## 5 Conclusions  
This work explores the use of the MARISA Game, which is an analytical game designed to support knowledge acquisition from maritime surveillance experts of a considerable amount of conditional probabilities to be used into a maritime behavioral analysis service, based on a multi-source dynamic Bayesian network. Two experiments have been run with different players and the results yield a positive outcome. More specifically, all the objectives were met in both cases. Those objectives consisted of the validation of the multi-source dynamic Bayesian network structure, the elicitation of its conditional probabilities, and the game evaluation. In fact, the game acted as a natural form of structured interview, supporting not only the elicitation of the conditional probabilities, but also the validation of the network structure. The knowledge utility was proven in the successful operational trials where the behavioral analysis service was deployed. Improvements in the KA task were observed in terms of expert time required and complexity of the task compared to traditional questionnaire approaches. Finally, a positive feedback was received in terms of player experience.  
This paper supports the concept of using analytical games, such as the MARISA one, for knowledge acquisition, knowledge engineering, and, in general, participatory design. Future work will be devoted to increase the understanding of which game mechanics better fit the knowledge engineering problem and experimentation constraints (i.e., time availability), to enhance the player profiling and to improve the pre-game communication protocol. Moreover, future efforts will focus on improving the game from a sensory and imaginative immersion perspective, in order to provide a richer experience to the players. Furthermore, research will address the definition of evaluation protocols for such games (including the validation of player-experience questionnaires) and on the deployment of the game to a broader set of experts in order to increase our understanding of the strength and weaknesses of the use of gaming for knowledge acquisition.  
Conceptualization, F.d.R.; Methodology, F.d.R.; Validation, F.d.R., Investigation, F.d.R.; Writing--Original draft preparation, F.d.R.; Writing--Review and editing, A.D.G. and F.d.R.; Supervision, A.D.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors gratefully acknowledge the support of NATO STO Centre for Maritime Research and Experimentation for sharing the data that enabled this work. In fact, this data has been collected through the deployment of the game within the MARISA project. MARISA European research project has received funding from the European Union's Horizon 2020 research and innovation program under Grant No. 740698. The authors would like to thank all the game participants, [PERSON], and [PERSON] for their support with the experimentation sessions. Moreover, the authors would like to thank Cdr. [PERSON] for the support in the playtesting of the game.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BN & Bayesian network \\ CISE & Common information sharing environment \\ CPT & Conditional probability table \\ DAG & Direct acyclic graph \\ DBN & Dynamic Bayesian network \\ EC & European Commission \\ H2020 & Horizon 2020 \\ IUU & Illegal, unreported and unregulated \\ KA & Knowledge acquisition \\ MARISA & Maritime integrated surveillance awareness \\ MARISA Game & MARItime Surveillance knowledge Acquisition Game \\ MSDBN & Multi-source dynamyc Bayesian network \\ NASA TLX & NASA task load index \\ PX & Player experience \\ QUIS & Questionnaire for user interaction satisfaction \\ SAW & Situational awareness \\ SLOC & Sea line of communication \\ SMG & Smuggling of goods \\ \end{tabular}