Proactive Intention Recognition for Home Ambient Intelligence

We explore a coherent combination of two jointly implemented logic programming based systems, namely those of Evolution Prospection and Intention Recognition, to address a number of issues pertinent for Ambient Intelligence (AmI), namely in the home environment context. The Evolution Prospection system designs and implements several kinds of well-studied preferences and useful environment-triggering constructs for decision making. These enable a convenient declarative encoding of users’ preferences and needs, as well as reactive constructs like goal triggering rules. The other system performs intention recognition by means of Causal Bayes Nets and a planner. This approach to intention recognition is appropriate to tackle several AmI issues, such as security and emergency. We also present a novel method for collective intention recognition to allow tackling the case where multiple users are of concern. We exemplify our methods with examples in the elder care domain as it is one typical concern in the home environ-