  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.
