rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: INTEGRATING ARTIFICIAL AND HUMAN INTELLIGENCE THROUGH AGENT ORIENTED SYSTEMS DESIGN
  name:
    name part: Miller, Michael E.
    role:
      role term: creator
  name:
    name part: Rusnock, Christina F.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    place:
      place term: S.l.]
    publisher: CRC PRESS
    date issued: 2024
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: This book departs from the assumption that Artificial Intelligence (AI) systems will provide a maximum advantage by replacing human cognitive processing. Instead, this book subscribes to the assumption that AI systems will provide a maximal advantage when the system is specifically designed to augment human intelligence. It provides methods for designing effective systems that include one or more humans and one or more AI entities and uses the approach that assumes automation does not replace human activity but fundamentally changes the structure of human work when AI is added to existing systems. Integrating Artificial and Human Intelligence through Agent Oriented Systems Design discusses the potential impact of AI on human work and life and explores why teamwork is necessary today for complex work environments. The book explains the processes and methods humans employ to effectively team with one another and presents the elements of artificial agents that permit them to function as team members in joint human and artificial teams. It discusses design goals and illustrates how the methods that have been used to model the complex interactions among human and artificial agents can be expanded to enable the design of interaction between them to make possible the attainment of the shared goals. Model-Based Systems Engineering (MBSE) tools that provide logical designs of human-agent teams, the AI within these teams, training to be deployed for human and artificial agent team members, and the interfaces between human and artificial agent team members are all covered. MBSE files containing profiles and examples for building MBSE models used in the design approach are featured on the author's website (https://lodesterresci.com/hat). This book is an ideal read for students, professors, engineers, and project managers associated with designing and developing AI systems or systems that seek to incorporate AI.
  note: Michael E. Miller is a Professor of Systems Engineering at the Air Force Institute of Technology where he teaches courses in human factors, human performance modeling, and human systems integration, including a course in Human-Agent Teaming. Mike's recent research has focused on human representations in systems engineering models, including a SysML extension for developing descriptive models of systems which include small teams of humans and agents working to seek common goals. Mike has contributed to more than 100 issued U.S. patents on digital imaging and display systems, 50 peer-reviewed journal articles, and numerous conference proceedings. Lt Col Christina F. Rusnock is an Adjunct Associate Professor of the Department of Systems Engineering and Management at the Air Force Institute of Technology, Wright Patterson AFB, OH. Dr. Rusnock earned her Ph.D. in Industrial Engineering, Human Factors/Ergonomics Specialization, from the University of Central Florida, and an MS in Research and Development Management from the Air Force Institute of Technology. Her research interests include human performance modeling, mental workload, trust in automation, and situation awareness, with a focus on applications in human-machine teaming for autonomous systems. She has contributed to over 50 books, journal articles, and conference proceedings.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Intelligence artificielle
  subject:
    topic: artificial intelligence
  subject:
    topic: COMPUTERS / Artificial Intelligence
  classification: Q335
  classification: 006.3
  related item:
    identifier: (OCoLC)1425238134
  identifier: 9781040111741
  identifier: 1040111742
  identifier: 9781003428183
  identifier: 1003428185
  identifier: 9781040111758
  identifier: 1040111750
  identifier: 1032546573
  identifier: 9781032546575
  identifier: 9781003428183 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 240710
    record change date: 20241108165918.1
    record identifier: in00005524047
    language of cataloging:
      language term: eng