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1.4 Applications of AI Techniques:
Almost every branch of science and engineering currently shares the tools and
techniques available in the domain of AI. However, we mention here a few typical
applications, where AI plays a significant role in engineering automation.
[1] 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.(1.6). PR1 in Fig.(1.6) 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.(1.6): The reasoning process of expert system
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[2] 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.(1.7). 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.
Fig. (1.7): Basic steps in scene interpretation.
[3] Navigational Planning for Mobile Robots: Mobile robots, sometimes called
automated guided vehicles (AGV), are a challenging area of research, where AI finds
extensive applications. A mobile robot generally has one or more camera or
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ultrasonic sensors, which help in identifying the obstacles on its trajectory. The
navigational planning problem persists in both static and dynamic environments. In a
static environment, the position of obstacles is fixed, while in a dynamic environment
the obstacles may move at arbitrary directions with varying speeds, lower than the
maximum speed of the robot. Many researchers using spatio-temporal logic have
attempted the navigational planning problems for mobile robots in a static
environment. On the other hand, for path planning in a dynamic environment, the
genetic algorithm and the neural network-based approach have had some success. In
the near future, mobile robots will find extensive applications in fire-fighting, mine
clearing and factory automation. In accident prone industrial environment, mobile
robots may be exploited for automatic diagnosis and replacement of defective parts of
instruments.
[4] Speech and Natural Language Understanding: Understanding of speech and
natural languages is basically two classical problems. In speech analysis, the main
problem is to separate the syllables of a spoken word and determine features like
amplitude, and fundamental and harmonic frequencies of each syllable. The words
then could be identified from the extracted features by pattern classification
techniques. Recently, artificial neural networks have been employed to classify words
from their features. The problem 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.
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[5] 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 flow
shop 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.(1.8) (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.(1.8): 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
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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.
[6] 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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