Improving Smart Environments with Knowledge Ecosystems

This paper presents a distributed cognitive architecture suitable for Ambient Intelligence applications. The key idea is to model an intelligent space as an ecosystem composed by artificial entities which collaborate with each other to perform an intelligent multi-sensor data fusion of both numerical and symbolic information. The semantics associated with the knowledge representation can be used to aid intelligent systems or human supervisors to take decisions according to situations and events occurring within the intelligent space. Experimental results are presented showing how this approach has been successfully applied to smart environments for elderly and disabled.

[1]  Yuan Yan Tang,et al.  Signal denoising using wavelets and block hidden Markov model , 2005, Int. J. Pattern Recognit. Artif. Intell..

[2]  Zu-De Zhou,et al.  Distributed Temperature Control System Based on Multi-Sensor Data Fusion , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[3]  Hector J. Levesque,et al.  Expressiveness and tractability in knowledge representation and reasoning 1 , 1987, Comput. Intell..

[4]  E. Odum Fundamentals of ecology , 1972 .

[5]  F.E. White,et al.  Managing data fusion systems in joint and coalition warfare , 1999, Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers (Cat. No.CH37020).

[6]  H. Odum,et al.  Fundamentals of ecology , 1954 .

[7]  Henry A. Kautz,et al.  Location-Based Activity Recognition using Relational Markov Networks , 2005, IJCAI.

[8]  Donald E. Brown,et al.  Health-status monitoring through analysis of behavioral patterns , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  Hong Bao,et al.  A fire detecting method based on multi-sensor data fusion , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[10]  Maurizio Piaggio,et al.  Pre-emptive versus non-pre-emptive real time scheduling in intelligent mobile robotics , 2000, J. Exp. Theor. Artif. Intell..

[11]  A.H.G. Al-Dhaher,et al.  Multi-sensor data fusion architecture , 2004, Proceedings. Second International Conference on Creating, Connecting and Collaborating through Computing.

[12]  Alexei Makarenko,et al.  Decentralized Bayesian algorithms for active sensor networks , 2006, Inf. Fusion.

[13]  Hani Hagras,et al.  A hierarchical fuzzy-genetic multi-agent architecture for intelligent buildings online learning, adaptation and control , 2003, Inf. Sci..