Comparing a Traditional and a Multi-Agent Load-Balancing System

This article presents a comparison between agent and non-agent based approaches to building network-load-balancing systems. In particular, two large software systems are compared, one traditional and the other agent-based, both performing the same load balancing functions. Due to the two different architectures, several differences emerge. The differences are analyzed theoretically and practically in terms of design, scalability and fault-tolerance. The advantages and disadvantages of both approaches are presented by combining an analysis of the system and gathering the experience of designers, developers and users. Traditionally, designers specify rigid software structure, while for multi-agent systems the emphasis is on specifying the different tasks and roles, as well as the interconnections between the agents that cooperate autonomously and simultaneously. The major advantages of the multi-agent approach are the introduced abstract design layers and, as a consequence, the more comprehendible top-level design, the increased redundancy, and the improved fault tolerance. The major improvement in performance due to the agent architecture is observed in the case of one or more failed computers. Although the agent-oriented design might not be a silver bullet for building large distributed systems, our analysis and application confirm that it does have a number of advantages over non-agent approaches

Evaluating the applicability of multi-agent software for implementing distributed industrial data management approaches

Distributed approaches to industrial control or information management problems are often tackled using Multi-agent methods. Multi-Agent systems – solutions resulting from taking a Multi-agent based approaches - often come with a certain amount of “overhead” such as communication systems, but can provide a helpful tool with the design and implementation. In this paper, a distributed data management problem is addressed with both a bespoke approach developed specifically for this problem and a more general Multi-agent approach. The two approaches are compared using architecture and software metrics. The software metric results show similar results, although overall the bespoke approach was more appropriate for the particular application examined. The architectural analysis indicates that the main reason for this difference is the communication and computation overhead associated with the agent-based system. It was not within the scope of this study to compare the two approaches under multiple application scenarios.BoeingThis is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/978-3-319-15159-5_1

Uncertainty propagation in multi-agent systems for multidisciplinary optimization problems

International audienceBecause of uncertainties on models and variables, deterministic multidisciplinary optimization may achieve under-sizing (without design margins) or over-sizing (with arbitrary design margins). Thus, it is necessary to implement multidisciplinary optimization methods that take into account the uncertainties in order to design systems that are both robust and reliable. Probabilistic methods such as reliability-based design optimization (RBDO) or robust design methods, provide designers with powerful decision-making tools but may involve very time-consuming calculations. New optimization approaches have been developed to deal with such complex problems. Auto-adaptive Multi-Agent Systems (AMAS) is a new approach developed recently, allowing to take into account the various aspects of a multidisciplinary optimization problem (multi-level, computation burden etc.). This approach was suggested for solving complex deterministic optimization problem. Now, the question of the integration of uncertainties in this multi-agent based optimization arises. The aim of this paper is to propose a new methodology for integrating the treatment of uncertainties in an adaptive multi-agent system for sequential optimization. The developed method employs a single loop process in which cycles of deterministic optimization alternate with evaluations of the system reliability. For each cycle, the optimization and the reliability analysis are decoupled from each other. The reliability analysis is carried out at agent level and only after the resolution of the deterministic optimization, to verify the feasibility of the constraints under uncertainties. Following the probabilistic study, the constraints violated (with low reliability) are shifted to the area of feasibility by integrating adaptive safety coeficients whose calculations are based on the agent-level reliability information. The method developed is applied to a conceptual aircraft design problem

Designing a Cockpit Functionalities Architecture for Trajectory Based Operations

Trajectory Based Operations (TBO) will require new procedures and systems to achieve a suitable automation of air traffic operations. Procedures and systems for automated operations are closely related and therefore frequently they need to be modeled in a combined way. Our group is currently employing recent agent-oriented methodological approaches to obtain conceptual models about TBO scenarios. Conceptual models define roles of air traffic entities as well as their interactions together with a detailed description of the entities’ architecture and dynamic behaviour. In this paper we present a cockpit functionality architecture built upon a methodological analysis and design of a TBO scenario as a multi-agent system. The proposed design has the advantage of mapping to an executable model for analytical simulation of TBO concepts and its modular architecture allows for a progressive integration of additional underlying models with specific functionalities

Advances in infrastructures and tools for multiagent systems

In the last few years, information system technologies have focused on solving challenges in order to develop distributed applications. Distributed systems can be viewed as collections of service-provider and ser vice-consumer components interlinked by dynamically defined workflows (Luck and McBurney 2008).Alberola Oltra, JM.; Botti Navarro, VJ.; Such Aparicio, JM. (2014). Advances in infrastructures and tools for multiagent systems. Information Systems Frontiers. 16:163-167. doi:10.1007/s10796-014-9493-6S16316716Alberola, J. M., Búrdalo, L., Julián, V., Terrasa, A., & García-Fornes, A. (2014). An adaptive framework for monitoring agent organizations. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9478-x .Alfonso, B., Botti, V., Garrido, A., & Giret, A. (2014). A MAS-based infrastructure for negotiation and its application to a water-right market. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9443-8 .Andrighetto, G., Castelfranchi, C., Mayor, E., McBreen, J., López-Sánchez, M., & Parsons, S. (2013). (Social) norm dynamics. In G. Andrighetto, G. Governatori, P. Noriega, & L. W. van der Torre (Eds.), Normative multi-agent systems (pp. 135–170). Dagstuhl: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik.Baarslag, T., Fujita, K., Gerding, E. H., Hindriks, K., Ito, T., Jennings, N. R., et al. (2013). Evaluating practical negotiating agents: results and analysis of the 2011 international competition. Artificial Intelligence, 198, 73–103.Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747–761.Campos, J., Esteva, M., López-Sánchez, M., Morales, J., & Salamó, M. (2011). Organisational adaptation of multi-agent systems in a peer-to-peer scenario. Computing, 91(2), 169–215.Carrera, A., Iglesias, C. A., & Garijo, M. (2014). Beast methodology: an agile testing methodology for multi-agent systems based on behaviour driven development. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9438-5 .Criado, N., Such, J. M., & Botti, V. (2014). Norm reasoning services. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9444-7 .Del Val, E., Rebollo, M., & Botti, V. (2014). Enhancing decentralized service discovery in open service-oriented multi-agent systems. Journal of Autonomous Agents and Multi-Agent Systems, 28(1), 1–30.Denti, E., Omicini, A., & Ricci, A. (2002). Coordination tools for MAS development and deployment. Applied Artificial Intelligence, 16(9–10), 721–752.Dignum, V., & Dignum, F. (2012). A logic of agent organizations. Logic Journal of IGPL, 20(1), 283–316.Ferber, J., & Gutknecht, O. (1998). A meta-model for the analysis and design of organizations in multi-agent systems. In Multi agent systems. Proceedings. International Conference on (pp. 128–135). IEEE.Fogués, R. L., Such, J. M., Espinosa, A., & Garcia-Fornes, A. (2014). BFF: a tool for eliciting tie strength and user communities in social networking services. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9453-6 .Garcia, E., Giret, A., & Botti, V. (2011). Evaluating software engineering techniques for developing complex systems with multiagent approaches. Information and Software Technology, 53(5), 494–506.Garcia-Fornes, A., Hübner, J., Omicini, A., Rodriguez-Aguilar, J., & Botti, V. (2011). Infrastructures and tools for multiagent systems for the new generation of distributed systems. Engineering Applications of Articial Intelligence, 24(7), 1095–1097.Jennings, N., Faratin, P., Lomuscio, A., Parsons, S., Sierra, C., & Wooldridge, M. (2001). Automated negotiation: prospects, methods and challenges. International Journal of Group Decision and Negotiation, 10(2), 199–215.Jung, Y., Kim, M., Masoumzadeh, A., & Joshi, J. B. (2012). A survey of security issue in multi-agent systems. Artificial Intelligence Review, 37(3), 239–260.Kota, R., Gibbins, N., & Jennings, N. R. (2012). Decentralized approaches for self-adaptation in agent organizations. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 7(1), 1.Kraus, S. (1997). Negotiation and cooperation in multi-agent environments. Artificial Intelligence, 94(1), 79–97.Lin, Y. I., Chou, Y. W., Shiau, J. Y., & Chu, C. H. (2013). Multi-agent negotiation based on price schedules algorithm for distributed collaborative design. Journal of Intelligent Manufacturing, 24(3), 545–557.Luck, M., & McBurney, P. (2008). Computing as interaction: agent and agreement technologies.Luck, M., McBurney, P., Shehory, O., & Willmott, S. (2005). Agent technology: Computing as interaction (A roadmap for agent based computing). AgentLink.Ossowski, S., & Menezes, R. (2006). On coordination and its significance to distributed and multiagent systems. Concurrency and Computation: Practice and Experience, 18(4), 359–370.Ossowski, S., Sierra, C., & Botti. (2013). Agreement technologies: A computing perspective. In Agreement Technologies (pp. 3–16). Springer Netherlands.Pinyol, I., & Sabater-Mir, J. (2013). Computational trust and reputation models for open multi-agent systems: a review. Artificial Intelligence Review, 40(1), 1–25.Ricci, A., Piunti, M., & Viroli, M. (2011). Environment programming in multi-agent systems: an artifact-based perspective. Autonomous Agents and Multi-Agent Systems, 23(2), 158–192.Sierra, C., & Debenham, J. (2006). Trust and honour in information-based agency. In Proceedings of the 5th international conference on autonomous agents and multi agent systems, (p. 1225–1232). New York: ACM.Sierra, C., Botti, V., & Ossowski, S. (2011). Agreement computing. KI-Knstliche Intelligenz, 25(1), 57–61.Vasconcelos, W., García-Camino, A., Gaertner, D., Rodríguez-Aguilar, J. A., & Noriega, P. (2012). Distributed norm management for multi-agent systems. Expert Systems with Applications, 39(5), 5990–5999.Wooldridge, M. (2002). An introduction to multiagent systems. New York: Wiley.Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: theory and practice. Knowledge Engineering Review, 10(2), 115–152

An agent-based industrial cyber-physical system deployed in an automobile multi-stage production system

Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitivity. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches such as those based on Multi-agent Systems (MAS) are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers. © 2020, Springer Nature Switzerland AG.info:eu-repo/semantics/publishedVersio

Optimism Based Exploration in Large-Scale Recommender Systems

Bandit learning algorithms have been an increasingly popular design choice for recommender systems. Despite the strong interest in bandit learning from the community, there remains multiple bottlenecks that prevent many bandit learning approaches from productionalization. Two of the most important bottlenecks are scaling to multi-task and A/B testing. Classic bandit algorithms, especially those leveraging contextual information, often requires reward for uncertainty estimation, which hinders their adoptions in multi-task recommender systems. Moreover, different from supervised learning algorithms, bandit learning algorithms emphasize greatly on the data collection process through their explorative nature. Such explorative behavior induces unfair evaluation for bandit learning agents in a classic A/B test setting. In this work, we present a novel design of production bandit learning life-cycle for recommender systems, along with a novel set of metrics to measure their efficiency in user exploration. We show through large-scale production recommender system experiments and in-depth analysis that our bandit agent design improves personalization for the production recommender system and our experiment design fairly evaluates the performance of bandit learning algorithms

Agent-based hybrid framework for decision making on complex problems

Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents\u27 track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified

Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning

Integrating robotic systems in architectural and construction processes is of core interest to increase the efficiency of the building industry. Automated planning for such systems enables design analysis tools and facilitates faster design iteration cycles for designers and engineers. However, generic task-and-motion planning (TAMP) for long-horizon construction processes is beyond the capabilities of current approaches. In this paper, we develop a multi-agent TAMP framework for long horizon problems such as constructing a full-scale building. To this end we extend the Logic-Geometric Programming framework by sampling-based motion planning,a limited horizon approach, and a task-specific structural stability optimization that allow an effective decomposition of the task. We show that our framework is capable of constructing a large pavilion built from several hundred geometrically unique building elements from start to end autonomously

Parallelisation strategies for agent based simulation of immune systems

Background In recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches. Results This paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment. Conclusions Our performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware