a review - Acta Technica Corviniensis

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CONCLUSIONS Based on the discussed research works we can conclude that the need for flexible communication between the agents is very important, especially when there is a shared resource to be used by the agents. We also realized that when designing such systems, a friendly user interface to control the system would be in the favor of the home inhabitants because these systems are very complex. From the illustration of the given works we can conclude that predicting future inhabitant’s device interaction is needed for home automation. On the other hand, since the prediction algorithms do not always provide 100% accuracy it should not be used alone for home automation. To avoid wrong prediction we can combine both prediction algorithms and reasoning techniques such as reinforcement-learning, this way we can make sure that unnecessary prediction outcomes are avoided. We can conclude from the discussed research work that the use of neural-networks significantly increases the automation of home appliances as well as a very user-friendly solution. We further think that such a user interface is very helpful; especially when it comes to people with disabilities, because there is not need for direct interaction with the system and also it is automatically configurable. We can conclude that FL is very suitable for heterogeneous home appliances, because fuzzy logic is very robust in headlining computing problems where we have many rules and devices to be addressed. We conclude that reinforcement learning is very straightforward to implement in smart-home automation application. The simplicity and robustness makes this method very desirable especially when dealing with situation where the agent may not be used. We suggest that future work could be to use a hardware prototype of multi-agent system to manage the smart-home environment. Hardware prototyping using FPGA has been used effectively in many areas and showed a great result. We believe that a multiagent hardware prototype could be very useful for home automation, as it could provide faster speed and more efficient power consumption than software based. REFERENCES [1.] Jaszczyk P, Król D (2010). Updatable multiagent OSGi architecture for smart home system. Agent and Multi-Agent Systems: Technologies and Applications. 6071: 370-379. [2.] Liu C, Liu F, Liu C, Wu H (2011). Multi-agent reinforcement learning based on K-means algorithm. Chinese Journal of Electronics. 20(3): 414-418. [3.] Medjahed H, Istrate D, Boudy J, Dorizzi B (2009). Human activities of daily living recognition using fuzzy logic for elderly home monitoring. In: IEEE International Conference on Fuzzy Systems held at Jeju Island, South Korea.IEEE, pp 2001-2006. [4.] Jae CM, Soon JK (2000). A Multi-Agent Architecture for Intelligent Home Network 56 ACTA TECHNICA CORVINIENSIS – Bulletin of Engineering Service Using Tuple Space Model. IEEEIEEE Transactions on Consumer Electronics. 46(3):791-794 [5.] Carrier N, Gelemter D (1989). Linda in Context. ACM Transactions on Communications. 1.32(4):444 – 458. [6.] Cheng-Fa T, Hang-Chang W (2002). MASSIHN: a multi-agent architecture for intelligent home network service. IEEE Transactions on Consumer Electronics. 48(3): 505 – 514. [7.] Alam MR, Reaz, MBI, Ali MAM, Samad SA, Hashim FH, Hamzah MK (2010). Human activity classification for smart home: A multiagent approach. In: IEEE Symposium on Industrial Electronics & Applications (ISIEA) held at Penang, Malaysia. IEEE, pp 511-514. [8.] Cook DJ, Huber M, Gopalratnam K, Youngblood M (2003). Learning to Control a Smart Home Environment. Available from http://ranger.uta.edu/holder/courses/cse6362 /pubs/Cook03.pdf [9.] Heierman EO, Cook DJ (2003). Improving Home Automation by Discovering Regularly Occurring Device Usage Patterns. Third IEEE International Conference on Data Mining held at Washington DC. IEEE, pp 537- 540. [10.] Sira PR, Cook DJ (2004). Identifying Tasks and Predicting Action in Smart Homes using Unlabeled Data. International Journal on Artificial Intelligence Tools. 13(1): 81-100. [11.] Lucian V, Arpad G (2004). Person Movement Prediction Using Neural Networks. Proceedings of the KI2004 International Workshop on Modeling and Retrieval of Context (MRC 2004) held at Ulm, Germany. pp. 618-623. [12.] Teoh CC and Tan CE (2010). A neural network approach towards reinforcing smart home security. In: 8th Asia-Pacific Symposium on Information and Telecommunication Technologies (APSITT), 2010 held at Kuching, Malaysia. IEEE, pp 1-5. [13.] Chan M, Hariton C, Ringeard P, Campo E (1995). Smart house automation system for the elderly and the disabled. IEEE International Conference on Systems, Man and Cybernetics held at Vancouver, Canada. IEEE, 2:1586-1589. [14.] Michael CM (1998). The Neural Network House: An Environment that Adapts to its Inhabitants. Proceedings of the American Association for Artificial Intelligence Spring Symposium on Intelligent Environments held at Menlo Park, CA. AAAI Press, pp. 110-114. [15.] Fei Z, Peter HN (2005). Real-time Embedded Face Recognition for Smart Home. IEEE Transactions on Consumer Electronics. 51(1): 183-190. [16.] Zainzinger HJ (1998). An Artificial Intelligence Based Tool for Home Automation Using MATLAB. Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence held at Taipei, Taiwan. IEEE, pp. 256-261. [17.] Nauck D (1994). A Fuzzy Perceptron as a Generic Model for Neuro-Fuzzy Approaches. 2013. Fascicule 2 [April–June]
ACTA TECHNICA CORVINIENSIS – Bulletin of Engineering Proceedings of the German GI-Workshop Fuzzy- Systeme held at Miinchen, Germancy. [18.] Hagras H, Colley M, Callaghan V, Clarke G, Duman H, and Holmes A (2002). A Fuzzy Incremental Synchronous Learning Technique for Embedded-Agents Learning and Control in Intelligent Inhabited Environments. Proceedings of the IEEE International Conference on Fuzzy Systems held at Honolulu, Hawaii. IEEE, pp.139-145. [19.] Mendel J, Wang L (1992). Generating Fuzzy Rules by Learning through Examples. IEEE Transactions on Systems, Man and Cybernetics. 22(6): 1414-1427. [20.] Doctor F, Hagras H, Callaghan V (2005). A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments. 35(1): 55-65. [21.] Wang Y, Huber M, Papudesi VN, Cook DJ (2003). User-Guided Reinforcement Learning of Robot Assistive Tasks for an Intelligent Environment. Proceedings of the IEEE/RJS International Conference on Intelligent Robots and Systems held at Las Vegas. IEEE,, 1:424- 429. [22.] Sajal D and Cook DJ (2004). Health Monitoring in an Agent-Based Smart Home. Proceedings of the International Conference on Smart Homes and Health Telematics (ICOST) held at Singapore. 8(3):238- 247. [23.] Gatzoulis C, Tang W, Wan TR (2004). Fuzzy Reinforcement Learning for an Evolving Virtual Servant Robot. IEEE International Workshop on Robot and Human Interactive Communication held at Kurashiki, Japan. IEEE, pp. 685- 690. [24.] Sutton RS, Barto AG (1998). Reinforcement Learning: An Introduction. MIT Press, Cambridge, Chapter 6, pp 148. [25.] Wilson SW (1994). ZCS: A Zeroth Level Classifier System. Evolutionary Computation. MIT Press. 2(1): 1-18. ACTA TECHNICA CORVINIENSIS – BULLETIN of ENGINEERING ISSN: 2067‐3809 [CD‐Rom, online] copyright © UNIVERSITY POLITEHNICA TIMISOARA, FACULTY OF ENGINEERING HUNEDOARA, 5, REVOLUTIEI, 331128, HUNEDOARA, ROMANIA http://acta.fih.upt.ro 2013. Fascicule 2 [April–June] 57
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