Artificial intelligence raises important social and ethical concerns, especially about accountability, autonomy, dignity, and justice. We focus on the specific concerns arising from how the emerging autonomous vehicle (AV) technology will affect professional drivers. We posit that we must engage with stakeholders to understand the implications of a technology that will affect the stakeholders’ lives, livelihoods, or wellbeing. We conducted nine in-depth interviews with professional drivers, with at least two years of driving experience, to understand the ethical and societal challenges from the drivers’ perspective during the predicted widespread implementation of AVs. Safety was the most commonly discussed issue, which was mentioned by all drivers (17 times by truck drivers and 18 times by Uber/Lyft drivers). We find that although drivers agree that AVs will significantly impact future transportation systems, they are apprehensive about the prospects of reskilling for other jobs and want their employers to be straightforward in how the introduction of AVs will affect them. Additionally, drivers dismiss the suggestions that driving jobs are unsatisfying and potentially unhealthy and thus should be eliminated. These findings should be considered seriously in decision-making about questions of socioeconomic justice, and could be useful to policymakers as they shape relevant regulations.

Multi-agent systems provide a basis for developing systems of autonomous entities and thus find application in a variety of domains. We consider a setting where not only the member agents are adaptive but also the multi-agent system viewed as an entity in its own right is adaptive. Specifically, the social structure of a multi-agent system can be reflected in the social norms among its members. It is well recognized that the norms that arise in society are not always beneficial to its members. We focus on prosocial norms, which help achieve positive outcomes for society and often provide guidance to agents to act in a manner that takes into account the welfare of others. Specifically, we propose Cha, a framework for the emergence of prosocial norms. Unlike previous norm emergence approaches, Cha supports continual change to a system (agents may enter and leave) and dynamism (norms may change when the environment changes). Importantly, Cha agents incorporate prosocial decision-making based on inequity aversion theory, reflecting an intuition of guilt arising from being antisocial. In this manner, Cha brings together two important themes in prosociality: decision-making by individuals and fairness of system-level outcomes. We demonstrate via simulation that Cha can improve aggregate societal gains and fairness of outcomes.

The dynamic behavior of traffic lights regulating a network of intersections can be viewed as a complex discrete-event system that can be modeled and controlled by Petri nets. In this paper, a bilayer signalized control method is presented. In the lower layer, a colored stochastic Petri net controls the signal indication displays and stage transitions. In the upper layer, a supervisory logic layer decides which stage should be serviced next. The proposed model increases modularity, reduces complexity, and, more importantly, is capable of being used to implement various signal control strategies. The method is used to implement four different control strategies: pretimed control, fully actuated control, semiactuated control, and random stage selection (as a benchmark) in the context of a signalized traffic network containing six intersections. Extensibility and future potential research to further enhance the proposed method are discussed and proposed at the end.

This paper focuses on the use of Petri nets (PN) to model the control of signalized intersections. The application of PN to an eight-phase traffic signal controller is illustrated. Structural analysis of the control PN model is performed to demonstrate how the model enforces the traffic operation safety rules. This is followed by a discussion of why this modeling tool has future value as the use of more advanced control strategies continue to expand.

A multiobjective approach to manufacturing process optimization is described. This approach places no restriction on the type or number of performance criteria and no a priori weights need be chosen for it since performance criteria are not combined into a single objective function. An algorithm is implemented that runs in real-time and provides immediate feedback to a process operator about the current operating point. If the current operating point is not Pareto optimal, the algorithm automatically adjusts the parameters to achieve this. If it is Pareto optimal but not desirable, the algorithm can provide information on how tradeoffs can be made so that the desired operating point is achieved. The algorithm was applied to the tuning of process inputs for the plastication and injection phases of an injection molding process. The optimization took place with respect to three quality characteristics: cycle time, flashing, and underfill. Measurement of flashing and underfill was done by the operator and both binary and fuzzy techniques were used. The algorithm was used to tune the process online, and experimental results are presented.>