An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Bayesian Networks Based Decision Making <s> The author discusses the development of artificial intelligence (AI). He explains the basic elements of AI: Heuristic search, knowledge representation, AI languages and tools, Natural Language Processing, computer vision, expert systems and problem solving and planning. <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Bayesian Networks Based Decision Making <s> This paper describes a systematic procedure for constructing Bayesian networks (BNs) from domain knowledge of experts using the causal mapping approach. We outline how causal knowledge of experts can be represented as causal maps, and how the graphical structure of causal maps can be modified to construct Bayes nets. Probability encoding techniques can be used to assess the numerical parameters of the resulting Bayes nets. We illustrate the construction of a Bayes net starting from a causal map of a systems analyst in the context of an information technology application outsourcing decision. <s> BIB002
BN can be used in making decision in variety of applications as discussed in previous sub-section. Based on this discussion, we have considered causal maps for representing BN for making decision. The procedure for constructing Bayesian causal maps can be divided into two stages namely: qualitative and probabilistic. The qualitative stage eliminates the limitations of modeling and makes causal maps compatible with the BN. This procedure has four major modeling issues. First, conditional independencies mean that a network can either be Dependence Map (D-Map) or Independence Map (I-Map). I-map is a graph in which the entire set unconnected of nodes corresponds to independent variables. On the other hand, D-map is a graph that represents the entire set of connected nodes corresponding to dependent variables. A model that follows both I-maps and D-maps is known as a perfect map. Further details of dependency model with necessary and sufficient conditions on perfect maps are given in  . Second, reasoning underlying cause-effect relations can either be abductive or deductive BIB001 , [72] . Therefore, a reason is a rational ground or motive for an action or decision. Thus, abductive reasoning is a process that justifies a rational ground from effects to causes. The deductive reasoning is a process that justifies a rational ground from causes to effects. The difference between both reasoning processes is shown in Fig. 5(a) BIB002 . Third, distinguishing between direct and indirect relationships means to identify the conditional independencies as depicted in Fig. 5(b) . The purpose is to draw attention to the conditional independencies assumption affects in causal maps BIB002 . Also, causal maps lack of distinguishing in direct and indirect relationship. 