IEEE Systems, Man and Cybernetics Magazine - April 2022 - 10

doctrine, and process endeavors in an IAS design,
development, validation, certification, and exploitation
lifecycle.
The Emergence of Brain-Inspired IASs
It is commonly assumed that autonomous systems toward
general AI may be achieved when brain-inspired cognitive
capabilities for decision making are available to exhibit
human-like behaviors of both intelligence and adaptability.
Recent theoretical advances and technological convergence
in cybernetics, system science, cognitive informatics,
and augmented cognition offer possibilities of a
brain-inspired and knowledge-based IAS that harnesses
leap-ahead technologies, such as the next generation of
computing (quantum computing and cognitive computing)
[9], [16]-[18]. Thus, a machine is able to " think " like
humans to improve decision making and generate anticipatory
intelligence by mimicking the neurological processes
of the human brain. With the collective human-machine
intelligence, an IAS is able to effectively draw inferences
from data, including those that are incomplete, uncertain,
or ambiguous, and learn from experience and users' interaction
responses.
An IAS is capable of changing its behavior in real time
as a function of a user cognitive state and the status of the
task, machine, and world (working environment). By optimizing
human-machine interactions, an IAS may intelligently
adapt to the capabilities, capacities, limitations,
needs, and demands of a user and machine to attain and
maintain safety, trust, effectiveness, and efficiency when
humans perform various cognitively challenging tasks in
complex and dynamic environments [9].
The Architectural Framework of IASs
To achieve IAS objectives for its broad range of applications,
a system needs to exhibit at least five fundamental
characteristics: 1) tracking goals, plans, or intents, and the
progress toward them; 2) monitoring and inferring the
internal state of a user (behavioral, physical, cognitive,
and emotional); 3) monitoring and inferring the external
status of the world (environmental conditions, entities, and
domain constraints); 4) monitoring the effects of machine
status, automation, advice, and adaptation on user and
world status (closed-loop feedback); and 5) customizing its
human-machine interface (HMI), including a brain-
machine interface to handle the interactions and trust
relations between a user and a machine. To manifest these
characteristics, a conceptual architectural framework has
been developed as a basic IAS anatomy with critical components
common to knowledge-based IASs [9].
As illustrated in Figure 1, a generic IAS includes four
modules pertaining to situation assessment, user state
assessment, adaptation engine, and HMI. There are several
knowledge models to support each of these modules.
Situation Assessment
This module is concerned with the assessment of the " situation "
and comprises functionality relating to the real-time
analysis of the activities (required to achieve a specific
goal), automation, and decision support. The module
Interaction Model
Knowledge Model
Task Model, Machine Model, World Model
Situation
Assessment
Compare
HMI
Adaptation
Engine
User State
Assessment
Autonomous Intelligent
Behavior Generation
Trust Model
User Model (Behavioral, Contextual,
Neurophysiological, and Psychophysiological States)
Figure 1. The architectural framework of a generic IAS with the four modules and their supporting models.
10 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE April 2022
IEEE Systems, Man and Cybernetics Magazine - April 2022

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