# Diag-Skills: A Diagnosis System Using Belief Functions and Semantic Models in ITS
## 2 Related Works  
As stated in the introduction of this paper, the diagnosis process in ITS is often marked by uncertainty. This issue had led several researchers to consider different approaches to take this uncertainty into account, and improve the quality of the diagnosis process consequently.  
Bayesian networks are one of the most widely used approaches that deal with uncertainty in the diagnosis process [10]. A Bayesian network is a directed acyclic graph in which the nodes represent the variables and the edges represent the probabilistic dependencies or causal relationships between these variables. Among the systems that use such networks, we can mention TELEOS (Technology Enhanced Learning Environment for Orthopedic Surgery) [21] that uses a Bayesian student model to diagnose the students' knowledge states and cognitive behaviors. Other systems and computing environments have used these networks in different domains such as TIDES [22], AdaptErrEx [23], and INQPRO [24]. The Bayesian networks offer a well-defined formalism that lends itself to sound probability computations of unobserved nodes from the evidence of observed nodes [25]. However, one major limitation of the use of Bayesian networks is that they need to be initially set with probabilities, and it is very hard for the domain's expert to encrypt these probabilities, when (s)he does not have enough data. Furthermore, the collection of these data is usually difficult.  
Another well-established approach for reasoning with uncertainty in the diagnosis process is the use of fuzzy logic [26]. Some researchers use fuzzy logic to model uncertainty that is due to the subjectivity of some human characteristics and imprecisions of the learner's data [10]. The fuzzy logic is a generalization of the classical set theory. It introduces the notion of degree of membership which allows a condition to be in a state other than true or false. It also introduces the use of linguistic variables and the design of models that are both intelligible and runnable by computer systems. Therefore, many learning environments use this approach to model the learners' characteristics such as [5; 27; 28]. However, it has also some limitations. The rule base and membership functions become difficult to acquire when the domain model is complex and when there are several input variables. Moreover, when it comes to merging information coming from multiple sources, fuzzy logic fails to quantify the conflict when it exists.  
Some researchers adopted a hybrid approach by combining Bayesian networks with fuzzy logic. [PERSON] and [PERSON] [2], for example, proposed a hybrid system to adaptively support students in learning an Excel course. The adaptation is achieved by modeling the students according to their knowledge level. The authors used fuzzy logic to determine the performance of the students according to their prior knowledge, and the Bayesian network to identify the state of the related topics they are ready to learn, or not, according to the evidence that comes from the fuzzy logic layer. This system benefits from the strength of both approaches; however, it remains limited when it comes to merging information and quantifying the conflict when it exists.