International Workshops of PAAMS 2013, Salamanca, Spain, May 22-24, 2013. Proceedings

This book constitutes the refereed proceedings of the Workshops which complemented the 11th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2013, held in Salamanca, Spain, in May 2013. This volume presents the papers that have been accepted for the workshops: Workshop on Agent-based Approaches for the Transportation Modeling and Optimization, Workshop on Agent-Based Solutions for Manufacturing and Supply Chain, Workshop on User-Centric Technologies and Applications, Workshop on Conflict Resolution in Decision Making, Workshop on Multi-Agent System Based Learning Environments, Workshop on Multi-agent based Applications for Sustainable Energy Systems, Workshop on Agents and multi-agent Systems for AAL and e-Healt

Implementing MAS agreement processes based on consensus networks

[EN] Consensus is a negotiation process where agents need to agree upon certain quantities of interest. The theoretical framework for solving consensus problems in dynamic networks of agents was formally introduced by Olfati-Saber and Murray, and is based on algebraic graph theory, matrix theory and control theory. Consensus problems are usually simulated using mathematical frameworks. However, implementation using multi-agent system platforms is a very difficult task due to problems such as synchronization, distributed finalization, and monitorization among others. The aim of this paper is to propose a protocol for the consensus agreement process in MAS in order to check the correctness of the algorithm and validate the protocol. © Springer International Publishing Switzerland 2013.This work is supported by ww and PROMETEO/2008/051 projects of the Spanish government, CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, TIN2012-36586-C03-01 and PAID-06-11-2084.Palomares Chust, A.; Carrascosa Casamayor, C.; Rebollo Pedruelo, M.; Gómez, Y. (2013). Implementing MAS agreement processes based on consensus networks. Distributed Computing and Artificial Intelligence. 217:553-560. https://doi.org/10.1007/978-3-319-00551-5_66S553560217Argente, E.: et al: An Abstract Architecture for Virtual Organizations: The THOMAS approach. Knowledge and Information Systems 29(2), 379–403 (2011)Búrdalo, L.: et al: TRAMMAS: A tracing model for multiagent systems. Eng. Appl. Artif. Intel. 24(7), 1110–1119 (2011)Fogués, R.L., et al.: Towards Dynamic Agent Interaction Support in Open Multiagent Systems. In: Proc. of the 13th CCIA, vol. 220, pp. 89–98. IOS Press (2010)Luck, M., et al.: Agent technology: Computing as interaction (a roadmap for agent based computing). Eng. Appl. Artif. Intel. (2005)Mailler, R., Lesser, V.: Solving distributed constraint optimization problems using cooperative mediation. In: AAMAS 2004, pp. 438–445 (2004)Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE 95(1), 215–233 (2007)Pujol-Gonzalez, M.: Multi-agent coordination: Dcops and beyond. In: Proc. of IJCAI, pp. 2838–2839 (2011)Such, J.: et al: Magentix2: A privacy-enhancing agent platform. Eng. Appl. Artif. Intel. 26(1), 96–109 (2013)Vinyals, M., et al.: Constructing a unifying theory of dynamic programming dcop algorithms via the generalized distributive law. Autonomous Agents and Multi-Agent Systems 22, 439–464 (2011

Multi-agent system for integrating quality and process control in a home appliance production line

A current trend in manufacturing is the deployment of modular, distributed and intelligent control systems that introduce adaptation facing unexpected deviations and failures, namely in terms of production conditions and product demand fluctuation. The integration of quality and process control allows the implementation of dynamic self-adaptation procedures and feedback control loops to address a large variety of disturbances and changes in process parameters and variables, aiming to improve the production efficiency and the product quality. Multi-agent systems (MAS) technology (Wooldridge 2002)(Leitão et al. 2013) is suitable to face this challenge, offering an alternative way to design these adaptive systems, based on the decentralization of functions over distributed autonomous and cooperative agents, providing modularity, flexibility, adaptation and robustness. In spite of the potential benefits of the MAS technology, the number of deployed agent-based solutions in industrial environments, reported in the literature, are few, as illustrated in (Leitão et al. 2013) [colocar aqui referencia ao Pechoucek & Marik]. This chapter describes the development, installation and operation of a multi-agent system, designated as GRACE, integrating quality and process control to operate in a real home appliance production line, producing laundry washing machines, owned by Whirlpool and located in Naples, Italy. The use of the MAS technology acts as the intelligent and distributed infra-structure to support the implementation of real-time monitoring and feedback control loops that apply dynamic self-adaptation and optimization mechanisms to adjust the process and product variables. The agent-based solution was developed using the JADE (Java Agent DEvelopment Framework) framework and successfully installed in the industrial factory plant, contributing for demonstrating the effective applicability and benefits of the MAS technology, namely in terms of production efficiency and product quality.This work has been partly financed by the EU Commission, within the research contract GRACE coordinated by Univ. Politecnica delle Marche and having partners SINTEF, AEA srl, Instituto Politécnico de Bragança, Whirlpool Europe srl, Siemens AG.info:eu-repo/semantics/publishedVersio

Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions