Robust Multi-Agent Task Assignment in Failure-Prone and Adversarial Environments
This addresses the challenge of ensuring reliable performance in multi-agent autonomous systems, such as robotics, by accounting for agent failures, though it appears incremental as it builds on existing assignment frameworks.
The paper tackles the problem of robust multi-agent task assignment in environments where agents may fail stochastically or adversarially, presenting efficient algorithms that achieve optimal or near-optimal results.
The problem of assigning agents to tasks is a central computational challenge in many multi-agent autonomous systems. However, in the real world, agents are not always perfect and may fail due to a number of reasons. A motivating application is where the agents are robots that operate in the physical world and are susceptible to failures. This paper studies the problem of Robust Multi-Agent Task Assignment, which seeks to find an assignment that maximizes overall system performance while accounting for potential failures of the agents. We investigate both, stochastic and adversarial failures under this framework. For both cases, we present efficient algorithms that yield optimal or near-optimal results.