Automated Verification of Social Laws for Continuous Time Multi-Robot Systems
Automated Verification of Social Laws for Continuous Time Multi-Robot Systems
This academic paper presents a method for verifying the robustness of social laws in continuous time multi-robot systems where agents act concurrently. The authors propose a compilation technique from temporal multi-agent planning to temporal planning to automatically check if a given social law prevents interference and deadlocks. The approach is evaluated theoretically and empirically on a real multi-robot system and across several benchmark domains using temporal planners.
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Who issued it
Who it governs
Researchers and robotics engineers working on formal verification and multi-agent coordination should read this to understand algorithmic approaches to social law robustness.
What the document contains
The paper addresses the automated verification of social law robustness in continuous-time multi-robot systems where actions take time and execute concurrently. It provides a formal model combining MA-STRIPS and PDDL 2.1, defines rational robustness under concurrent execution, and presents a compilation-based algorithm to detect failures, deadlocks, and goal non-achievement.
The document does not provide legal regulations, binding policy standards for industrial deployment, or enforcement mechanisms outside of academic planning formalism.
- Social laws restrict agent actions to prevent interference in multi-agent environments.
- Extending social law verification to continuous time requires handling durative actions and concurrent execution.
- The proposed approach compiles temporal multi-agent social law verification into standard temporal planning problems.
- Empirical evaluation demonstrates that temporal planners can efficiently find flaws in non-robust social laws, though proving robustness remains challenging.
· Abstract · Introduction · Preliminaries · Waiting in Temporal Social Laws · Properties Of Temporal Social Laws · Verifying Rational Robustness · Compilation Correctness · Empirical Evaluation · Conclusion
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
0/5Clear responsibility for AI outcomes
screening score · 2026-10-23
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-23
Transparency
0/5Openness about how AI systems work and are used
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-23
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
screening score · 2026-10-23