applying computational intelligence volume 5969 || artificial vs. computational intelligence
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Artificial vs. Computational Intelligence
There are three great events in history. One, the creationof the universe. Two, the appearance of life. The thirdone, which I think is equal in importance, is the appear-ance of Artificial Intelligence.
Artificial Intelligence is not a science.Maurizio Matteuzzi
Hardly any area of research has created such wide publicity and controversy as
Artificial Intelligence (AI). To illustrate, the abbreviation AI is one of the few
scientific buzzwords accepted in contemporary English language. Artificial intelli-
gence has successfully penetrated Hollywood and even grabbed the attention of star
director Steven Spielberg. His blockbuster movie AI spread the artistic vision of theconcept to millions.
Considering the enormous popularity of AI, the reader may ask the logical
question: Why would we need a different name? The first chapter objective is to
answer this question by defining the fields of artificial and computational intelli-
gence and clarifying their differences. In both cases, the focus will be on the
application capabilities of these broad research areas. The chapter introduces the
most important techniques and application areas of classical AI.1 The key methods
and application issues of computational intelligence are discussed in detail in the
rest of the book. If a reader is not interested in the technical nature of AI, she/he can
skip Sect. 1.1.
1the state of AI before the appearance of computational intelligence.
A.K. Kordon, Applying Computational Intelligence,DOI 10.1007/978-3-540-69913-2_1,# Springer-Verlag Berlin Heidelberg 2010
1.1 Artificial Intelligence: The Pioneer
The enormous popularity of AI has its price. The perception of the field from
different research communities is very diverse and the opinions about its scientific
importance vary from one extreme of treating AI as low as contemporary alchemyto another extreme of glorifying its impact as one of the key events in history. The
reason for this controversy is the complex nature of the key subject of AI human
intelligence. AI research efforts are focused on analyzing human intelligence using
the methods of computer science, mathematics, and engineering. The classical
definition of AI, according to The Handbook of Artificial Intelligence is as follows:
Artificial Intelligence is the part of computer science concerned with designing intelligent
computer systems, that is, systems that exhibit the characteristics we associate with intelli-
gence in human behavior understanding language, learning reasoning, solving problems,
and so on.2
Meanwhile, human intelligence is a very active research area of several human-
ity-based scientific communities, such as neurophysiology, psychology, linguistics,
sociology, and philosophy, to name a few. Unfortunately, the necessary collabora-
tion between the computer science-based and humanity-based research camps is
often replaced by the fierce exchange of scientific artillery fire, which does not
resolve the issues but increases the level of confusion. There is a common percep-
tion inside and outside the academic community that the research efforts on
analyzing intelligence have not been handled in a very intelligent way.
1.1.1 Practical Definition of Applied Artificial Intelligence
As a result of the academic schisms, different definitions about the nature of human
and artificial intelligence are fighting for acceptance from the research community
at large. The interested reader could find them in the recommended references at the
end of the chapter. Since our focus is on value creation, we will pay attention to
those features of AI that are important from an implementation point of view.
The accepted birth of AI is the famous two-month workshop at Dartmouth
College during the summer of 1956 when ten founding fathers defined the initial
scientific directions in this new research area imitating human reasoning, under-
standing language (machine translation), finding a General Problem Solver, etc. For
many reasons, however, these generic directions did not produce the expected
results in the next decade even with very generous funding from several govern-
mental agencies in different countries. One of the lessons learned was that the initial
strategy of neglecting domain knowledge (the so-called weak methods for buildinggeneral-purpose solutions valid for any problems) failed. Research efforts have been
2A. Barr and E. Feigenbaum, The Handbook of Artificial Intelligence, Morgan Kaufmann, 1981.
4 1 Artificial vs. Computational Intelligence
redirected to developing reasoning mechanisms based on narrow areas of expertise.
It turns out that this approach not only delivered successful scientific results in the
early 1970s but opened the door to numerous practical applications in the 1980s.
From the practical point of view, the most valuable feature of human intelligence
is the ability to make correct predictions about the future based on all available data
from the changing environment. Usually, these predictions are specific and limited
to some related area of knowledge. From that perspective, we define applied AI as asystem of methods and infrastructure to mimic human intelligence by representingavailable domain knowledge and inference mechanisms for solving domain-specificproblems. In a very elementary way we can describe this narrow-focused definitionof AI as Put the expert in the box (computer), and we visualize it in Fig. 1.1.
In contrast to the initial generic-type of AI research, which had difficulties in
identifying the potential for value creation, the defined applied AI has several
sources for profit generation. The first one is improving the productivity of any
process by operating continuously at the top experts level. The second source of
potential value creation is reducing the risks of decision-making through knowl-
edge consolidation of the best and the brightest in the field. The third source of
profit from using applied AI is by conserving the existing domain knowledge
independently when the expert leaves the organization.
The definition of success for applied AI is a computerized solution that responds
to different situations like the domain experts, imitates their reasoning and makes
predictions which are indistinguishable from the experts. The practical AI dream is
a machine clone of the domain-related brain of the expert.
1.1.2 Key Practical Artificial Intelligence Approaches
There are several methods to clone an expert brain and the most important are
shown in Fig. 1.2. An expert system captures expert knowledge in the computer
Fig. 1.1 The icon of appliedAI Put the expert in the
1.1 Artificial Intelligence: The Pioneer 5
using rules and frames. Expert reasoning is mimicked by two key inference
mechanisms goal-driven (backward-chaining) and event-driven (forward chain-
ing). If expert knowledge can be represented by different cases, it could be
structured in a special type of indexed inference, called case-based reasoning.
Knowledge management is a systematic way of knowledge acquisition, representa-
tion, and implementation. These key applied AI methods are discussed in more
188.8.131.52 Expert Systems (Rule-Based and Frame-Based)
Rule-Based Expert Systems
One possible way to represent human knowledge in a computer is by rules. A rule
expresses a programmatic response to a set of conditions. A rule contains text and a
set of attributes. When you create a new rule, you enter a two-part statement in its
text. The first part, called the antecedent, tests for a condition. The second part,
called the consequent, specifies the actions to take when the condition returns a
value of true. This is an example of the text of a rule:
If the level of any tank = 0 then conclude that the status of the tank is empty
A rule-based expert system consists of a set of independent or related rules. The
performance and the value creation of the rule-based expert system strongly depend
on the quality of the defined rules. The domain expert plays a critical role in this
Key Artificial IntelligenceApproaches
Case Based Reasoning
Fig. 1.2 Key approaches of applied AI
6 1 Artificial vs. Computational Intelligence
process. Rule-based expert systems can only be as good as the domain experts they
are programmed to mimic.
Frame-Based Expert Systems
Another way to represent domain knowledge is by frames. This method was
introduced by one of the AI founding fathers, Marvin Minsky, in the 1970s and
was pushed further by the emerging field of object-oriented programming. A frame
is a type of knowledge representation in which a group of attributes describes a
given object. Each attribute is stored in a slot which may contain default values. An
example of a typical object in a manufacturing monitoring system is a process
variable with its attributes. Object attributes include all necessary information
for representing this entity, including the data server name, tag name, process
variable value, update interval, valid