Collaborative Trustworthiness for Good Decision Making in Autonomous Systems

Autonomous systems are becoming an integral part of many application domains, like in the mobility sector. However, ensuring their safe and correct behaviour in dynamic and complex environments remains a significant challenge, where systems should autonomously make decisions e.g., about manoeuvring. We propose in this paper a general collaborative approach for increasing the level of trustworthiness in the environment of operation and improve reliability and good decision making in autonomous system. In the presence of conflicting information, aggregation becomes a major issue for trustworthy decision making based on collaborative data sharing. Unlike classical approaches in the literature that rely on consensus or majority as aggregation rule, we exploit the fact that autonomous systems have different quality attributes like perception quality. We use this criteria to determine which autonomous systems are trustworthy and borrow concepts from social epistemology to define aggregation and propagation rules, used for automated decision making. We use Binary Decision Diagrams (BDDs) as formal models for beliefs aggregation and propagation, and formulate reduction rules to reduce the size of the BDDs and allow efficient computation structures for collaborative automated reasoning. Autonomous systems have the ability to interact with their environment and act independently by solving complex tasks without human intervention. The main challenge with designing autonomous systems is to provide them with the ability to operate correctly and safely in dynamic and open contexts. That is, performing tasks at operation time in an environment that is uncertain or was not fully known or defined at design time. When extended to fields such as automated driving, where safety is a critical requirement, assuring high levels of trustworthiness and "good" decision making in complex environments becomes a real challenge. In this paper, we consider collaboration between autonomous systems to increase trustworthiness and improve decision making.