Wireless communication/networks are developing into very complex systems because of different requirements and applications of consumers. Today, mobile terminals are equipped with multi-channel and multiple access interfaces for different kinds of applications (or services). The combination of these access technologies needs an intelligent control to interface the best channel, interface/access or link for best services. In interface management, an arrangement is used to assign channels to interfaces in the multi-channel multi-interface environment. Artificial intelligence is one of the upcoming areas with different techniques which is used now a days to meet user's requirements. Quality of service (QoS) and quality of experience (QoE) are the performance parameters on which the success of any technique depends upon. Reliability of any system plays an important role in user satisfaction. This chapter shows some of the artificial techniques that can be used to make a reliable system.
TopIntroduction
Artificial Intelligence (AI) is related with two terms “artificial” and “intelligence”. Artificial related to anything which is manmade. Things prepared / generated by human with the help of machines are synthetic or artificial. Intelligence shows the capability of an individual to realize, learn or assume. Artificial intelligence is an arrangement. It is not a system although implemented in the system. AI is defined in many ways .One researcher termed AI as “It is the study of how to train the computers so that computers can do things which at present human can do better”. Thus, it is an arrangement of intelligence where human can include the entire the qualities in a device that he has itself.
Thus, AI has the ability of a computer system or a device to imagine, assume and learn. It is related with area of research which tries to make a system “smart and intelligent”.
In other language, AI is an area of computer science that emphasizes the design of intelligent machines that acts like human brain. Some of the actions of system designed with artificial intelligence are: thinking, voice recognition, capturing and learning etc. AI systems typically express some of the following nature linked with individual intelligence: scheduling, learning, interpretation, trouble solving, information designing, examination, movement managing, social intelligence and imagination in somewhere.
AI is not limited to just computer or technology industry. Instead, it is being broadly used in other areas such as medical, business, education, law, manufacturing and wireless communication. Some of the examples of AI which used now a days are: Siri (which uses machine-learning technology to interacts with the user on a daily routine), Netflix (information-on-demand service), Drone (translate the environment into a 3D model through sensors and video cameras.), Alexa (friendly female voice-activated assistant) and many more.
In simple way, AI is the implementation of individual intelligence processes by machines, mostly computer systems. The process include following steps to execute any task.
Learning: Gathering of information and set rules or protocols for implementation this information.
Reasoning: Implement the rules to acquire estimated or definite conclusions.
Manipulation: Making adjustments without human intervention.
The somewhat same process is followed by one of the branch of AI which is termed as machine learning although it is different.
Machine Learning (ML)
ML is a sub-branch of artificial intelligence. In ML devices understand, execute and get better their operations by exploiting the process knowledge and experience obtained in the form of outcome data. It is also considered as a function of AI that provides the ability to self learns and improves from experience. The simple definitions is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learner’s performance at the task in the class as measured by P improves with experiences”. ML focuses on the development of algorithms that can analyze data and make predictions. ML also differs from normal computer programming. In computer programming the set of instructions are given by individual to solve the problem whereas ML made the program itself. It is named as an algorithm, a model and sometimes an agent learning from the data it is given. Computer programming will not learn or get any better with experience whereas ML has ability to solve the problem gets better with experience.
ML also defined as automating and improving the learning process of computers depends on their past experiences with no help of programming i.e. without any human help. The operation begins with giving a high-quality quality data and then guiding our machines (computers) by creating machine learning models using the data and different programs. The selection of program depends on what kind of data do we have and what kind of job we are demanding to computerize.