AI research focused on allowing systems to respond to novelty and uncertainty in more flexible ways is starting to be used in IDSS. For example, intelligent agents that perform complex cognitive tasks without the need for human intervention have been used in a variety of decision-making support applications. The capabilities of these intelligent agents include knowledge sharing, machine learning, data mining, and automated inference. A number of AI techniques, such as case-based reasoning, approximate sets and fuzzy logic, have also been used to allow decision support systems to work better under uncertain conditions.
An application that is used to support decision-making is commonly known as DSS and can be classified into three categories, which are passive DSS, active DSS and proactive DSS (Kwon et al. Passive DSS is a traditional DSS with functionalities to react as a personalized decision, built-in knowledge support, without content and only for static user preferences. In addition, the components of passive DSS are data storage, OLAP and rules-based. The second category of DSS is active DSS, which is known as personalized decision support with learning capacity, without content and with static user preferences.
The expert system, the adaptive DSS and the knowledge-based system (KBS) are classified as part of the Intelligent DSS (IDSS). In this category, machine and agent learning are the main component of active DSS. Finally, the third category is the proactive DSS, known as DSS based on ubiquitous computing technology (UBIDs), which contains decision-making and context-sensitive functionalities. This type of DSS has mobility, portability and proactivity capabilities.
The proactive applications of the DSS are: proactive based on rolls, proactive based on push and automated based on push. In this study, we focused on active DSS, known as Intelligent DSS (IDSS), using a machine learning approach. DSS and Business Intelligence (BI) are often combined. Some experts believe that BI is the successor to the DSS.
Decision support systems are generally recognized as an element of business intelligence systems, along with data warehousing and data mining. In that case, the integration between DSS and hybrid intelligent techniques will improve the capabilities of IDSS applications. The concept of DSS grew out of research conducted at the Carnegie Institute of Technology in the 1950s and 1960s, but it really took root in the company in the 1980s in the form of executive information systems (EIS), group decision support systems (GDSS) and organizational decision support systems (ODSS). Decision support systems are a subset of business intelligence intended to help organizations make informed business decisions based on enormous amounts of analyzed data.
Therefore, to fill the gap in the literature, this article studies the influence of each component and its subcomponents on decision-making in the practical application of companies. There are several types of intelligent techniques that are applied in IDSS applications, such as the knowledge base system (Quintero et al. This can help increase IDSS products in the market as alternative tools to support and improve decision-making processes for specific problem domains. OLAP technologies implement multidimensional data analysis, data mining is used to extract knowledge in databases and data warehouses, the model base makes the combined help decision of multiple generalized models, and the expert system uses knowledge reasoning to achieve qualitative analysis.
The basic ideas of intelligence are to study the thought processes of humans, to try to represent and duplicate those processes through machines (p. In the 1990s, the data warehouse concept, which was combined with the traditional intelligent FDSS, further improved the effect of auxiliary decision-making. The intensification of competition and the development of electronic commerce require companies to have more accurate, timely and critical information, deeper financial analysis, more effective and timely communication and higher-quality decision-making. Combined with the traditional business financial decision support system, this document summarizes and presents in detail the structure and function of the Business Financial Decision Support System and its subsystems within the framework of the development of artificial intelligence.
The objective of AI techniques integrated into an intelligent decision support system is to allow a computer to perform these tasks and, at the same time, to emulate human capabilities as faithfully as possible. For these reasons, this study presents an idea for applying the IDSS approach to human resources decision-making activities by using some of the possible intelligent techniques. .
