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Electricity Markets and Intelligent Agents Part II: Agent Architectures and Capabilities

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Electricity Markets with Increasing Levels of Renewable Generation: Structure, Operation, Agent-based Simulation, and Emerging Designs

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 144))

Abstract

Agent technology is a relatively new and rapidly expanding area of research and development. The major motivations for the increasing interest in intelligent agents and multi-agent systems include the ability to provide solutions to problems that can naturally be regarded as a society of autonomous interacting components, to solve problems that are too large for a centralized agent to solve, and to provide solutions in situations where expertise is distributed. Electricity markets (EMs) are complex distributed systems, typically involving a variety of transactive techniques (e.g., centralized and bilateral market clearing). The agent-based approach is an ideal fit to the naturally distributed domain of EMs. Accordingly, a number of agent-based models and systems for EMs have been proposed in the technical literature. These models and systems exhibit fairly different features and make use of a diverse range of concepts. At present, there seems to be no agreed framework to analyze and compare disparate research efforts. Chapter 2 and this companion chapter claim that such a framework can be very important and instructive, helping to understand the interrelationships of disparate research efforts. Accordingly, Chap. 2 (Part I) and this chapter (Part II) introduce a generic framework for agent-based simulation of EMs. The complete framework includes three groups (or categories) of dimensions: market architecture, market structure and software agents. The first two groups were the subject of Chap. 2. This chapter discusses in considerable detail the last group of dimensions, labeled “software agents”, and composed by two distinct yet interrelated dimensions: agent architectures and agent capabilities.

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Notes

  1. 1.

    Although it is conceptually useful to distinguish between the two broad dimensions of “agent architectures” and “agent capabilities”, the distinction is not absolute (and may at times be somewhat arbitrary). Accordingly, the reader may find some overlap between these dimensions.

  2. 2.

    The architectures discussed in the first part of this section are based on the five basic types of agents presented in [25, Chap. 2]. The present section, however, is not intended as a summary of the authors’ views on agent architectures, and also presents a top-level view of a software agent.

  3. 3.

    For convenience, throughout this section we use the term “environment” to denote a generic agent environment. For software agents that represent market entities operating in a competitive energy market, the term should denote, naturally, this particular market environment.

  4. 4.

    Formally speaking, several conceptions of limited rationality for software agents have been proposed in the literature, notably bounded optimality—the capacity to generate maximally successful behavior given the available information and computational resources. Bounded optimal agents behave as well as possible, given their computational resources (see, e.g., [26, 27] for details).

  5. 5.

    Figure 3.1 gives an abstract view of an agent. Specific details about each component module and the control flow among modules need further architectural refinement (e.g., details about the decision-making mechanism). See [32, Chap. 2] and [33] for representative surveys of concrete agent architectures up to 1998, and [2, Chaps. 3–5] for a description of subsequent work.

  6. 6.

    The agent commonly operates in an environment populated by other agents and interacts with them to meet its design objectives. In Fig. 3.1, we have not included components that explicitly support such interaction (but see the next subsection).

  7. 7.

    For the sake of simplicity, we consider fairly direct representations of the agent’s beliefs and internal state. However, the internal state may be seen as a subset of the beliefs, namely beliefs about the environment where the agent operates.

  8. 8.

    Plans-as-recipes are often stored in an internal data structure called plan library (see, e.g., [34]). Also, researchers working in the area of agent architectures use different terms to denote structures similar in function to plans-as-recipes (e.g., knowledge areas [35], or plan templates or schemata [36]).

  9. 9.

    Software agents that can combine both reactive and deliberative reasoning are commonly refereed to as hybrid agents.

  10. 10.

    At this stage, a natural question to ask is: “Which of the architectures described in the previous subsection should be considered by agent designers?” The answer is: “All of them” [25, Chap. 27].

  11. 11.

    Although the three agent properties discussed earlier—autonomy, reactivity and pro-activeness—are mainly related to the micro aspects of agent technology (the agent level), social ability is closely related to the macro aspects of agent technology (the social level). In other words, we now move from the micro level of individual agents to the macro level of multi-agent systems.

  12. 12.

    http://www.lneg.pt/iedt/projectos/473/ (access date: September 2016).

  13. 13.

    Chapter 8 is entirely devoted to the agent-based system and presents a detailed description of its main features. The reader is therefore referred to it for details.

  14. 14.

    As noted earlier, a market to match the imbalances caused by the variability and uncertainty present in power systems is currently being developed.

References

  1. Macal, C., North, M.: Tutorial on agent-based modelling and simulation. J. Simul. 4, 151–162 (2010)

    Article  Google Scholar 

  2. Wooldridge, M.: An Introduction to Multi-agent Systems. Wiley, Chichester (2009)

    Google Scholar 

  3. Jennings, N.R., Sycara, K., Wooldridge, M.: A roadmap of agent research and development. Auton. Agents Multi Agent Syst. 1, 7–38 (1998)

    Article  Google Scholar 

  4. Pĕchouc̆ek, M., Mar̆ík, V.: Industrial deployment of multi-agent technologies: review and selected case studies. Auton. Agents Multi Agent Syst. 17, 397–431 (2008)

    Article  Google Scholar 

  5. Stoft, S.: Power System Economics: Designing Markets for Electricity. IEEE Press and Wiley Interscience, New York (2002)

    Book  Google Scholar 

  6. Wood, A., Wollenberg, B., Sheblé, G.: Power Generation, Operation, and Control. Wiley, Chichester (2014)

    Google Scholar 

  7. Kirschen, D., Strbac, G.: Fundamentals of Power System Economics. Wiley, Chichester (2004)

    Book  Google Scholar 

  8. Ventosa, M., Baíllo, A., Ramos, A., Rivier, M.: Electricity market modeling trends. Energy Policy 33(7), 897–913 (2005)

    Article  Google Scholar 

  9. Zhou, Z.Z., Chan, W.K., Chow, J.H.: Agent-based simulation of electricity markets: a survey of tools. Artif. Intell. Rev. 28, 305–342 (2007)

    Article  Google Scholar 

  10. Harp, S.A., Brignone, S., Wollenberg, B.F., Samad, T.: SEPIA: a simulator for the electric power industry agents. IEEE Control Syst. Mag. 20(4), 53–69 (2000)

    Article  Google Scholar 

  11. Koritarov, V.: Real-world market representation with agents: modeling the electricity market as a complex adaptive system with an agent-based approach. IEEE Power Energy Mag. 2(4), 39–46 (2004)

    Article  Google Scholar 

  12. Batten, D., George Grozev, G.: NEMSIM: finding ways to reduce greenhouse gas emissions using multi-agent electricity modelling. In: Perez, P., Batten, D. (eds.) Complex Science for a Complex World Exploring Human Ecosystems with Agents, pp. 227–252. Australian National University Press, Canberra (2006)

    Google Scholar 

  13. Sun, J., Tesfatsion, L.: Dynamic testing of wholesale power market designs: an open-source agent-based framework. Comput. Econ. 30, 291–327 (2007)

    Article  MATH  Google Scholar 

  14. Sensfuß, F.: Assessment of the impact of renewable electricity generation on the German electricity sector: an agent-based simulation approach. Ph.D. Dissertation, Karlsruhe University (2007)

    Google Scholar 

  15. Vale, Z., Pinto, T., Praça, I., Morais, H.: MASCEM - electricity markets simulation with strategically acting players. IEEE Intell. Syst. 26(2), 9–17 (2011)

    Article  Google Scholar 

  16. Sensfuß, F., Genoese, M., Ragwitz, M., Möst, D.: Agent-based simulation of electricity markets—a literature review. Energy Stud. Rev. 15(2), 1–29 (2007)

    Google Scholar 

  17. Weidlich, A., Veit, D.: A critical survey of agent-based wholesale electricity market models. Energy Econ. 30, 1728–1759 (2008)

    Article  Google Scholar 

  18. Weidlich, A.: Engineering Interrelated Electricity Markets. Physica-Verlag, Heidelberg (2008)

    Google Scholar 

  19. Guerci, E., Rastegar, M., Cincotti, S.: Agent-based modeling and simulation of competitive wholesale electricity markets. In: Rebennack, S., Pardalos, P., Pereira, M., Iliadis, N. (eds.) Handbook of Power Systems II, pp. 241–286. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  20. Franklin, S., Graesser, A.: Is it an agent, or just a program?: a taxonomy for autonomous agents. In: Müller, J., Wooldridge, M., Jennings, N. (eds.) Intelligent Agents III: Agent Theories, Architectures, and Languages, pp. 21–35. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  21. Wooldridge, J., Jennings, N.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)

    Article  Google Scholar 

  22. Kaelbling, P.: A situated-automata approach to the design of embedded agents. SIGART Bull. 2(4), 85–88 (1991)

    Article  Google Scholar 

  23. Maes, P.: The agent network architecture (ANA). SIGART Bull. 2(4), 115–120 (1991)

    Article  Google Scholar 

  24. Shehory, O., Sturm, A.: A brief introduction to agents. In: Shehory, O., Sturm, A. (eds.) Agent-Oriented Software Engineering, pp. 3–11. Springer, Heidelberg (2014)

    Google Scholar 

  25. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education Inc., New Jersey (2010)

    MATH  Google Scholar 

  26. Russell, S.: Rationality and intelligence. Artif. Intell. 94, 57–77 (1997)

    Article  MATH  Google Scholar 

  27. Russell, S.: Rationality and intelligence. In: Elio, R. (ed.) Common Sense, Reasoning, and Rationality, pp. 37–57. Oxford University Press, New York (2002)

    Chapter  Google Scholar 

  28. Kaelbling, L., Littman, M., Moore, A.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Google Scholar 

  29. Sen, S., Weiss, G.: Learning in multi-agent systems. In: Weiss, G. (ed.) Multi-Agent Systems: A Modern Approach to Distributed Artificial Intelligence, pp. 259–298. MIT Press, USA (1999)

    Google Scholar 

  30. Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  31. Halevy, A., Norvig, P., Pereira, F.: The unreasonable effectiveness of data. IEEE Intell. Syst. 24(2), 8–12 (2009)

    Article  Google Scholar 

  32. Müller, J.: The Design of Intelligent Agents: A Layered Approach. Springer, Heidelberg (1996)

    Book  Google Scholar 

  33. Müller, J.: Architectures and applications of intelligent agents: a survey. Knowl. Eng. Rev. 13(4), 353–380 (1998)

    Article  Google Scholar 

  34. Bratman, M., Israel, D., Pollack, M.: Plans and resource-bounded practical reasoning. Comput. Intell. 4, 349–355 (1988)

    Article  Google Scholar 

  35. Georgeff, M., Ingrand, F.: Decision-making in an embedded reasoning system. In: Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), Detroit, Michigan, pp. 972–978 (1989)

    Google Scholar 

  36. Ferguson, I.: Touring machines: autonomous agents with attitudes. IEEE Comput. 25(5), 51–55 (1992)

    Article  Google Scholar 

  37. Sridharan, N.: 1986 workshop on distributed AI. AI Mag. 8(3), 75–85 (1987)

    Google Scholar 

  38. Huhns, M., Singh, M.: Agents and multiagent systems: themes, approaches, and challenges. In: Huhns, M., Singh, M. (eds.) Readings in Agents, pp. 1–23. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  39. Scerri, P., Pynadath, D., Tambe, M.: Adjustable autonomy for the real world. In: Hexmoor, H., Castelfranci, C., Falcone, R. (eds.) Agent Autonomy, pp. 211–241. Springer Science+Business Media, New York (2003)

    Chapter  Google Scholar 

  40. Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., Lau, K., Oakley, C., Palatucci, M., Pratt, V., Stang, P., Strohband, S., Dupont, C., Jendrossek, L.-E., Koelen, C., Markey, C., Rummel, C., van Niekerk, J., Jensen, E., Alessandrini, P., Bradski, G., Davies, B., Ettinger, S., Kaehler, A., Nefian, A., Mahoney, P.: Stanley: the robot that won the DARPA grand challenge. In: Buehler, M., Iagnemma, K., Singh, S. (eds.) The 2005 DARPA Grand Challenge, pp. 1–43. Springer, Berlin (2007)

    Google Scholar 

  41. Gruber, T.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)

    Article  Google Scholar 

  42. Noy, N., McGuinness, D.: Ontology development 101: a guide to creating your first ontology. Technical report KSL-01-05, Knowledge Systems Laboratory, Stanford University, USA (2001)

    Google Scholar 

  43. Bechhofer, S., van Harmelen, F., Horrocks, I., McGuinness, D., Patel-Schneider, P., Stein, L.: OWL Web Ontology Language Reference (2004). http://www.w3.org/TR/2004/REC-owl-ref-20040210/. Accessed 12 Jan 2017

  44. W3C Recommendation: The Extensible Markup Language (XML) 1.0 (2008). http://www.w3.org/TR/REC-xml/. Accessed 12 Jan 2017

  45. Finin, T., Fritzson, R., McKay, D., McEntire, R.: KQML – a language and protocol for knowledge and information exchange. In: Proceedings of the 13th International Distributed Artificial Intelligence Workshop, pp. 93–103. AAAI Press, Menlo Park, California (1994)

    Google Scholar 

  46. Austin, J.: How to Do Things With Words. Oxford University Press, London (1962)

    Google Scholar 

  47. Searle, J.: Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press, London (1969)

    Book  Google Scholar 

  48. Jeon, H., Petrie, C., Cutkosky, M.: JATLite: a java agent infrastructure with message routing. IEEE Internet Comput. 4(2), 87–96 (2000)

    Article  Google Scholar 

  49. FIPA: ACL Message Structure Specification. Foundation for Intelligent Physical Agents, Document Number SC00061G (2002). http://www.fipa.org/specs/fipa00061/. Accessed 12 Jan 2017

  50. FIPA: Communicative Act Library Specification. Foundation for Intelligent Physical Agents, Document Number SC00037J (2002). http://www.fipa.org/specs/fipa00037/. Accessed 12 Jan 2017

  51. Cohen, P., Levesque, H.: Rational interaction as the basis for communication. In: Cohen, P., Morgan, J., Pollack, M. (eds.) Intentions in Communication, pp. 221–256. The MIT Press, Cambridge (1990)

    Google Scholar 

  52. Bretier, P., Sadek, D.: A rational agent as the kernel of a cooperative spoken dialogue system: implementing a logical theory of interaction. In: Müller, J., Wooldridge, M., Jennings, N. (eds.) Intelligent Agents III (LNAI 1193), pp. 189–203. Springer, Heidelberg (1997)

    Google Scholar 

  53. Bellifemine, F., Caire, G., Greenwood, D.: Developing Multi-agent Systems with JADE. Wiley, Chichester (2007)

    Book  Google Scholar 

  54. Bond, A., Gasser, L.: An analysis of problems and research in DAI. In: Bond, A., Gasser, L. (eds.) Readings in Distributed Artificial Intelligence, pp. 3–35. Morgan Kaufmann Publishers, San Mateo (1988)

    Google Scholar 

  55. Bobrow, D.: Dimensions of interaction. AI Mag. 12(3), 64–80 (1991)

    Google Scholar 

  56. Lopes, F., Wooldridge, M., Novais, A.Q.: Negotiation among autonomous computational agents: principles, analysis and challenges. Artif. Intell. Rev. 29, 1–44 (2008)

    Article  Google Scholar 

  57. Weiss, G. (ed.): Multiagent Systems. The MIT Press, Cambridge (2013)

    Google Scholar 

  58. Lopes, F., Coelho, H. (eds.): Negotiation and Argumentation in Multi-agent Systems. Bentham Science, The Netherlands (2014)

    Google Scholar 

  59. Poole, D., Mackworth, A.: Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press, Cambridge (2010)

    Book  MATH  Google Scholar 

  60. Goodwin, R.: Formalizing properties of agents. Technical report CMUCS93159, School of Computer Science, Carnegie-Mellon University, Pittsburgh (1993)

    Google Scholar 

  61. Braubach, L., Pokahr, A., Lamersdorf, W.: Jadex: a BDI-agent system combining middleware and reasoning. In: Unland, R., Klusch, M., Calisti, M. (eds.) Software Agent-Based Applications, Platforms and Development Kits, pp. 143–168. Birkhäuser Verlag, part of Springer Science+Business Media, Basel, Switzerland (2005)

    Google Scholar 

  62. Vidigal, D., Lopes, F., Pronto, A., Santana, J.: Agent-based simulation of wholesale energy markets: a case study on renewable generation. In: Spies, M., Wagner, R., Tjoa, A. (eds.) 26th Database and Expert Systems Applications (DEXA 2015), pp. 81–85. IEEE (2015)

    Google Scholar 

  63. Algarvio, H., Couto, A., Lopes, F., Estanqueiro, A., Santana, J.: Multi-agent energy markets with high levels of renewable generation: a case-study on forecast uncertainty and market closing time. In: Omatu, S. et al. (eds.) 13th International Conference on Distributed Computing and Artificial Intelligence, pp. 339–347. Springer International Publishing (2016)

    Google Scholar 

  64. Algarvio, H., Couto, A., Lopes, F., Estanqueiro, A., Holttinen, H., Santana, J.: Agent-based simulation of day-ahead energy markets: impact of forecast uncertainty and market closing time on energy prices. In: Tjoa, A., Vale, Z., Wagner, R. (eds.) 27th Database and Expert Systems Applications (DEXA 2016), pp. 166–70. IEEE (2016)

    Google Scholar 

  65. Lopes, F., Rodrigues, T., Sousa, J.: Negotiating bilateral contracts in a multi-agent electricity market: a case study. In: Hameurlain, A., Tjoa, A., Wagner, R. (eds.) 23rd Database and Expert Systems Applications (DEXA 2012), pp. 326–330. IEEE (2012)

    Google Scholar 

  66. Algarvio, H., Lopes, F., Santana, J.: Bilateral contracting in multi-agent energy markets: forward contracts and risk management. In: Bajo, J. et al. (eds.) Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability: The PAAMS Collection (PAAMS 2015), pp. 260–269. Springer International Publishing (2015)

    Google Scholar 

  67. Sousa, F., Lopes, F., Santana, J.: Contracts for difference and risk management in multi-agent energy markets. In: Demazeau, Y., Decker, K., Pérez, J., De la Prieta, F. (eds.) Advances in Practical Applications of Agents, Multi-Agent Systems, and Sustainability: The PAAMS Collection (PAAMS 2015), pp. 339–347. Springer International Publishing (2015)

    Google Scholar 

  68. Sousa, F., Lopes, F., Santana, J.: Multi-agent electricity markets: a case study on contracts for difference. In: Spies, M., Wagner, R., Tjoa, A. (eds.) 26th Database and Expert Systems Applications (DEXA 2015), pp. 88–90. IEEE (2015)

    Google Scholar 

  69. Lopes, F., Coelho, H.: Strategic and tactical behaviour in automated negotiation. Int. J. Artif. Intell. 4(S10), 35–63 (2010)

    Google Scholar 

  70. Osborne, M., Rubinstein, A.: Bargaining and Markets. Academic Press, London (1990)

    MATH  Google Scholar 

  71. Algarvio, H., Lopes, F., Santana, J.: Multi-agent retail energy markets: bilateral contracting and coalitions of end-use customers. In: 12th International Conference on the European Energy Market (EEM 2015), pp. 1–5. IEEE (2015)

    Google Scholar 

  72. Algarvio, H., Lopes, F., Santana, J.: Multi-agent retail energy markets: contract negotiation, customer coalitions and a real-world case study. In: Demazeau, I., Takayuki, I., Javier, B., Escalona, M. (eds.) Advances in Practical Applications of Scalable Multi-agent Systems (The PAAMS Collection), LNAI 9662, pp. 13–23. Springer International Publishing (2016)

    Google Scholar 

  73. Bratman, M.: Intentions, Plans, and Practical Reason. Harvard University Press, Cambridge (1987)

    Google Scholar 

  74. Haddadi, A., Sundermeyer, K.: Belief-desire-intention agent architectures. In: O’Hare, G., Jennings, N. (eds.) Foundations of Distributed Artificial Intelligence, pp. 169–185. Wiley, New York (1996)

    Google Scholar 

  75. D’Inverno, M., Luck, M., Georgeff, M., Kinny, D., Wooldridge, M.: The dMARS architecture: a specification of the distributed multi-agent reasoning system. Auton. Agents Multi Agent Syst. 9, 5–53 (2004)

    Article  Google Scholar 

  76. Burmeister, B., Arnold, M., Copaciu, F., Rimassa, G.: BDI-agents for agile goal-oriented business processes. In: Berger, M., Burg, B., Nishiyama, S. (eds.) International Conference on Autonomus Agents and Multi-agent Systems (Industry and Applications Track) IFAAMAS, pp. 37–44 (2008)

    Google Scholar 

  77. Shoham, Y.: Agent-oriented programming. Artif. Intell. 60, 51–92 (1993)

    Article  MathSciNet  Google Scholar 

  78. Broersen, J., Dastani, M., Hulstijn, J., Huang, Z., van der Torre, L.: The BOID Architecture. In: André, E., Sen, S., Frasson, C., Müller, J. (eds.) International Conference on Autonomous Agents (AGENTS-01), pp. 9–16. ACM Press, New York (2001)

    Google Scholar 

  79. Broersen, J., Dastani, M., Hulstijn, J., van der Torre, L.: Goal generation in the BOID architecture. Cogn. Sci. Q. 2(3–4), 428–447 (2002)

    Google Scholar 

  80. Schut, M., Wooldridge, M., Parsons, S.: On partially observable MDPs and BDI models. In: dInverno, M., Luck, M., Fisher, M., Preist, C. (eds.) Foundations and Applications of Multi-Agent Systems (UKMAS Workshops 1996-2000, Selected Papers), LNAI 2403, pp. 243–259. Springer, Berlin (2002)

    Google Scholar 

  81. Simari, G., Parsons, S.: Markov Decision Processes and the Belief-Desire-Intention Model: Bridging the Gap for Autonomous Agents. Springer, Heidelberg (2011)

    Book  MATH  Google Scholar 

  82. Nair, R., Tambe, M.: Hybrid BDI-POMDP framework for multiagent teaming. J. Artif. Intell. Res. 23, 367–420 (2005)

    MATH  Google Scholar 

  83. Dimuro, G., Costa, A., Gonçalves, L., Pereira, D.: Recognizing and learning models of social exchange strategies for the regulation of social interactions in open agent societies. J. Braz. Comput. Soc. 17, 143–161 (2011)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The work described in this chapter was performed under the project MAN-REM: Multi-agent Negotiation and Risk Management in Electricity Markets (FCOMP-01-0124-FEDER-020397), supported by FEDER Funds, through the program COMPETE (“Programa Operacional Temático Factores de Competividade”), and also National Funds, through FCT (“Fundação para a Ciência e a Tecnologia”). The authors also wish to acknowledge the valuable comments and suggestions made by Hannele Holttinen, from the VTT Technical Research Centre of Finland.

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  1. LNEG–National Laboratory of Energy and Geology, Estrada do Paço do Lumiar 22, 1649-038, Lisbon, Portugal

    Fernando Lopes

  2. University of Lisbon, Bloco C6, Campo Grande, 1749-016, Lisbon, Portugal

    Helder Coelho

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    Fernando Lopes

  2. University of Lisbon, Lisbon, Portugal

    Helder Coelho

Appendix: Notes on Agents and Agent-based Modeling

Appendix: Notes on Agents and Agent-based Modeling

Software Agents. Agents are computer systems that perceive the environment with sensors and are able to react over it through actuators. They have several important capabilities, notably autonomy (they decide for themselves which actions to perform in order to satisfy their design objectives) and social ability (they interact with other agents, either to achieve their objectives or to manage the dependencies that ensue from being situated in a common environment). The interactions can vary from simple communication of information to cooperation, collaboration, coordination and negotiation.

An important question is whether the (abstract) architecture or (conceptual) model that underpins software agents should be relatively simple or more sophisticated in nature. A simple and abstract view of an architecture considers sensors and inputs, some sort of internal state, actions and outputs. A more complex and concrete view, based on cognition, considers deliberation (generation of goals or plans, reconsideration of goals, etc.), decision making (choice of options, commitment, etc.) and execution of actions (rules of action, movement, etc.).

Fig. 3.2
figure 2

Generic belief-desire-intention (BDI) architecture

Full size image

An even more sophisticated view, based on the folk-psychology concepts by which human behavior is normally predicted and explained, is shown in Fig. 3.2. Put simply, software agents reason and act according to three key mental attitudes: belief, desire, and intention (and, mainly for this reason, they are called BDI agents). They perceive the world, acquire and update information (beliefs), reason about the objectives to achieve (desires), and deliberate (choose based on preferences) to find the objectives to commit to (intentions).

The belief-desire-intention (BDI) model of practical reasoning is arguably the dominant force in the theoretical foundations of rational agency (see, e.g., [73,74,75,76]). However, the question of exactly which combination of mental attitudes is most appropriate to characterize software agents has been the subject of some debate. As a result, several different models that predict and explain agent behavior according to combinations of mental attitudes different from beliefs, desires and intentions, yet often interrelated, have been proposed in the technical literature. Put simply, there are alternatives to the popular use of beliefs, desires and intentions. For example, Shoham [77] suggests that the notion of choice is fundamental. Broersen et al. [78, 79] propose the beliefs-obligations-intentions-desires (BOID) architecture. Schut et al. [80] discuss the integration of the BDI model with partially observable Markov decision processes (POMDP). Simari and Parsons [81] analyze, in detail, several key relationships between the BDI model and (fully observable) Markov Decision Processes. Nair and Tambe [82] present the BDI-POMDP framework for multi-agent teaming. Dimuro et al. [83] extend the BDI-POMDP framework with a module based on the hidden Markov model (HMM). Despite these and other relevant efforts, however, comparatively little work has yet been done on comparing the suitability of different combinations (of mental attitudes) to characterize agents.

Agent-based Modeling versus Equation-based Modeling. Two different types of approaches are face-to-face in competition: system level (equation-based), the traditional type, and individual level (agent-based), the “modern” type. They differ in what is the model and the execution. Equation-based modeling (EBM) operates with variables, and evaluates or integrates sets of equations relating such variables. The model is a set of equations and the execution is supported by evaluating them. Agent-based modeling (ABM) is based on a multitude of agents that encapsulate the behaviors of the diverse individuals that compose a system. The execution consists of emulating such behaviors.

EBM and ABM have common objectives, but differ in both the essential relationships among the entities they model and the level at which they focus attention. Both approaches identify two entities, with a temporal feature: the individuals and the observables. Individuals are characterized by observables and affect their values by specific actions. Observables are related to one another by equations. Individuals interact with one another through their behaviors.

It is worth to highlight a key feature of the models underlying the two approaches. EBM has the equation as the basic unit whereas ABM represents the internal behavior of each individual. This diversity in model structure gives to ABM a significant advantage in most commercial and industrial applications, because the natural unit of system decomposition is the individual rather than the equation, and the physical distribution of computation across multiple processors is naturally desirable.

Agent-based systems are often easier to construct and facilitates the distinction between the physical and the interaction space. Also, they offer an additional level of validation, support direct experimentation, and are easier to translate back into practice. Typically, ABM gives more realistic results than EBM.

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Lopes, F., Coelho, H. (2018). Electricity Markets and Intelligent Agents Part II: Agent Architectures and Capabilities. In: Lopes, F., Coelho, H. (eds) Electricity Markets with Increasing Levels of Renewable Generation: Structure, Operation, Agent-based Simulation, and Emerging Designs. Studies in Systems, Decision and Control, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-319-74263-2_3

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