Efficient Task Collaboration with Execution Uncertainty

TL;DR

This paper investigates task allocation among multiple agents with uncertain execution outcomes, proposing mechanisms that maximize social welfare under certain valuation and trust aggregation conditions.

Contribution

It introduces PEV-based mechanisms for task allocation considering execution uncertainty and trust, establishing conditions for their truthful implementation.

Findings

PEV-based mechanisms can maximize social welfare with multilinear valuations.

Trust aggregation remains truthful if it is multilinear.

The framework extends to complex settings involving aggregated trust opinions.

Abstract

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.