Decentralized Models for Use in a Real-World Personal Assistant Agent Scenario

Many approaches have been introduced for representing and solving multiagent coordination problems. Unfortunately, these methods make assumptions that limit their usefulness when combined with human operators and real-life hardware and software. In this paper, we discuss the problem of using agents in conjunction with human operators to improve coordination as well as possible models that could be used in these problems. Our approach — Space Collaboration via an Agent Network (SCAN) — enables proxy agents to represent each of the stakeholder agencies in a space setting and shows how the SCAN agent network could facilitate collaboration by identifying opportunities and methods of collaboration. We discuss this approach as well as the challenges in extending models to 1) take advantage of human input, 2) deal with the limited and uncertain information that will be present and 3) combat the scalability issues in solution methods for a large number of decentralized agents. As a first step toward providing rich models for these domains, we describe a method to bound the solution quality due to bounded model uncertainty.