Community care system design and development with AUML

An approach to the development of an appropriate agent environment is described in which software researchers collaborate with environment builders to enhance the levels of cooperation and support provided within an integrated agent-oriented community system. Agent-oriented Unified Modelling Language (AUML) is a practical approach to the analysis, design, implementation and management of such a software agent system, whilst providing the power and expressiveness necessary to support the specification, design and organisation of a health care service. This paper describes the background of agent-based health care and the fundamental concepts of Agentoriented UML and outlines how this refreshing approach can be used in the analysis, design, development and organization of agent-based community health care systems. Our approach to building agent-oriented software development solutions emphasizes the importance of AUML as a fundamental initial step in producing agent-based architectures and applications. This approach aims to present an effective schedule and methodology for an agent software development process, by addressing the complex agent environments decomposition, abstraction, organization and software development process activities characteristics, whilst reducing the complexity of the complex agent systems' design and development by using and exploiting AUML's productivity potential

SCoRe: a Self-Organizing Multi-Agent System for Decision Making in Dynamic Software Developement Processes

International audienceSoftware systems are becoming more and more complex. A common dilemma faced by software engineers in building complex systems is the lack of method adaptability. However, existing agent-based methodologies and tools are developed for particular system and are not tailored for new problems. This paper proposes an architecture of a new tool based on SME for self-constructing customized method processes. Our approach is based on two pillars: the process fragment and the MAS meta-model. These two elements are both defined and considered under a specific agent-oriented perspective thus creating a peculiar approach. Our work is based on the self-organization of agents, making it especially suited to deal with highly dynamic systems such as the design of an interactive and adaptive software engineering process

SCoRe: a Self-Organizing Multi-Agent System for Decision Making in Dynamic Software Developement Processes

Software systems are becoming more and more complex. A common dilemma faced by software engineers in building complex systems is the lack of method adaptability. However, existing agent-based methodologies and tools are developed for particular system and are not tailored for new problems. This paper proposes an architecture of a new tool based on SME for self-constructing customized method processes. Our approach is based on two pillars: the process fragment and the MAS meta-model. These two elements are both defined and considered under a specific agent-oriented perspective thus creating a peculiar approach. Our work is based on the self-organization of agents, making it especially suited to deal with highly dynamic systems such as the design of an interactive and adaptive software engineering process

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

Evaluating how agent methodologies support the specification of the normative environment through the development process

[EN] Due to the increase in collaborative work and the decentralization of processes in many domains, there is an expanding demand for large-scale, flexible and adaptive software systems to support the interactions of people and institutions distributed in heterogeneous environments. Commonly, these software applications should follow specific regulations meaning the actors using them are bound by rights, duties and restrictions. Since this normative environment determines the final design of the software system, it should be considered as an important issue during the design of the system. Some agent-oriented software engineering methodologies deal with the development of normative systems (systems that have a normative environment) by integrating the analysis of the normative environment of a system in the development process. This paper analyses to what extent these methodologies support the analysis and formalisation of the normative environment and highlights some open issues of the topic.This work is partially supported by the PROMETEOII/2013/019, TIN2012-36586-C03-01, FP7-29493, TIN2011-27652-C03-00, CSD2007-00022 projects, and the CASES project within the 7th European Community Framework Program under the grant agreement No 294931.Garcia Marques, ME.; Miles, S.; Luck, M.; Giret Boggino, AS. (2014). Evaluating how agent methodologies support the specification of the normative environment through the development process. Autonomous Agents and Multi-Agent Systems. 1-20. https://doi.org/10.1007/s10458-014-9275-zS120Cossentino, M., Hilaire, V., Molesini, A., & Seidita, V. (Eds.). (2014). Handbook on agent-oriented design processes (Vol. VIII, 569 p. 508 illus.). Berlin: Springer.Akbari, O. (2010). A survey of agent-oriented software engineering paradigm: Towards its industrial acceptance. 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Verhagen (Eds.), Normative multi-agent systems, number 09121 in Dagstuhl seminar proceedings.Boella, G., Torre, L., & Verhagen, H. (2006). Introduction to normative multiagent systems. Computational and Mathematical Organization Theory, 12(2–3), 71–79.Bogdanovych, A., Esteva, M., Simoff, S., Sierra, C., & Berger, H. (2008). A methodology for developing multiagent systems as 3d electronic institutions. In M. Luck & L. Padgham (Eds.), Agent-Oriented Software Engineering VIII (Vol. 4951, pp. 103–117). Lecture Notes in Computer Science. Berlin: Springer.Boissier, O., Padget, J., Dignum, V., Lindemann, G., Matson, E., Ossowski, S., Sichman, J., & Vazquez-Salceda, J. (2006). Coordination, organizations, institutions and norms in multi-agent systems. LNCS (LNAI) (Vol. 3913).Bordini, R. H., Fisher, M., Visser, W., & Wooldridge, M. (2006). Verifying multi-agent programs by model checking. In Autonomous agents and multi-agent systems (Vol. 12, pp. 239–256). Hingham, MA: Kluwer Academic Publishers.Botti, V., Garrido, A., Giret, A., & Noriega, P. (2011). The role of MAS as a decision support tool in a water-rights market. In Post-proceedings workshops AAMAS2011 (Vol. 7068, pp. 35–49). Berlin: Springer.Breaux, T. (2009). Exercising due diligence in legal requirements acquisition: A tool-supported, frame-based approach. In Proceedings of the IEEE international requirements engineering conference (pp. 225–230).Breaux, T. D., & Baumer, D. L. (2011). Legally reasonable security requirements: A 10-year ftc retrospective. Computers and Security, 30(4), 178–193.Breaux, T. D., Vail, M. W., & Anton, A. I. (2006). Towards regulatory compliance: Extracting rights and obligations to align requirements with regulations. In Proceedings of the 14th IEEE international requirements engineering conference, RE ’06 (pp. 46–55). Washington, DC: IEEE Computer Society.Bresciani, P., Perini, A., Giorgini, P., Giunchiglia, F., & Mylopoulos, J. (2004). Tropos: An agent-oriented software development methodology. Autonomous Agents and Multi-Agent Systems, 8(3), 203–236.Cardoso, H. L., & Oliveira, E. (2008). A contract model for electronic institutions. In COIN’07: Proceedings of the 2007 international conference on Coordination, organizations, institutions, and norms in agent systems III (pp. 27–40).Castor, A., Pinto, R. C., Silva, C. T. L. L., & Castro, J. (2004). Towards requirement traceability in tropos. In WER (pp. 189–200).Chopra, A., Dalpiaz, F., Giorgini, P., & Mylopoulos, J. (2009). Modeling and reasoning about service-oriented applications via goals and commitments. ICST conference on digital business.Cliffe, O., Vos, M., & Padget, J. (2006). Specifying and analysing agent-based social institutions using answer set programming. In O. Boissier, J. Padget, V. Dignum, G. Lindemann, E. Matson, S. Ossowski, J. Sichman, & J. Vázquez-Salceda (Eds.), Coordination, organizations, institutions, and norms in multi-agent systems. Lecture Notes in Computer Science (Vol. 3913, pp. 99–113). Springer. Berlin.Criado, N., Argente, E., Garrido, A., Gimeno, J. A., Igual, F., Botti, V., Noriega, P., & Giret, A. (2011). Norm enforceability in Electronic Institutions? In Coordination, organizations, institutions, and norms in agent systems VI (Vol. 6541, pp. 250–267). Springer.Dellarocas, C., & Klein, M. (2001). Contractual agent societies. In R. Conte & C. Dellarocas (Eds.), Social order in multiagent systems (Vol. 2, pp. 113–133)., Multiagent Systems, Artificial Societies, and Simulated Organizations New York: Springer.DeLoach, S. A. (2008). Developing a multiagent conference management system using the o-mase process framework. In Proceedings of the international conference on agent-oriented software engineering VIII (pp. 168–181).DeLoach, S. A., & Garcia-Ojeda, J. C. (2010). O-mase; a customisable approach to designing and building complex, adaptive multi-agent systems. 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Lecture Notes in Computer Science (Vol. 3346, pp. 181–198). Berlin: Springer.d’Inverno, M., Luck, M., Noriega, P., Rodriguez-Aguilar, J., & Sierra, C. (2012). Communicating open systems, 186, 38–94.Elsenbroich, C., & Gilbert, N. (2014). Agent-based modelling. In Modelling norms (pp. 65–84). Dordrecht: Springer.Esteva, M., Rosell, B., Rodriguez, J. A., & Arcos, J. L. (2004). AMELI: An agent-based middleware for electronic institutions. In AAMAS04 (pp. 236–243).Fenech, S., Pace, G. J., & Schneider, G. (2009). Automatic conflict detection on contracts. In Proceedings of the 6th international colloquium on theoretical aspects of computing, ICTAC ’09 (pp. 200–214).Garbay, C., Badeig, F., & Caelen, J. (2012). Normative multi-agent approach to support collaborative work in distributed tangible environments. In Proceedings of the ACM 2012 conference on computer supported cooperative work companion, CSCW ’12 (pp. 83–86). New York, NY: ACM.Garcia, E., Giret, A., & Botti, V. (2011). Regulated open multi-agent systems based on contracts. In Information Systems Development (pp. 243–255).Garcia, E., Tyson, G., Miles, S., Luck, M., Taweel, A., Staa, T. V., & Delaney, B. (2012). An analysis of agent-oriented engineering of e-health systems. In 13th international eorkshop on sgent-oriented software engineering (AOSE-AAMAS).Garcia, E., Tyson, G., Miles, S., Luck, M., Taweel, A., Staa, T. V., and Delaney, B. (2013). Analysing the Suitability of Multiagent Methodologies for e-Health Systems. In Agent-Oriented Software Engineering XIII, volume 7852, pages 134–150. Springer-Verlag.Garrido, A., Giret, A., Botti, V., & Noriega, P. (2013). mWater, a case study for modeling virtual markets. In New perspectives on agreement technologies (Vol. Law, Gover, pp. 563–579). Springer.Gteau, B., Boissier, O., & Khadraoui, D. (2006). Multi-agent-based support for electronic contracting in virtual enterprises. IFAC Symposium on Information Control Problems in Manufacturing (INCOM), 150(3), 73–91.Hollander, C. D., & Wu, A. S. (2011). The current state of normative agent-based systems. Journal of Artificial Societies and Social Simulation, 14(2), 6.Hsieh, F.-S. (2005). Automated negotiation based on contract net and petri net. In E-commerce and web technologies. Lecture Notes in Computer Science (Vol. 3590, pp. 148–157).Kollingbaum, M., Jureta, I. J., Vasconcelos, W., & Sycara, K. (2008). Automated requirements-driven definition of norms for the regulation of behavior in multi-agent systems. In Proceedings of the AISB 2008 workshop on behaviour regulation in multi-agent systems, Aberdeen, Scotland, U.K., April 2008.Li, T., Balke, T., Vos, M., Satoh, K., & Padget, J. (2013). Detecting conflicts in legal systems. In Y. Motomura, A. Butler, & D. Bekki (Eds.), New Frontiers in Artificial Intelligence (Vol. 7856, pp. 174–189)., Lecture Notes in Computer Science Berlin Heidelberg: Springer.Lomuscio, A., Qu, H., & Solanki, M. (2010) Towards verifying contract regulated service composition. Journal of Autonomous Agents and Multi-Agent Systems (pp. 1–29).Lopez, F., Luck, M., & d’Inverno, M. (2006). A normative framework for agent-based systems. Computational and Mathematical Organization Theory, 12, 227–250.Lpez, F. y, Luck, M., & dInverno, M. (2006). A normative framework for agent-based systems. Computational and Mathematical Organization Theory, 12(2–3), 227–250.Mader, P., & Egyed, A. (2012). Assessing the effect of requirements traceability for software maintenance. In 28th IEEE International Conference on Software Maintenance (ICSM) (pp. 171–180), Sept 2012.Mao, X., & Yu, E. (2005). Organizational and social concepts in agent oriented software engineering. In AOSE IV. Lecture Notes in Artificial Intelligence (Vol. 3382, pp. 184–202).Meyer, J.-J. C., & Wieringa, R. J. (Eds.). (1993). Deontic logic in computer science: Normative system specification. Chichester, UK: Wiley.Okouya, D., & Dignum, V. (2008). Operetta: A prototype tool for the design, analysis and development of multi-agent organizations (demo paper). In AAMAS (pp. 1667–1678).Malone, T. W., Smith J. B., & Olson, G. M. (2001). Coordination theory and collaboration technology. Mahwah, NJ: Lawrence Erlbaum Associates.Oren, N., Panagiotidi, S., Vázquez-Salceda, J., Modgil, S., Luck, M., & Miles, S. (2009). Towards a formalisation of electronic contracting environments. COIN (pp. 156–171).Osman, N., Robertson, D., & Walton, C. (2006). Run-time model checking of interaction and deontic models for multi-agent systems. In AAMAS ’06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (pp. 238–240). New York, NY: ACM.Pace, G., Prisacariu, C., & Schneider, G. (2007). Model checking contracts a case study. In Automated technology for verification and analysis. Lecture Notes in Computer Science (Vol. 4762, pp. 82–97).Rotolo, A., & van der Torre, L. (2011). Rules, agents and norms: Guidelines for rule-based normative multi-agent systems. RuleML Europe, 6826, 52–66.Saeki, M., & Kaiya, H. (2008). Supporting the elicitation of requirements compliant with regulations. In CAiSE ’08 (pp. 228–242).Siena, A., Mylopoulos, J., Perini, A., & Susi, A. (2009). Designing law-compliant software requirements. In Proceedings of the 28th international conference on conceptual modeling, ER ’09 (pp. 472–486).Singh, M. P. Commitments in multiagent systems: Some history, some confusions, some controversies, some prospects.Solaiman, E., Molina-Jimenez, C., & Shrivastav, S. (2003). Model checking correctness properties of electronic contracts. In Service-oriented computing—ICSOC 2003. Lecture Notes in Computer Science (Vol. 2910, pp. 303–318). Berlin: Springer.Telang, P. R., & Singh, M. P. (2009). Conceptual modeling: Foundations and applications. Enhancing tropos with commitments (pp. 417–435).Vázquez-Salceda, J., Confalonieri, R., Gomez, I., Storms, P., Nick Kuijpers, S. P., & Alvarez, S. (2009). Modelling contractually-bounded interactions in the car insurance domain. DIGIBIZ 2009.Viganò, F., & Colombetti, M. (2007). Symbolic model checking of institutions. In ICEC (pp. 35–44).Walton, C. D. (2007). Verifiable agent dialogues. Journal of Applied Logic, 5(2):197–213, Logic-Based Agent Verification.Winkler, S., & Pilgrim, J. (2010). A survey of traceability in requirements engineering and model-driven development. Software and Systems Modeling (SoSyM), 9(4), 529–565.Wooldridge, M., Fisher, M., Huget, M., & Parsons, S. (2002). Model checking multi-agent systems with mable. In AAMAS02 (pp. 952–959). ACM

Designing normative open virtual enterprises

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Enterprise Information Systems on 23/03/2016, available online: http://www.tandfonline.com/10.1080/17517575.2015.1036927.[EN] There is an increasing interest on developing virtual enterprises in order to deal with the globalisation of the economy, the rapid growth of information technologies and the increase of competitiveness. In this paper we deal with the development of normative open virtual enterprises (NOVEs). They are systems with a global objective that are composed of a set of heterogeneous entities and enterprises that exchange services following a specific normative context. In order to analyse and design systems of this kind the multi-agent paradigm seems suitable because it offers a specific solution for supporting the social and contractual relationships between enterprises and for formalising their business processes. This paper presents how the Regulated Open Multiagent systems (ROMAS) methodology, an agent-oriented software methodology, can be used to analyse and design NOVEs. ROMAS offers a complete development process that allows identifying and formalising of the structure of NOVEs, their normative context and the interactions among their members. The use of ROMAS is exemplified by means of a case study that represents an automotive supply chain.This work was partially supported by the projects [PROMETEOII/2013/019], [TIN2012-36586-C03-01], [FP7-29493], [TIN2011-27652-C03-00] and [CSD2007-00022], and the CASES project within the 7th European Community Framework Programme [grant agreement number 294931].Garcia Marques, ME.; Giret Boggino, AS.; Botti Navarro, VJ. (2016). Designing normative open virtual enterprises. Enterprise Information Systems. 10(3):303-324. https://doi.org/10.1080/17517575.2015.1036927S303324103Cardoso, H. L., Urbano, J., Brandão, P., Rocha, A. P., & Oliveira, E. (2012). ANTE: Agreement Negotiation in Normative and Trust-Enabled Environments. Advances on Practical Applications of Agents and Multi-Agent Systems, 261-264. doi:10.1007/978-3-642-28786-2_33Chu, X. N., Tso, S. K., Zhang, W. J., & Li, Q. (2002). Partnership Synthesis for Virtual Enterprises. The International Journal of Advanced Manufacturing Technology, 19(5), 384-391. doi:10.1007/s001700200028Davidsson, P., & Jacobsson, A. (s. f.). Towards Norm-Governed Behavior in Virtual Enterprises. Studies in Computational Intelligence, 35-55. doi:10.1007/978-3-540-88071-4_3DeLoach, S. A., & Ojeda, J. C. G. (2010). O-MaSE: a customisable approach to designing and building complex, adaptive multi-agent systems. International Journal of Agent-Oriented Software Engineering, 4(3), 244. doi:10.1504/ijaose.2010.036984DI MARZO SERUGENDO, G., GLEIZES, M.-P., & KARAGEORGOS, A. (2005). Self-organization in multi-agent systems. The Knowledge Engineering Review, 20(2), 165-189. doi:10.1017/s0269888905000494Dignum, V. 2003. “A Model for Organizational Interaction: Based on Agents, Founded in Logic.” PhD diss., Utrecht University.Dignum, V., and F. Dignum. 2006.A Landscape of Agent Systems for the Real World. Technical Report 44-CS-2006-061. Utrecht: Institute of Information and Computing Sciences, Utrecht University.Dignum, V., Meyer, J.-J. C., Dignum, F., & Weigand, H. (2003). Formal Specification of Interaction in Agent Societies. Lecture Notes in Computer Science, 37-52. doi:10.1007/978-3-540-45133-4_4Garcia, E. 2013. “Engineering Regulated Open Multiagent Systems.” PhD diss., Universitat Politecnica de Valencia.Garcia, E., Giret, A., & Botti, V. (s. f.). Software Engineering for Service-Oriented MAS. Lecture Notes in Computer Science, 86-100. doi:10.1007/978-3-540-85834-8_9Garcia, E., Giret, A., & Botti, V. (2013). A Model-Driven CASE tool for developing and verifying regulated open MAS. Science of Computer Programming, 78(6), 695-704. doi:10.1016/j.scico.2011.10.009Garcia, 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. doi:10.1016/j.infsof.2010.12.012Garcia, E., Giret, A., & Botti, V. (2011). Regulated Open Multi-Agent Systems Based on Contracts. Information Systems Development, 243-255. doi:10.1007/978-1-4419-9790-6_20Garcia, E., Giret, A., & Botti, V. (2014). ROMAS Methodology. Handbook on Agent-Oriented Design Processes, 331-369. doi:10.1007/978-3-642-39975-6_11Hollander, C. D., & Wu, A. S. (2011). The Current State of Normative Agent-Based Systems. Journal of Artificial Societies and Social Simulation, 14(2). doi:10.18564/jasss.1750HORLING, B., & LESSER, V. (2004). A survey of multi-agent organizational paradigms. The Knowledge Engineering Review, 19(4), 281-316. doi:10.1017/s0269888905000317Julian, V., Rebollo, M., Argente, E., Botti, V., Carrascosa, C., & Giret, A. (2009). Using THOMAS for Service Oriented Open MAS. Lecture Notes in Computer Science, 56-70. doi:10.1007/978-3-642-10739-9_5Luck, M., Barakat, L., Keppens, J., Mahmoud, S., Miles, S., Oren, N., … Taweel, A. (2011). Flexible Behaviour Regulation in Agent Based Systems. Lecture Notes in Computer Science, 99-113. doi:10.1007/978-3-642-22427-0_8Meneguzzi, F., Modgil, S., Oren, N., Miles, S., Luck, M., & Faci, N. (2012). Applying electronic contracting to the aerospace aftercare domain. Engineering Applications of Artificial Intelligence, 25(7), 1471-1487. doi:10.1016/j.engappai.2012.06.004Presley, A., Sarkis, J., Barnett, W., & Liles, D. (2001). International Journal of Flexible Manufacturing Systems, 13(2), 145-162. doi:10.1023/a:1011131417956Saeki, M., & Kaiya, H. (2008). Supporting the Elicitation of Requirements Compliant with Regulations. Active Flow and Combustion Control 2018, 228-242. doi:10.1007/978-3-540-69534-9_18Such, J. M., García-Fornes, A., Espinosa, A., & Bellver, J. (2013). Magentix2: A privacy-enhancing Agent Platform. Engineering Applications of Artificial Intelligence, 26(1), 96-109. doi:10.1016/j.engappai.2012.06.009Telang, P. R., & Singh, M. P. (2009). Enhancing Tropos with Commitments. Lecture Notes in Computer Science, 417-435. doi:10.1007/978-3-642-02463-4_22Wooldridgey, M., & Ciancarini, P. (2001). Agent-Oriented Software Engineering: The State of the Art. Lecture Notes in Computer Science, 1-28. doi:10.1007/3-540-44564-1_

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

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

Multi-agent evolutionary systems for the generation of complex virtual worlds

Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer's intent through interaction, and encourages playful discovery