Artificial intelligence and DSS development

Artificial Intelligence focuses on constructing a person or animal with the aid of science, engineering and IT. However, the successful achievement of this objective, is heavily dependent on the applied learning system. Although the notion of a DSS as a resource for querying and analysis goes back to the 1960s, it was not until the 1990s that data storage was universally available and adopted (Baranovic, Madunic and Mekterovic, 2003). The fundamental objective of all the literature reviewed, embraces the concept of decision making by improving the DSS with various alternative architectures, such as neural networks and finely tuned algorithms, and applying them with high accuracy across all decision-making platforms.

Despite their large number and potential for improving organisational decision making, multi- criteria decision models and decision support systems (MCDM DSS) are not adequately applied and used. The developed MCDM theories currently used are well-known and are generally accepted almost without criticism (Chungyong, 1999). However, there are two main reasons behind these systems being unable to adequately meet the necessary requirements, namely unforeseen technological advances and breakthroughs in software development. The necessity for institutions to invest in the hardware and software necessary for operating a DSS is undeniable, and over time there have been great advances in this field; however, the hope, desire and need for better systems is driving serious research in this area. This leaves institutions fearful that systems will become obsolete before they are implemented. In a time when funding is under great pressure, it is necessary to carry out studies that prove the benefits of the DSS and its adjoining systems.

Therefore, a DSS for resource management is becoming an important tool used for complex administrative tasks carried out by institutions in various fields, such as education. These

activities include resource planning, evaluation of competing strategies and decision making. The DSS most suitable for resource distribution and making decisions are more data centric for data retrieval and data analysis activity. For strategic planning purposes, a model centric oriented DSS is more favourable as it offers suggested actions based on simulations performed, which enable universities to maximize or optimize their resources (Dahlan and Yahaya, 2010).

Encheva and Tumin (2008) presented a novel prototype of a knowledge learning system capable of learning from experience and receiving knowledge. The suggested learning system contains artificial neural networks which are known as ANN's, and an object-oriented knowledge base. It also proposed a genetic algorithm (GA) and fuzzy inference engine. Simulation results confirm the feasibility of this system. However, the author concluded that much effort is necessary to improve the performance of the system proposed in his research. The concept is very interesting and intriguing. However, as an educator trying to establish a strong DSS base in an institution, these types of ground breaking concepts are not yet ‘workable’. Field laboratory experimentation and intense research are invaluable, but beta trials in online situations are not feasible until the research and development provides applications which are suitable for professional use (Encheva and Tumin, 2008).

Encheva and Tumin (2008) paper proposed the application of many-valued logic for the process of selecting and recommending learning objects to be included in a subject. They implemented a prototype of a decision support system using three-tiers of web application server architecture (Encheva and Tumin, 2008). The Apache web server is used for the presentation layer, Python for a logic layer, and the SQLite (structure query language) database engine for the data layer (Limayem and Chelbi, 1997) and (Butler et al., 2006). This is a critical concept for learning object ranking by applying algorithms which make decisions on the best subject choice for a learner. This is one of the tools most needed for the situation in which universities in Saudi Arabia find themselves. Based on both this approach and the traditional way of decision making way, there is also a procedure which recommends the best choice of subject to m learner preferences to a particular learner. A learning object ranking example is discussed to demonstrate the method of implementation based on multi-agent framework (Pukkhem and Vatanawood, 2009).

The capability of learning from experience is of critical importance in developing multi-element systems supplying dynamic team decision making. Then experience acquisition and adaptation are tightly connected to learning. For example, instance-based learning (Gonzalez, Lerch and

Lebiere, 2003) is particularly proper to experience integrity and the Recognition-Primed Decision (RPD) model highly locates on the availability and correct recognition of past experiences. It introduces a hierarchical learning approach which aims to support hierarchical group decision making as the decision makers at a low standard only have a one side view of the whole picture. To further understand such a hierarchical learning concept a learning component within the R- CAST (RPD-enabled Collaborative Agents for Simulating Teamwork) agent architecture, with lower-level learners using the Log it Boost algorithm with decision stumps (Chen et al, 2006) and (Hanratty et al., 2007) was implemented. The boosting-based learning elements were used in their experiments to identify experience cases. The results refer that hierarchical learning can hugely improve suitable decision while lower-rank decision makers have little information that can be applied. In most cases business needs drove the innovation (Chen et al, 2006).

Accordingly, artificial intelligence helped in developing a DSS in the field of the decision-making process for several purposes because both artificial intelligence and DSS are related.