## 1 Introduction  
Due to the significant amount of information available and its associated uncertainty, there is a growing attention towards expert systems in support of human Situational Awareness able to perform probabilistic reasoning [1], such as the ones based on Bayesian networks (BNs). Expert systems are intelligent systems designed on the basis of knowledge acquired from experts [2]. Situational Awareness (SAW), which represents one of the steps of decision-making processes, can be defined as a mental model of the environment, built through \"the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future\" [3]. Knowledge acquisition (KA), which is a step of primer importance in the design of many systems, is the process of extracting, structuring, and organizing domain knowledge from experts [4]. Two widely used techniques are unstructured or structured interviews (i.e., protocol-generation techniques) [4]. Additional techniques include protocol analysis techniques, hierarchy-generation techniques, matrix-based techniques, sorting techniques, limited-information, and constrained-processing tasks and diagram-based techniques [5]. Details on the different techniques and their use can be found in the abundant literature on the topic (e.g., [6; 7; 8; 9]). With respect to KA for Bayesian networks, the most widely adopted methods for the elicitation of the network structure and parameters are interviews and questionnaires. However, literature hasdiscussed several issues related to probability elicitation from experts, such as expert limited time availability, biases, probability inconsistencies (i.e., probabilities not summing up to one), and the difficulty of verbalizing probabilities, especially when related to tacit knowledge [1]. In fact, often expert's knowledge is not directly accessible or easy to express [10]. Researchers have been looking at solving those issues by modifying the interview or questionnaire approaches. For example, some proposed to adopt gamble-like elicitation methods or the use of specifically designed probability scales [11; 12]. Others proposed techniques to generate full conditional probability distributions from a reduced number of elicited assessments (e.g., [13; 14; 15]). Although several works have addressed the problem of experts' probability elicitation (e.g., [16; 17; 18; 19]), research is still required to develop more efficient and effective techniques. The authors have proposed the use of innovative approaches based on analytical gaming (i.e., [20]). The use of games for KA is based on the concept that games are \"a communication mode capable of linking tacit to formal knowledge by provoking action and stimulating experience\" [21]. The use of games and gamification [22; 23] in a non-gaming context is not a new concept. While the terms serious game and wargame identify full games that have a scope beyond mere entertainment, the term gamification refers to the use of game elements for this scope. Serious games have been formally established by [PERSON] in the seventies [24], when he started exploring the use of games for learning purposes. However, the modern use of games with a purpose different than entertainment (i.e., training, communication or research) can be traced back to the early 19 th century, when modern wargames started to appear (i.e., Kriegsspiel). Wargames are a well-formalized kind of simulation game which embodies a \"warfare model or simulation whose operation does not involve the activities of actual military forces, and whose sequence of events affects and is, in turn, affected by the decisions made by players representing the opposing sides\" [25]. Analytical wargames are wargames designed with a research purpose. Data-exchange serious game, instead, is the term through which the serious game community identifies games for collecting information from the players [26].