Managing Multiple Hypotheses with Agents to Handle Incomplete and Uncertain Data
Year: 2011
Type: article
Abstract: . In this paper, we study health monitoring, using ambulatory sensors, where the data available are limited, and can be both unreliable and ambiguous. Hence, the need to consider a person’s context: surrounding environment and previous situations. We propose studying multiple situational hypotheses, and the relations between hypotheses present and past. Such hypotheses are managed with a multi-agent system: the agents embody hypotheses on several levels of abstraction, from a general, rough scenario, down to precise states of both physiology and activity. These agents ’ hypotheses are evaluated and compared so that plausible hypotheses emerge. We discuss both the representation of situations, and multi-agent adaptive control mechanisms. This is a mainly theoretical approach, although these proposals are illustrated by an application on real data from a daily office life scenario. 1
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2906405042