedings - 2014 IEEE 8th International Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW 2014 | Year: 2014

The Collective Adaptive Systems problem is particularly challenging when applied to resource allocation and resource coordination in wireless tactical networks. This paper attempts to characterize the problem in detail in an incremental manner, starting with the simplest version of the problem that includes many assumptions and then building up the complexity of the problem by removing the assumptions. The objective is for researchers to be able to understand the full complexity and subtleties of the problem and to provide a common language for discussing the problems, assumptions, and solutions. © 2014 IEEE.


Casini E.,Florida Institute for Human and Machine Cognition IHMC | Suri N.,Florida Institute for Human and Machine Cognition IHMC | Suri N.,U.S. Army | Bradshaw J.M.,Florida Institute for Human and Machine Cognition IHMC
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

The popularity of cloud computing along with the increased availability of cheap storage have led to the necessity of elaboration and transformation of large volumes of open-source data, all in parallel. One way to handle such extensive volumes of information properly is to take advantage of distributed computing frameworks like Map-Reduce. Unfortunately, an entirely automated approach that excludes human intervention is often unpredictable and error prone. Highly accurate data processing and decision-making can be achieved by supporting an automatic process through human collaboration, in a variety of environments such as warfare, cyber security and threat monitoring. Although this mutual participation seems easily exploitable, human-machine collaboration in the field of data analysis presents several challenges. First, due to the asynchronous nature of human intervention, it is necessary to verify that once a correction is made, all the necessary reprocessing is done in chain. Second, it is often needed to minimize the amount of reprocessing in order to optimize the usage of resources due to limited availability. In order to improve on these strict requirements, this paper introduces improvements to an innovative approach for human-machine collaboration in the processing of large amounts of open-source data in parallel. © 2015 SPIE.


Bradshaw J.M.,Florida Institute for Human and Machine Cognition IHMC | Dignum V.,Technical University of Delft | Jonker C.M.,Technical University of Delft | Sierhuis M.,Agent ISolutions
HRI'12 - Proceedings of the 7th Annual ACM/IEEE International Conference on Human-Robot Interaction | Year: 2012

Teamwork has become a widely accepted metaphor for describing the nature of multi-robot and multi-agent cooperation. By virtue of teamwork models, team members attempt to manage general responsibilities and commitments to each other in a coherent fashion that both enhances performance and facilitates recovery when unanticipated problems arise. Whereas early research on teamwork focused mainly on interaction within groups of autonomous agents or robots, there is a growing interest in leveraging human participation effectively. Unlike autonomous systems designed primarily to take humans out of the loop, many important applications require people, agents, and robots to work together in close and relatively continuous interaction. For software agents and robots to participate in teamwork alongside people in carrying out complex real-world tasks, they must have some of the capabilities that enable natural and effective teamwork among groups of people. Just as important, developers of such systems need tools and methodologies to assure that such systems will work together reliably and safely, even when they have been designed independently. The purpose of the HART workshop is to explore theories, methods, and tools in support of humans, agents and robots working together in teams. Position papers that combine findings from fields such as computer science, artificial intelligence, cognitive science, anthropology, social and organizational psychology, human-computer interaction to address the problem of HART are strongly encouraged. The workshop will formulate perspectives on the current state-of-the-art, identify key challenges and opportunities for future studies, and promote community-building among researchers and practitioners. The workshop will be structured around four two-hour sessions on themes relevant to HART. Each session will consist of presentations and questions on selected position papers, followed by a whole-group discussion of the current state-of-the-art and the key challenges and research opportunities relevant to the theme. During the final hour, the workshop organizers will facilitate a discussion to determine next steps. The workshop will be deemed a success when collaborative scientific projects for the coming year are defined, and publication venues are explored. For example, results from the most recent HART workshop (Lorentz Center, Leiden, The Netherlands, December 2010) will be reflected in a special issue of IEEE Intelligent Systems on HART that is slated to appear in January/February 2012. © 2012 Authors.


Bradshaw J.M.,Florida Institute for Human and Machine Cognition IHMC | Forsythe J.C.,Sandia National Laboratories
HRI'12 - Proceedings of the 7th Annual ACM/IEEE International Conference on Human-Robot Interaction | Year: 2012

This tutorial provides a synopsis of key findings and theoretical advances from cognitive science and socio-cognitive theory, with examples of how the results of this research can be applied to the design of human-robotic systems. Topics covered will run the gamut from basic cognitive science (e.g., perception, attention, learning and memory, information processing, multi-tasking, conscious awareness, individual differences) to socio-cognitive issues (e.g., theories of social interaction, dynamic functional allocation, mixed-initiative interaction, human-agent-robot teamwork, coactive design, theory of organizations). Additionally, the tutorial will address new technologies that attempt to leverage the current state of theory (e.g., neuroergonomics, brain-machine interfaces, detection of cognitive states, robotic prostheses and orthotics, cognitive and sensory prostheses). Throughout the tutorial, the presenters will give descriptions and demonstrations of working systems that exemplify the principles being taught. Separately, the presenters have given highly-successful tutorials on relevant subjects at workshops and conferences such as CHI and HCI International, as well as in a variety of industrial and government settings. In this tutorial, they propose to bring together their experience to bear on issues of specific interest to the HRI community. © 2012 Authors.


Galescu L.,Florida Institute for Human and Machine Cognition IHMC | Blaylock N.,Florida Institute for Human and Machine Cognition IHMC
IHI'12 - Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium | Year: 2012

Clinical reports often include descriptions of events in the patient's medical history, as well as explicit or implicit temporal information about these events. We are working towards applying deep Natural Language Processing tools towards understanding such narratives. This requires both the extraction and classification of the relevant events, and the placing of those events in time, or at least in relation to one another. Although several corpora of news data exist that have been annotated using the TimeML schema, similar corpora of clinical reports are not readily available. In this paper we report on the design of a small corpus and the annotation schema we developed, based on data from the fourth i2b2/VA challenge. These data include, among others, annotations for medical problems, tests, and treatments in clinical reports from several healthcare institutions. We have selected a subset of clinical reports and added annotations similar to those used in the TempEval tasks for the annotation of events, time expressions and temporal relations for the news domain. The annotations have been made freely available to the research community. Copyright © 2012 ACM.


Bradshaw J.M.,Florida Institute for Human and Machine Cognition IHMC
International Journal of Human Computer Studies | Year: 2013

In the mid-1980s, Brian Gaines first developed a model to predict the trajectory of progress in human-computer relationships, including how the knowledge science research programme would naturally transform itself over time into something he called symbiosis science. In this article, we reflect both on the extraordinary prescience of this model, and the contributions and challenges faced by researchers intent on progressive achievement toward the aspirations it inspires. © 2012 Jeffrey M. Bradshaw.


Atkinson D.J.,Florida Institute for Human and Machine Cognition IHMC | Clark M.H.,Florida Institute for Human and Machine Cognition IHMC
AAAI Spring Symposium - Technical Report | Year: 2013

There is a recognized need to employ autonomous agents in domains that are not amenable to conventional automation and/or which humans find difficult, dangerous, or undesirable to perform. These include time-critical and mission-critical applications in health, defense, transportation, and industry, where the consequences of failure can be catastrophic. A prerequisite for such applications is the establishment of well-calibrated trust in autonomous agents. Our focus is specifically on human-machine trust in deployment and operations of autonomous agents, whether they are embodied in cyber-physical systems, robots, or exist only in the cyber-realm. The overall aim of our research is to investigate methods for autonomous agents to foster, manage, and maintain an appropriate trust relationship with human partners when engaged in joint, mutually interdependent activities. Our approach is grounded in a systems-level view of humans and autonomous agents as components in (one or more) encompassing meta-cognitive systems. Given human predisposition for social interaction, we look to the multi-disciplinary body of research on human interpersonal trust as a basis from which we specify engineering requirements for the interface between human and autonomous agents. If we make good progress in reverse engineering this "human social interface," it will be a significant step towards devising the algorithms and tests necessary for trustworthy and trustable autonomous agents. This paper introduces our program of research and reports on recent progress. © 2013, Association for the Advancement of artificial intelligence.


Blaylock N.,Florida Institute for Human and Machine Cognition IHMC
Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011 | Year: 2011

In this paper, we describe the PURSUIT Corpus- an annotated corpus of geospatial path descriptions in spoken natural language. PURSUIT includes the spoken path descriptions along with a synchronized GPS track of the path actually taken. Additionally, we have manually annotated geospatial entity mentions in PURSUIT, mapping them onto point entries in several geographic information system databases. PURSUIT has been made freely available for download. © 2011 IEEE.

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