Optimize My Schedule but Keep It Flexible: Distributed Multi-Criteria Coordination for Personal Assistants

Persistent Assistants: Living and Working with AI

Volume

Persistent Assistants: Living and Working with AI

Contents

Research projects have begun focusing on deploying personal assistant agents to coordinate users in such diverse environments as offices, distributed manufacturing or design centers, and in support of first responders for emergencies. In such environments, distributed constraint optimization (DCOP) has emerged as a key technology for multiple collaborative assistants to coordinate with each other. Unfortunately, while previous work in DCOP only focuses on coordination in service of optimizing a single global team objective, personal assistants often require satisfying additional individual userspecified criteria. This paper provides a novel DCOP algorithm that enables personal assistants to engage in such multicriteria coordination while maintaining the privacy of their additional criteria. It uses n-ary NOGOODS implemented as private variables to achieve this. In addition, we've developed an algorithm that reveals only the individual criteria of a link and can speed up performance for certain problem structures. The key idea in this algorithm is that interleaving the criteria searches -- rather than sequentially attempting to satisfy the criteria -- improves efficiency by mutually constraining the distributed search for solutions. These ideas are realized in the form of private-g and public-g Multi-criteria ADOPT, built on top of ADOPT, one of the most efficient DCOP algorithms. We present our detailed algorithm, as well as some experimental results in personal assistant domains.