2017 IJCAI IJCAI 2017

Verifying Fault-tolerance in Parameterised Multi-Agent Systems

Abstract

We develop a technique to evaluate the fault-tolerance of a multi-agent system whose number of agents is unknown at design time. We present a method for injecting a variety of non-ideal behaviours, or faults, studied in the safety-analysis literature into the abstract agent templates that are used to generate an unbounded family of multi-agent systems with different sizes. We define the parameterised fault-tolerance problem as the decision problem of establishing whether any concrete system, in which the ratio of faulty versus non-faulty agents is under a given threshold, satisfies a given temporal-epistemic specification. We put forward a sound and complete technique for solving the problem for the semantical set-up considered. We present an implementation and a case study identifying the threshold under which the alpha swarm aggregation algorithm is robust to faults against its temporal-epistemic specifications.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Reinforcement Learning
🧭 Keyword Pioneer — temporal epistemic logic
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio