Systems, Man & Cybernetics - October 2015 - 9
respond to internal and environmental changes. Feedback is a key feature of such systems because it enables
appropriate responses to change. Artificial systems can
be made adaptive by using feedback to sense new conditions in the environment and then adjusting accordingly.
ACSs can find applications in almost all industrial sectors, particularly in aerospace, automotive, manufacturing, and management. Adaptive collaboration (AC) can
be realized through the promising architecture and process of role-based collaboration (RBC) [21]. RBC is a
computational methodology that uses roles [21] as primary underlying mechanisms to facilitate collaboration.
RBC has been developed into a methodology of discovery in the research of collaboration systems, because it
takes advantage of formalizations and abstractions of
system components through mathematical expressions.
Problem instances of such abstractions are easily found
in real-world scenarios.
Related Work
Various aspects of adaptive systems have been investigated for a long time, though it is impossible to list all
of the related contributions in this article. The work
mentioned here contributes overall reviews and general
methodologies [1]-[5], [8]-[14]. Traditional research
concentrates on the general model and design of an
adaptive system. Recent multiagent systems advocate
applying autonomous agents to provide for adaptations
of a system. Holland proposes that adaptive systems
should learn from the immune systems of a human
body and apply parallel computing to conduct simulations for adaptive systems [2], [3]. Recent research work
that includes autonomic and self-managing systems
emphasizes self-* properties [4], [9], [13]; i.e., a system
should: know itself and be aware of its state and its
behaviors (self-awareness); configure and reconfigure
itself under varying and unpredictable conditions (selfconfiguring); detect suboptimal behaviors and optimize itself to improve its execution (self-optimizing);
detect and recover from potential problems and continue to function smoothly (self-healing); and detect and
protect its resources from both internal and external
attacks while maintaining overall system security and
integrity (self-protection). Weyns et al. [14] propose a
model called FORMS that is claimed to possess the
abilities to describe and reason on context-awareness,
self-awareness, and coordination in a distributed setting, as well as possess capabilities for extension and
refinement. De Lemos et al. [8] summarize the state of
the art and limitations of adaptive systems and identify
critical challenges for engineering self-adaptive software systems. Salehie and Tahvildari [11] thoroughly
surveyed adaptive software systems and concluded that
no existing system before the year of their publication
offers all the self-* properties. Compared with conventional adaptive systems, ACSs provide support for all
possible self-* properties through the use of arole
engine (see the section "The Architecture and the Self-*
Properties of an ACS").
From the viewpoint of conventional agent systems, it
is essential for an agent to be adaptive within a team. In
such a system, every agent within the team tries to contribute at their highest potential. Ultimately such an
objective will require individual agents to adapt and
change their existing behavior. That is why traditional
research on adaptation focuses on the adaptability of
individual agents [1], [13]. However, true AC concentrates on the adaptability of the team as a whole. This
article proposes the investigation of a new way to
establish adaptive systems from the viewpoint of collaboration of a group of agents regulated by roles in an
environment that is composed of classes of objects, i.e.,
env ironments, classes, agents, roles, groups, and
objects (E-CARGO). ACSs seek maximum performance
while maintaining all of the self-* proper ties. We
emphasize that in this article, self refers to an ACS. The
proposed ACSs do not intend to replace existing models
and methodologies but will contribute to the literature
of adaptive systems.
Role-Based Collaboration
and the E-CARGO Model
As a computational methodology, RBC provides well-specified concepts, processes, and algorithms [20], [21]. This
methodology can be used to discover, model, understand,
and solve real-world problems with the help of computerbased systems. RBC was proposed in 2003 [21] and has
been the subject of research and investigation for more
than a decade [12], [15]-[18], [20]-[22]. RBC can be illustrated as a solution framework for complex tasks, and the process of RBC is shown in Figure 1. E-CARGO is the
fundamental model for RBC. Within the E-CARGO model
[8]-[16], a system R can be described as a 9-tuple R ::= , where C is a set of classes, O is a
set of objects, A is a set of agents, M is a set of messages,
R is a set of roles, E is a set of environments, G is a set
of groups, s0 is the initial state of the system, and H is a
set of users. In such a system, A and H as well as E and
G are tightly-coupled sets. A human user and his or her
agent play a role together. Every group should work in an
environment, one that regulates a group. In the formalization, we use N to denote the set of natural numbers or,
more exactly, nonnegative integers, i.e., {0, 1, 2, 3,...}. In the
following discussions, we concentrate on a group and use
nonnegative integers m ^= A h to express the size of the
agent set A, n ^= R h the size of the role set R, i, i1, i2, ...,
the indices of agents, and j, j1, j2, ..., the indices of roles. To
save space, we ignore the definitions of object and class that
have been well accepted in academia and industry [19].
◆ Definition 1: A role [15]-[18], [20], [21] is defined as r
::= , where: id is
the identification of the role; M r denotes a set of
O c tob e r 2015
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Table of Contents for the Digital Edition of Systems, Man & Cybernetics - October 2015
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