Applications of Artificial Intelligence Techniques

Applications of Artificial Intelligence Techniques

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.