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The smart house is a system composed of several cooperating agents, which make it possible to act autonomously in performing tasks, that in conventional houses are done by their inhabitants. The current work proposes a smart house capable: (a) of identifying the inhabitants in the house, (b) to adapt itself to the inhabitants preferences, as well as to modify itself to changes on these preferences, and (c)to maintain optimal levels of safety, energy saving and comfort. This work develops an identification and adaptation system for the smart house, based on a multiagent methodology. This approach simplifies the implementation of the communication policy among the agents; besides, it gives more flexibility to the system, by delegating to each agent a specific job in the house. New jobs are added to the system by simply developing the necessary agents to execute them. The system is built through the definition of all the tasks for the house; at this point, the agents and their relationships are defined, in order to properly execute the tasks. This system uses a pervasive human footstep sensor capable of recognizing the inhabitants of the house, through their weights and the characteristics of their locomotion. A neural network is used to learn and adapt to the daily habits of each individual, as well as to properly negotiate the changes in these patterns of behavior. In order to validate our idea, experiments were done with a room’s lighting system, the results of which show a very reliable system, capable to perform personalized actions in the house.

A Pervasive Identification and Adaptation System for the Smart House