Managing Multiple Hypotheses with Agents to Handle Incomplete and Uncertain Data

Benoît Vettier (1) , Laure Amate (1) , Catherine Garbay (1) , Julie Fontecave-Jallon (2) , Pierre Baconnier (2)

1 AMA - Analyse de données, Modélisation et Apprentissage automatique [Grenoble]
2 TIMC-IMAG-PRETA - Physiologie cardio-Respiratoire Expérimentale Théorique et Appliquée

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 o ce life scenario.

Domaines: Apprentissage [cs.LG]; Intelligence artificielle [cs.AI]

Benoît Vettier, Laure Amate, Catherine Garbay, Julie Fontecave-Jallon, Pierre Baconnier. Managing Multiple Hypotheses with Agents to Handle Incomplete and Uncertain Data. URMASSN'11 - First workshop on Uncertainty Reasoning and Multi-Agent Systems for Sensor Networks, Jun 2011, Belfast, Northern Ireland, United Kingdom. 12p. ⟨hal-00742112⟩