Efficient Task Collaboration with Execution Uncertainty
This addresses collaborative task allocation under uncertainty for multi-agent systems, but it appears incremental as it builds on existing mechanism design frameworks.
The paper tackles the task allocation problem with execution uncertainty by proposing a post-execution verification (PEV)-based mechanism, showing it maximizes social welfare if and only if agents' valuations are multilinear, and extends this to trust-based settings with similar conditions.
We study a general task allocation problem, involving multiple agents that collaboratively accomplish tasks and where agents may fail to successfully complete the tasks assigned to them (known as execution uncertainty). The goal is to choose an allocation that maximises social welfare while taking their execution uncertainty into account. We show that this can be achieved by using the post-execution verification (PEV)-based mechanism if and only if agents' valuations satisfy a multilinearity condition. We then consider a more complex setting where an agent's execution uncertainty is not completely predictable by the agent alone but aggregated from all agents' private opinions (known as trust). We show that PEV-based mechanism with trust is still truthfully implementable if and only if the trust aggregation is multilinear.