# Trust Model of Privacy-Concerned, Emotionally Aware Agents in a Cooperative Logistics Problem
## 8 Conclusions  
Interactions between autonomous agents and humans in mixed environments are associated with several challenges. Although our focus is on conversational means, emotions (their expression, perception and reasoning) may also play a major role. In this paper, we have addressed the topic of using emotions within a trust model with an innovative inclusion with respect to the role of privacy issues in an emotional trust model. To the best of our knowledge, this issue has not been addressed before in the scientific literature. Our research accomplishes several milestones:  
* We proposed a particular way to include privacy in an emotional model that is compliant with psychological theories and previous practical approaches to these theories, as explained in Section 1;
* We proposed a particular way to include privacy in the cooperation decisions of a trust model;
* We suggested a set of particular values for all variables that form our privacy-sensible emotional model;
* We implemented our model in the reasoning of symbolic agents in GAML (the programming language of the GAMA agent platform) according to the belief, desires and intentions deliberative paradigm, which is communicated using the IEEE FIRA standard. * We also defined a cooperative logistic problem to test our model. Figure 7: Comparison of the three alternatives when the probability of privacy issues increases. * And finally, we have executed agent simulations that generated two different comparisons. Such comparisons allowed us to observe the contribution of emotions and trust in the defined cooperative logistic problem to improve both of our goals: time savings and privacy protection. In our proposal, we intended for the agents to be as humanized as possible through the use of a symbolic approach that provides explainability and transparency via BDI reasoning with FIPA. Privacy issues are also an important factor in how we feel when interacting with others, and emotional agents should address their perceptions, expression and reasoning. The inclusion of privacy sensitivity in an emotional trust model is so innovative that a comparison with alternative models is not yet possible. Although the experimental results that we obtained from the simulation of the logistic problem do not seem to encourage the use of emotions to satisfy both our goals (time savings and privacy protection), the inclusion of emotions is assumed to be a key element for autonomous agents to become accepted in real-life applications. And, since the emotions do not significantly harm the results, its inclusion could balance the little bit of loss in performance with the improvement in representativeness that emotions provide to social interactions. This is the most significant contribution of our work, rather than the potential gain that sharing tasks themselves can provide. Our contribution is not the only way to model the social punishments that privacy issues cause, and many particular settings could take another form and lead to very different experimental results, including the conceptual representation of emotions based on the problem used to test the model and the mathematical quantification of all involved factors. Many other alternative uses of privacy in an emotional interaction between humans and autonomous agents are possible, but our model provides a step towards a path worth exploring. The adaptation of our emotional trust model to the hypothetical mixed environment of humans and unmanned vehicles is out of the scope of this contribution since it would require taking into account the existence of embodied unmanned vehicles expressing emotions through their gait, which we can currently only imagine. Also, other circumstances would have to be taken into account, such as the capacity and range of the different types of autonomous vehicles. Other transportation issues, such as the traffic, weather conditions or the different relevance of the boxes may also play a role.