wilamowski-b-m-irwin-j-d-industrial-communication-systems-2011

Processing Data in Complex Communication Systems 68-9
describe their behavior. In layer 5, the system learns about trajectories of symbols. Typical paths through
the view of a sensor node are stored. Layer 6 manages the communication to other nodes and establishes
a global world view. The trajectories are also used to correlate observations between neighboring nodes.
In layer 7, the recognition of unusual behavior and events takes place using two approaches: the first one
compares current observations with learned models by calculating probabilities of occurrence of the
observations with respect to their position, velocity, and direction. It also calculates the probabilities of
the duration that symbols remain in an area, probabilities of the movement along trajectories, including
trajectories across nodes. Observations with probabilities below defined thresholds raise “unusual
behavior” alarms. The second part of this layer is concerned with the recognition of predefined scenarios
and the creation of alarms in case predefined threat conditions are met. Finally, layer 8 is responsible
for the communication to the user. It generates alarm or status messages and filters them if particular
conditions would be announced too often or the same event is recognized by both methods in layer 7.
68.7 the Human Mind as an Archetype
for Cognitive Automation
A long-awaited breakthrough in technology is the ability of machines to operate in an everyday human
environment. Being able to sense the world in which they are immersed, robots may act in a way that is
useful for human users. Decades ago, automation system designers started to follow the path that literature
had drafted: intelligence and perception go hand in hand.
Thus, very simple devices can only carry out tasks that do not require sensing the real world. For instance,
moving a certain piece 5.cm ahead in the conveyor belt. In a way, this is like driving blind a car: if the piece
falls off the belt, the device won’t know the reason and, therefore, won’t be able to find a proper answer.
Obviously, more complex activities demand more complex devices and, in such cases, sophisticated
perception is the turning point that allows automation systems to collect the information needed to
control their actions and the consequences of them.
In the following section, we will give an overview on how machine perception has been addressed in
the past, and what are promising approaches for perception in future automation systems in order to be
able to fulfill useful tasks in more general environments—as humans can.
68.7.1 Perception in Automation: A Historic Overview
The term perception has been used in computer and automation systems from the 1950s onwards, since
the foundation of AI. It means acquiring, interpreting, selecting, and organizing sensory information.
The topic itself was not new to automation, but has gained a new quality from the moment information
processing could be separated from energy flow and performed in completely new ways.
The foreseen timeframe for revolutionary, human-like applications in AI (sometimes referred to as artificial
general intelligences) has been estimated to be roughly 10 years—for these last 60 years. Already in
early phases of research, engineers, psychologists, and neuropsychiatrists cooperated in order to exploit
synergies and achieve mutually fruitful results. Unfortunately, it soon turned out that loose couplings
between scientists from different fields would not be enough to find comprehensive solutions. In 1961,
E.E. David wrote in his introductory words for an issue of the Transactions on Information Theory [Dav61]:
Not to say that cross-fertilization of engineering and the life-sciences should be scorned. But
there must be more to these attempts than merely concocting a name, generating well-intentioned
enthusiasm, speculating with the aid of brain-computer analogies, and holding symposia packed
with “preliminary” results from inconclusive experiments. A bona fide “interdiscipline” draws its
vitality from people of demonstrated achievement in the contributing disciplines, not from those
who merely apply terminology of one field to another.
© 2011 by Taylor and Francis Group, LLC