Towards a Model of Evolving Agents for Ambient Intelligence
Stefania Costantini, Pierangelo Dell’Acqua, Luı́s Moniz Pereira, Francesca Toni · 2007
We propose a general vision for agents in Ambient Intelligent applications, whereby agents monitor and train unintrusively human users, and learn their patterns of behavior by observing and generalizing their observations, but also by “imitating” them. Agents can also learn by “imitating” other agents, after being told by them. Within this vision, agents need to evolve to take into account what they learn from or about users, and as a result of monitoring the users. In this paper we focus on modelling, by means of dynamic-logic-like rules, the monitoring behavior of agents, and by modelling the corresponding evolution of the agents.