Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded

A methodology for integrating asset administration shells and multi-agent systems

Industry 4.0 (I4.0) is promoting the digitization of industrial environments towards intelligent and distributed industrial automation systems based on Cyber-physical Systems (CPS). Currently, this digitization process is being leveraged by the Asset Administration Shell (AAS), which digitally describes an asset in a standardized and semantically unambiguous form throughout its lifecycle. However, more robust solutions based on autonomous AASs endowed with collaborative and intelligent capabilities, also called proactive AASs, are still in the early stages. In this context, Multi-agent Systems (MAS) are a key enabler to provide the required autonomy, intelligence and collaborative capabilities for the AASs. With this in mind, this paper presents a methodology positioned with respect to the Reference Architecture Model Industrie 4.0 (RAMI4.0) layers, which provides guidelines for integrating AASs and MAS, aiming to support the development of proactive AASs. The applicability of the proposed methodology was tested through the integration of AASs and MAS for a smallscale CPS demonstrator.The authors are grateful to the Foundation for Science and Technology (FCT), Portugal, for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). The author Lucas Sakurada thanks the FCT for the PhD Grant 2020.09234.BD.info:eu-repo/semantics/publishedVersio

Challenges for adaptation in agent societies

The final publication is available at Springer via http://dx.doi.org/[insert DOIAdaptation in multiagent systems societies provides a paradigm for allowing these societies to change dynamically in order to satisfy the current requirements of the system. This support is especially required for the next generation of systems that focus on open, dynamic, and adaptive applications. In this paper, we analyze the current state of the art regarding approaches that tackle the adaptation issue in these agent societies. We survey the most relevant works up to now in order to highlight the most remarkable features according to what they support and how this support is provided. In order to compare these approaches, we also identify different characteristics of the adaptation process that are grouped in different phases. Finally, we discuss some of the most important considerations about the analyzed approaches, and we provide some interesting guidelines as open issues that should be required in future developments.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, the European Cooperation in the field of Scientific and Technical Research IC0801 AT, and projects TIN2009-13839-C03-01 and TIN2011-27652-C03-01.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2014). Challenges for adaptation in agent societies. Knowledge and Information Systems. 38(1):1-34. https://doi.org/10.1007/s10115-012-0565-yS134381Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Abdallah S, Lesser V (2007) Multiagent reinforcement learning and self-organization in a network of agents. 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Two-Stage Dynamic Average Consensus in Asymmetric Networks

The main focus of this thesis is directed towards distributed control strategies for multi-agent systems. Given an asymmetric network of homogeneous agents with single-integrator dynamics and weighted links, it is desired to design a control rule for each agent using its local information as well as the information it receives from its neighbors to solve the average consensus problem. In other words, the global objective is to drive every agent's state to the average of the initial states of all agents (static average consensus) or the average of the reference inputs (dynamic average consensus). The main challenge, however, is to achieve these objectives in a general weighted network, i.e., when the graph representing the network is directed and each edge is weighted. To this end, a novel two-stage strategy is proposed, where in the first stage a mirror model is defined for every agent to compute its final state based on a standard consensus protocol. Then in the second stage, the standard update rule is adjusted for each agent accordingly to account for the discrepancy between the the final state of its mirror model and the desired average consensus state. Simulations demonstrate the effectiveness of the proposed control strategies in different scenarios

Agreement technologies and their use in cloud computing environments

The final publication is available at Springer via http://dx.doi.org/10.1007/s13748-012-0031-9[EN] Nowadays, cloud computing is revolutionizing the services provided through the Internet to adapt itself in order to keep the quality of its services. Recent research foresees the advent of a new discipline of agent-based cloud computing systems that can make decisions about adaption in an uncertain environment. This paper discusses the role of argumentation in the next generation of agreement technologies and its use in cloud computing environments.This work is supported by the Spanish government (MICINN), project reference: TIN2012-36586-C03-01.Heras Barberá, SM.; De La Piedra, F.; Julian Inglada, VJ.; Rodríguez, S.; Botti Navarro, VJ.; Bajo, J.; Corchado, JM. (2012). Agreement technologies and their use in cloud computing environments. Progress in Artificial Intelligence. 1(4):277-290. https://doi.org/10.1007/s13748-012-0031-9S27729014European Comission: The Future of Cloud Computing. Technical report (2010)Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization. In: SOSP03 Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, pp. 164–177. ACM, New York (2003)Wang, L., et. al.: Scientific cloud computing: early definition and experience. In: 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), pp. 825–830. IEEE Press (2008)Talia, D.: Clouds meet agents: toward intelligent cloud services. Internet Comput. IEEE 16(2), 78–81 (2012). doi: 10.1109/MIC.2012.28Heras, S.: Case-Based Argumentation Framework for Agent Societies. PhD thesis, Universitat Politècnica de València. http://hdl.handle.net/10251/12497 (2011)Ashton, K.: That ‘internet of things’ thing. RFID J. (2009). http://www.rfidjournal.com/article/view/4986Klusch, M.: Information agent technology for the Internet: a Survey. Data Knowl. Eng. 36, 337–372 (2001)Schaffer, H.E.: X as a Service. Cloud Computing, and the Need for Good Judgment IT Professional 11(5), 4–5 (2009). doi: 10.1109/MITP.2009.112Richardson, L., Ruby, S.: RESTful Web Services, Web services for the real world O’Reilly, Media, May, p. 454 (2007)GlusterFS Developers. The Gluster web site. http://www.gluster.org (2012)Chodorow, K., Dirolf, M.: The Definitive Guide. O’Reilly Media, MongoDB (2010)Fuentes-Fernandez, R., Hassan, S., Pavon, J., Galan, J.M., Lopez-Paredes, A.: Metamodels for role-driven agent-based modelling. Comput. Math. Organ. Theory 18(1), 91–112 (2012)Jordán, J., et al.: A customer support application using argumentation in multi-agent systems. In: 14th International Conference on, Information Fusion, pp. 772–778 (2011)Heras, S., Jordán, J., Botti, V., Julián, V.: Argue to agree: a case-based argumentation approach. Int. J. Approx. Reasoning (2012, in press)Walton, D., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge University Press, Cambridge (2008)Bench-Capon, T., Sartor, G.: A model of legal reasoning with cases incorporating theories and values. Artif. Intell. 150(1–2), 97–143 (2003)Dignum, F., Weigand, H.: Communication and deontic logic. In: Information Systems Correctness and Reusability, pp. 242–260. World Scientific, Singapore (1995)Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)Lopez-Rodriguez, I., Hernandez-Tejera, M.: Software agents as cloud computing services. In: 9th International Conference on Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, vol. 88, pp. 271–276. Springer, Berlin (2011)Sim, K.M.: Towards complex negotiation for cloud economy. In: 5th International Conference on Advances in Grid and Pervasive Computing. LNCS, vol. 6104, pp. 395–406. Springer, Berlin (2010)Aversa, R., et al.: Cloud agency: a mobile agent based cloud system. In: International Conference on Complex, Intelligent and Software Intensive Systems, pp. 132–137. IEEE Computer Society Press, Washington, DC (2010)Cao, B., et al.: A service-oriented qos-assured and multi-agent cloud computing architecture. In: 1st International Conference on Cloud Computing. LNCS, vol. 5931, pp. 644–649. Springer, Berlin (2009)Rahwan, I., Simari, G. (eds.): Argumentation in Artificial Intelligence. Springer, Berlin (2009

Adaptation to criticality through organizational invariance in embodied agents

Many biological and cognitive systems do not operate deep within one or other regime of activity. Instead, they are poised at critical points located at phase transitions in their parameter space. The pervasiveness of criticality suggests that there may be general principles inducing this behaviour, yet there is no well-founded theory for understanding how criticality is generated at a wide span of levels and contexts. In order to explore how criticality might emerge from general adaptive mechanisms, we propose a simple learning rule that maintains an internal organizational structure from a specific family of systems at criticality. We implement the mechanism in artificial embodied agents controlled by a neural network maintaining a correlation structure randomly sampled from an Ising model at critical temperature. Agents are evaluated in two classical reinforcement learning scenarios: the Mountain Car and the Acrobot double pendulum. In both cases the neural controller appears to reach a point of criticality, which coincides with a transition point between two regimes of the agent's behaviour. These results suggest that adaptation to criticality could be used as a general adaptive mechanism in some circumstances, providing an alternative explanation for the pervasive presence of criticality in biological and cognitive systems.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0525

Blockchain Solutions for Multi-Agent Robotic Systems: Related Work and Open Questions

The possibilities of decentralization and immutability make blockchain probably one of the most breakthrough and promising technological innovations in recent years. This paper presents an overview, analysis, and classification of possible blockchain solutions for practical tasks facing multi-agent robotic systems. The paper discusses blockchain-based applications that demonstrate how distributed ledger can be used to extend the existing number of research platforms and libraries for multi-agent robotic systems.Comment: 5 pages, FRUCT-2019 conference pape

Wealth distribution across communities of adaptive financial agents

This paper studies the trading volumes and wealth distribution of a novel agent-based model of an artificial financial market. In this model, heterogeneous agents, behaving according to the Von Neumann and Morgenstern utility theory, may mutually interact. A Tobin-like tax (TT) on successful investments and a flat tax are compared to assess the effects on the agents' wealth distribution. We carry out extensive numerical simulations in two alternative scenarios: i) a reference scenario, where the agents keep their utility function fixed, and ii) a focal scenario, where the agents are adaptive and self-organize in communities, emulating their neighbours by updating their own utility function. Specifically, the interactions among the agents are modelled through a directed scale-free network to account for the presence of community leaders, and the herding-like effect is tested against the reference scenario. We observe that our model is capable of replicating the benefits and drawbacks of the two taxation systems and that the interactions among the agents strongly affect the wealth distribution across the communities. Remarkably, the communities benefit from the presence of leaders with successful trading strategies, and are more likely to increase their average wealth. Moreover, this emulation mechanism mitigates the decrease in trading volumes, which is a typical drawback of TTs.Comment: 18 pages, 7 figures, published in New Journal of Physic