Using Feasible Action-Space Reduction by Groups to fill Causal Responsibility Gaps in Spatial Interactions
This work fills a gap in responsibility metrics for autonomous vehicles and mobile robots by handling cases where individual-focused metrics fail due to causal overdeterminism.
The paper addresses the problem of causal overdeterminism in spatial interactions, where multiple agents simultaneously cause an outcome, by proposing a metric for group causal responsibility. It introduces a tiering algorithm to identify assertive agents and demonstrates through simulations how group effects vary with interaction dynamics and agent proximity.
Heralding the advent of autonomous vehicles and mobile robots that interact with humans, responsibility in spatial interaction is burgeoning as a research topic. Even though metrics of responsibility tailored to spatial interactions have been proposed, they are mostly focused on the responsibility of individual agents. Metrics of causal responsibility focusing on individuals fail in cases of causal overdeterminism - when many actors simultaneously cause an outcome. To fill the gaps in causal responsibility left by individual-focused metrics, we formulate a metric for the causal responsibility of groups. To identify assertive agents that are causally responsible for the trajectory of an affected agent, we further formalise the types of assertive influences and propose a tiering algorithm for systematically identifying assertive agents. Finally, we use scenario-based simulations to illustrate the benefits of considering groups and how the emergence of group effects vary with interaction dynamics and the proximity of agents.