Almost every branch of science and engineering currently shares the
tools and techniques available in the domain of artificial intelligence.
However, for the sake of the convenience of the readers, we mention
here a few typical applications, where AI plays a significant and
decisive role in engineering automation.
Expert Systems: In this example, we illustrate the
reasoning process involved in an expert system for a weather forecasting
problem with special emphasis to its architecture. An expert system
consists of a knowledge base, database and an inference engine for
interpreting the database using the knowledge supplied in the knowledge
base. The reasoning process of a typical illustrative expert system is
described in Fig. PR 1 in Fig. represents i-th production rule. The
inference engine attempts to match the antecedent clauses (IF parts) of
the rules with the data stored in the database. When all the antecedent
clauses of a rule are available in the database, the rule is fired,
resulting in new inferences. The resulting inferences are added to the
database for activating subsequent firing of other rules. In order to
keep limited data in the database, a few rules that contain an explicit
consequent (THEN) clause to delete specific data from the databases are
employed in the knowledge base. On firing of such rules, the unwanted
data clauses as suggested by the rule are deleted from the database.
Here PR1 fires as both of its antecedent clauses are present in the
database. On firing of PR1, the consequent clause "it-will-rain" will be
added to the database for subsequent firing of PR2.
Fig. Illustrative architecture of an expert system.
Image Understanding and Computer Vision: A digital
image can be regarded as a two-dimensional array of pixels containing
gray levels corresponding to the intensity of the reflected illumination
received by a video camera. For interpretation of a scene, its image
should be passed through three basic processes: low, medium and high
level vision .
Fig.: Basic steps in scene interpretation.
The importance of low level vision is to pre-process the image by
filtering from noise. The medium level vision system deals with
enhancement of details and segmentation (i.e., partitioning the image
into objects of interest ). The high level vision system includes three
steps: recognition of the objects from the segmented image, labeling of
the image and interpretation of the scene. Most of the AI tools and
techniques are required in high level vision systems. Recognition of
objects from its image can be carried out through a process of pattern
classification, which at present is realized by supervised learning
algorithms. The interpretation process, on the other hand, requires
knowledge-based computation.
Speech and Natural Language Understanding:
Understanding of speech and natural languages is basically two class
ical problems. In speech analysis, the main probl em is to separate the
syllables of a spoken word and determine features like ampli tude, and
fundamental and harmonic frequencies of each syllable. The words then
could be ident ified from the extracted features by pattern class
ification techniques. Recently, artificial neural networks have been
employed to class ify words from their features. The probl em of
understanding natural languages like English, on the other hand,
includes syntactic and semantic interpretation of the words in a
sentence, and sentences in a paragraph. The syntactic steps are required
to analyze the sentences by its grammar and are similar with the steps
of compilation. The semantic analysis, which is performed following the
syntactic analysis, determines the meaning of the sentences from the
association of the words and that of a paragraph from the closeness of
the sentences. A robot capable of understanding speech in a natural
language will be of immense importance, for it could execute any task
verbally communicated to it. The phonetic typewriter, which prints the
words pronounced by a person, is another recent invention where speech
understanding is employed in a commercial application.
Scheduling: In a scheduling problem, one has to plan
the time schedule of a set of events to improve the time efficiency of
the solution. For instance in a class-routine scheduling problem, the
teachers are allocated to different classrooms at different time slots,
and we want most classrooms to be occupied most of the time. In a
flowshop scheduling problem, a set of jobs J1 and J2 (say) are to be
allocated to a set of machines M1, M2 and M3. (say). We assume that each
job requires some operations to be done on all these machines in a
fixed order say, M1, M2 and M3. Now, what should be the schedule of the
jobs (J1-J2) or (J2 -J1), so that the completion time of both the jobs,
called the make-span, is minimized? Let the processing time of jobs J1
and J2 on machines M1, M2 and M3 be (5, 8, 7) and (8, 2, 3)
respectively. The gantt charts in fig. (a) and (b) describe the
make-spans for the schedule of jobs J1 - J2 and J2 - J1 respectively. It
is clear from these figures that J1-J2 schedule requires less make-span
and is thus preferred.
Fig.: The Gantt charts for the flowshop scheduling problem with 2 jobs and 3 machines.
Flowshop scheduling problems are a NP complete problem and determination
of optimal scheduling (for minimizing the make-span) thus requires an
exponential order of time with respect to both machine-size and
job-size. Finding a sub-optimal solution is thus preferred for such
scheduling problems. Recently, artificial neural nets and genetic
algorithms have been employed to solve this problem. The heuristic
search, to be discussed shortly, has also been used for handling this
problem.
Intelligent Control: In process control, the controller is designed from the
known models of the process and the required control objective. When the
dynamics of the plant is not completely known, the existing techniques for
controller design no longer remain valid. Rule-based control is appropriate in
such situations. In a rule-based control system, the controller is realized by a
set of production rules intuitively set by an expert control engineer. The
antecedent (premise) part of the rules in a rule-based system is searched
against the dynamic response of the plant parameters. The rule whose
antecedent part matches with the plant response is selected and fired. When
more than one rule is firable, the controller resolves the conflict by a set of
strategies. On the other hand, there exist situations when the antecedent part
of no rules exactly matches with the plant responses. Such situations are
handled with fuzzy logic, which is capable of matching the antecedent parts of
rules partially/ approximately with the dynamic plant responses. Fuzzy
control has been successfully used in many industrial plants. One
typical
application is the power control in a nuclear reactor. Besides design of the
controller, the other issue in process control is to design a plant (process)
estimator, which attempts to follow the response of the actual plant, when
both the plant and the estimator are jointly excited by a common input signal.
The fuzzy and artificial neural network-based learning techniques have recently
been identified as new tools for plant estimation.
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