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Enhancing Multiagent Technology Solutions for Efficient Planning in Ambient Intelligence

This paper explores the application of multiagent technology in ambient intelligence, particularly within domotic environments. It introduces a two-tier agent model consisting of Operative Semi-Agents (OSA) and Cooperative Semi-Agents (CSA) to effectively plan and execute activities among connected devices. The study addresses current limitations in JADE/LEAP platforms and proposes solutions to enhance inter-device communication and execution efficiency. Experimental results demonstrate the potential for improved planning algorithms while setting the stage for future evaluations in real-world scenarios.

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Enhancing Multiagent Technology Solutions for Efficient Planning in Ambient Intelligence

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  1. Multiagent Technology Solutions for Planning in Ambient Intelligence Nicola Gatti, Francesco Amigoni, Marco Rolando {ngatti, amigoni}@elet.polimi.it, marco.rolando@gmail.com DEI, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy

  2. Application Scenario

  3. Domotic Agency Paradigm • Agent = • OSA (Operative Semi-Agent) • +CSA (Cooperative SA) DomoticAgency Majordomo CSA OSA

  4. Planning Devices’ Activities [Amigoni et al., T-SMC-A 2005] CSA 4 ? Majordomo ? ? CSA 3 CSA 1 ? CSA 2

  5. Employing Agent Technologies • Protocols: • FIPA interactionprotocols • Ontologies: • RDF • Software platform: • JADE/LEAP

  6. Technological Open Issues • JADE/LEAP is notfully appropriate to develop the whole system • The operative semi-agent of each device needs to run on the device • A JVM must run on each device, but computationally light devices cannot support JVM • A LEAP platform must run on each device, but inter-platform communication is supported only on TCP/IP protocol (and not, e.g., on Bluetooth) • The cooperative semi-agent of each device needs to move from the device to a platform with high computational capabilities • CSA’s classes need to be sent from the device to the platform by LEAP, but JVM for mobile devices does not support reflection • It is unreasonable that platforms have the classes of all the possible CSAs • As a result, JADE/LEAP must be improved to address such issues

  7. Our Improvements

  8. Experimental Setting and Result G A C D B I L E F G H Q M N O P R S

  9. Conclusions and Future Works • Our contributions • Development of a platformexploitingcurrent multiagent technologies for planning in AmbientIntelligence • Stretching of current technologies to address mobile devices • Promisingexperimentalevaluation in simple case studies • In future • Wewill experimentally evaluate our proposal in concrete settings • Wewillimproveefficiency in the planning algorithm

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