# Trust Model of Privacy-Concerned, Emotionally Aware Agents in a Cooperative Logistics Problem
## 1 Introduction  
Including emotions in the interactions between humans and autonomous computational entities (agents) is among the current challenges faced by artificial intelligence: so-called affective computing [1]. Success in addressing this challenge would provide efficiency and common understanding in such interactions. Trust (how trust is achieved, applied and updated) is also of considerable relevance with respect to how and whether such interactions between humans and agents take place. Both uniquely human concepts, trust and emotion have been addressed in the field of psychology from different theoretical perspectives in the scientific literature, as discussed in Section 2. We intend to suggest a system of agents that integrates the existing links between privacy, trust and emotions in a meaningful way that is coherent with such literature. We intend to achieve this goal by inspiring ourselves with a hypothetical cooperative logistics problem wherein humans and embodied unmanned vehicles gait according to their internal emotions and where they also perceive the emotions of other agents they meet (by observing their gait). Although our simulation does not include humans or futuristic embodied unmanned vehicles that are able to emotionally gait, the implemented agents reason and communicate in a human-like way, and they perceive simulated emotions through the hypothetical gaits of the agents they meet. In such a suggested cooperative logistic problem, these agents have to perform repetitive tasks (moving boxes). Because several of these tasks may overwhelm the ability of an agent, they request the cooperation of other agents to perform some of these tasks (move boxes). Asking for and accepting cooperation involves forming a trusting relationship, wherein trust in other agents is built not only based on knowledge of past direct interactions but also on the internal emotionsof the agent and with the interpretation of the perceived emotion of the interacting agent. In this way, internal emotions and the perception of alien emotions of other agents act as indirect knowledge in the trust decision, replacing the role that reputation information about third parties often plays in trust models. The trust model we suggest is therefore focused on social punishment applied to misbehaving agents (non-cooperative behavior) through reasoning about their own and alien emotions (acquired by observation and based on the privacy concern associated with the task to be accomplished). On one hand, our intended hypothetical use case consists of humans and embodied unmanned vehicles that are both moving boxes in the same environment. Existing unmanned vehicles are either able to autonomously transport boxes in real urban environments or they are embodied and able to gait as humans; however, the two features are currently mutually exclusive. In a case in which only humans have the ability to gait, only human emotions can be perceived, so our model must be significantly adapted to this limitation. On the other hand, the privacy issues associated with the boxes may take several forms depending on the nature of the box itself; for instance, some examples of real-life situations in which humans may feel shame are those regarding the potential object they are carrying:  
* A bouquet of flowers, especially if the handler is seen in a suspicious way due to personal circumstances, e.g., already married, too young/old, etc. ;
* A stroller for a baby, especially if the handler's work colleagues do not know about it;
* A set of masks when there is a mask shortage in a pandemic situation;
* A piece of cloth not coherent with the perceived gender role of the handler;
* Any kind of item with a political/ideological meaning. Possible reasons that justify privacy issues inducing shame in the handler often differ in nature, making it nearly impossible to produce an exhaustive list. Our work is not intended to be specific to any such context.