Collective Reasoning for Safe Autonomous Systems

Abstract

Autonomous systems, capable of independent interaction with dynamic environments, face significant challenges in ensuring safety and trustworthiness, particularly in uncertain contexts such as automated driving. This paper explores the concept of collective reasoning as a strategy to improve the reliability and decision-making capabilities of multi-agent autonomous systems. By leveraging collective intelligence, where systems share and process distributed knowledge, collaborative autonomy can enhance safety through cooperative mechanisms. This approach acknowledges the heterogeneity of system features, such as perception quality, and emphasizes structured methods for reasoning about shared information. Drawing on principles from social epistemology, the article discusses how autonomous systems can form a structured "society" to propagate reliable beliefs, ultimately improving safety and decision-making in open, dynamic environments.

Summary

The paper "Collective Reasoning for Safe Autonomous Systems" addresses the critical challenge of designing autonomous systems that operate safely and reliably in dynamic, uncertain environments. Unlike traditional automata designed for repetitive tasks, modern autonomous systems, such as those used in automated driving, must make decisions in contexts that are not fully defined at design time. This requires high levels of trustworthiness and robust decision-making, particularly in safety-critical applications. Despite advancements in machine learning and artificial intelligence, current safety assurance methods, such as worst-case scenario planning, failure mode and effects analysis (FMEA), and fault tree analysis (FTA), are insufficient for operational-time safety in dynamic settings. The paper proposes embedding safety assurance functions to supervise behavior and introduces collective intelligence as a mechanism to enhance system reliability.

Collective intelligence, as defined in the paper, involves the coordinated exploitation of distributed intelligence across multiple systems to improve decision-making. This concept, rooted in the idea that groups can outperform individuals in reasoning tasks, has been applied in domains like social computing and multi-agent systems. In autonomous systems, collective intelligence enables collaborative autonomy, where systems with varying capabilities, such as differing perception qualities, cooperate to build a shared environmental model. The paper distinguishes between collaborative knowledge acquisition (e.g., constructing a model of the environment) and collaborative decision-making (e.g., path planning), noting that these processes may not always occur together. For instance, vehicles in a cooperative driving scenario can use Vehicle-to-X communication to share information, such as collective awareness messages, to enhance local or global decision-making. However, sharing information alone does not guarantee safety, necessitating structured methods for collective reasoning.

The paper introduces collaborative trustworthiness, where reliability increases as systems share and refine information to create a more accurate environmental model. Figure 1 illustrates this concept, showing how autonomous systems can express knowledge as predicates (e.g., "an object has been detected" or "distance to this object is less than 500 meters"). By aggregating these predicates, systems expand their collective knowledge sphere, enhancing trustworthiness. However, the paper emphasizes that more knowledge does not automatically lead to better decisions; structured rules for reasoning are essential to filter and prioritize reliable information.

Figure 1. Increase collaboratively trustworthiness exploiting collective knowledge that can be expressed as predicates.

To formalize collective reasoning, the paper draws on the study of group knowledge, specifically belief propagation, where a group of autonomous systems is structured as a graph with nodes representing agents and edges indicating relationships based on feature quality (e.g., sensor accuracy or perception angle). Figure 2 illustrates this in a smart intersection scenario with four vehicles, where relationships are defined by features like distance and perception angle. For example, a vehicle with a better perception angle may be deemed more trustworthy for detecting a pedestrian. The paper proposes modeling these relationships using a lattice to represent partial-order hierarchies based on feature quality, enabling dynamic updates as conditions change.

Figure 2. Vehicles with different features possible detecting a pedestrian

The paper explores different rules for belief propagation, such as the "Most Expert" rule, where only the most capable system’s belief is propagated, and the "Majority" rule, where the most common belief among all systems is adopted. Each rule has trade-offs: the "Most Expert" rule risks errors if the expert system fails, while the "Majority" rule may dilute reliability by including less trustworthy inputs. The paper suggests hybrid rules involving subgroups of systems with varying expertise to balance accuracy and fairness.

Key challenges in implementing collective reasoning include defining appropriate rules for selecting trustworthy subgroups and aggregating beliefs, managing computational complexity as the number of features and systems increases, and transitioning from automated to autonomous reasoning, where systems adaptively generate new rules based on context. For example, in the smart intersection scenario, features like sensor quality or perception angle are dynamic, requiring real-time updates to the lattice model as seen in Figure 3. Efficient data structures and algorithms are needed to handle high-dimensional, dynamic lattices for real-time applications.

Figure 3. Schematic illustration of groups involved in forming a correct belief and its propagation based on different rules: (a)”Most Expert” rule: only the belief of the most expert propagates, (b) ”Majority” rule: propagation of all beliefs and the most common belief is then further propagated, (c) further rules involving only a sub-group of autonomous systems with better (and possibly less better) experts.

The paper concludes by emphasizing the need for systematic methods to ensure that collective reasoning enhances safety and trustworthiness. By structuring autonomous systems as a "society" that collaboratively reasons about shared information, the approach aims to address the limitations of individual system reliability, offering a promising framework for safer autonomous operations in uncertain environments.