Integrating BDI reasoning into agent based modeling and simulation
11 Dec 2011-pp 345-356
TL;DR: This work takes a Belief Desire Intention (BDI) agent platform and embeds it into Repast, to support more powerful modeling of human behavior.
Abstract: Agent Based modeling (ABM) platforms such as Repast and its predecessors are popular for developing simulations to understand complex phenomenon and interactions. Such simulations are increasingly used as support tools for policy and planning. This work takes a Belief Desire Intention (BDI) agent platform and embeds it into Repast, to support more powerful modeling of human behavior. We describe the issues faced in integrating the two paradigms, and how we addressed these issues to leverage the relevant advantages of the two approaches for real world applications.
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TL;DR: A methodological guide to the use of BDI agents in social simulations, an expressive paradigm using concepts from folk psychology, making it easier for modellers and users to understand the simulation.
Abstract: Modeling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the resulting complexity of models, Axelrod promoted the KISS principle: " Keep It Simple, Stupid ". But the community is divided and a new principle appeared: KIDS, " Keep It Descriptive Simple ". Richer models were thus developed for a variety of phenomena, while agent cognition still tends to be modelled with simple reactive particle-like agents. This is not always appropriate, in particular in the social sciences trying to account for the complexity of human behaviour. One solution is to model humans as BDI agents, an expressive paradigm using concepts from folk psychology, making it easier for modellers and users to understand the simulation. This paper provides a methodological guide to the use of BDI agents in social simulations, and an overview of existing methodologies and tools for using them.
122 citations
TL;DR: In this article, the authors present a framework that allows belief-desire-intention (BDI) cognitive agents to be embedded in an ABM system, which can be used for exploring and supporting decision-making about social science scenarios involving modelling of human agents.
Abstract: Agent-based models (ABMs) are increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire-Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the "brains" of an agent can be modelled in the BDI system in the usual way, while the "body" exists in the ABM system. The architecture is flexible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration of off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community.
51 citations
18 Aug 2014
TL;DR: This work has coupled MATSim with a BDI (Belief Desire Intention) system to allow both more extensive modelling of the agent's decision making, as well as reactivity to environmental situations to facilitate the use of MATSim in a wide range of simulation applications.
Abstract: MATSim is a mature and powerful traffic simulator, used for large scale traffic simulations, primarily to assess likely results of various infrastructure or road network changes. More recently there has been work to extend MATSim to allow its use in applications requiring what has been referred to as "within day replanning". In the work described here we have coupled MATSim with a BDI (Belief Desire Intention) system to allow both more extensive modelling of the agent's decision making, as well as reactivity to environmental situations. The approach used allows for all agents to be "intelligent" or for some to be "intelligent"/reactive, while others operate according to plans that are static within a single day. The former is appropriate for simulations such as a bushfire evacuation, where all agents will be reacting to the changing environment. The latter is suited to introducing agents such as taxis into a standard MATSim simulation, as they cannot realistically have a predetermined plan, but must constantly respond to the current situation. We have prototype applications for both bushfire evacuation and taxis. By extending the capabilities of MATSim to allow agents to respond intelligently to changes in the environment, we facilitate the use of MATSim in a wide range of simulation applications. The work also opens the way for MATSim to be used alongside other simulation components, in a simulation integrating multiple components.
30 citations
18 Aug 2014
TL;DR: This paper presents an open source framework, called OpenSim, that allows such integrated simulations to be built in a modular way, by linking together agent-based and other models.
Abstract: The growing use of agent-based modelling and simulation for complex systems analysis has led to the availability of numerous published models. However, reuse of existing models in new simulations, for studying new problems, is largely not attempted. This is mainly because there is no systematic way of integrating agent-based models, that deals with the nuances of complex interactions and overlaps in concepts between components, in the shared environment. In this paper we present an open source framework, called OpenSim, that allows such integrated simulations to be built in a modular way, by linking together agent-based and other models. OpenSim is designed to be easy to use, and we give examples of the kinds of simulations we have built with this framework.
19 citations
TL;DR: An adapted DES approach is proposed for modelling such systems and the approach is demonstrated through a model of a coffee shop, where a key innovation is that queues are not explicitly modelled.
Abstract: Discrete-event simulation (DES), which has largely grown out of modelling manufacturing systems, has increasingly been applied in the service sector. The approach, however, is not always appropriate for modelling service operations. In particular, it cannot help with detailed decisions about the layout of service operations in which the customers are present such as retail outlets and airports. An adapted DES approach is proposed for modelling such systems and the approach is demonstrated through a model of a coffee shop. A key innovation is that queues are not explicitly modelled. The benefit of the approach is that it simplifies the modelling of service systems in which the customers are present by reducing the number of components that need to be modelled. It can also aid decisions about the layout of a system. We ask whether the approach is in fact an agent-based simulation and identify ways in which the approach could be extended.
15 citations
References
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Proceedings Article•
01 Oct 1997
TL;DR: A formalization of intentions based on a branching-time possible-worlds model is presented and it is shown how the formalism realizes many of the important elements of Bratman's theory of intention.
2,319 citations
TL;DR: A collection of seven essays that serve as an introductory text on complexity theory and computer modelling in the social sciences, and as an overview of the current state of the art in this field can be found in this paper.
Abstract: A collection of seven essays that serves as an introductory text on complexity theory and computer modelling in the social sciences, and as an overview of the current state of the art in this field. The articles move beyond the basic paradigm of the "Prisoner's Dilemma" to study a rich set of issues, including how to cope with errors in perception or implementation, how norms emerge, and how political actors and regions of shared culture can develop. They use the shared methodology of agent-based modelling, a technique that specifies the rules of interaction between individuals and uses computer simulation to discover emergent properties of the social system.
2,231 citations
31 Jan 1997
TL;DR: The latest volume in the series of Princeton Studies in Complexity is Robert Axelrod's sequel to his influential book, The Evolution of Cooperation as discussed by the authors, which consists of an introduction and seven chapters extending and developing the earlier research in various directions as well as appendices containing useful technical information for researchers in the fields of complexity and agent-based modeling.
Abstract: T he latest volume in the series of Princeton Studies in Complexity is Robert Axelrod's sequel to his influential book, The Evolution of Cooperation [1]. The earlier book was an extended commentary on his pioneering computer simulations of cooperation and competition in the iterated Prisoner's Dilemma game, originally reported in 1980 and discussed in a prize-winning journal article [2]. The new book consists of an introduction and seven chapters extending and developing the earlier research in various directions as well as appendices containing useful technical information for researchers in the fields of complexity and agent-based modeling. The chapters are all reprints of previously published material, but they originally appeared in such widely scattered journals and edited volumes that few readers will have seen all of them before. For this volume, Axelrod has included brief introductory comments to each of them, describing the circumstances in which they were written and reactions to them. Some chapters are only indirectly related to Axelrod's well-known agent-based models of cooperation. In particular , there are two chapters devoted to his more recent landscape theory— according to which, in a group of decision makers who are myopic in their assessments of their own payoffs, the coalitions that are likely to form are those that minimize strain between the multiple elements of the interacting system. A chapter by Axelrod and D. Scott Bennett shows that landscape theory successfully predicts the alignment of 17 European nations in World War II. A separate chapter by Axelrod, Bennett, and three others shows how the theory predicts the alignment that occurred among nine major computer companies promoting two different UNIX operating system standards in 1988. A major problem with these contributions is that the coalitions predicted by landscape theory turn out to be nothing more than the Nash equilibria of conventional game theory—outcomes in which none of the actors can gain an individual advantage by defecting unilaterally to a different coalition. Nash equilibria provide necessary but insufficient criteria for deter-minate solutions to games, and game theorists have developed theories of coalition formation that are far more sophisticated and subtle and that go far beyond mere Nash equilibria [3]. The most interesting chapters are the ones in which Axelrod is on home territory, developing his earlier agent-based simulation models of the evolution of cooperation. Starting in 1978-79, he had organized two r o u n d-r o b i n computer tournaments …
1,085 citations
TL;DR: Generative Social Science: Studies in Agent-Based Computational Modeling as mentioned in this paper is a collection of works with all but three chapters (Introduction, Chapters 2 and 13) published separately elsewhere in books or journals.
Abstract: Generative Social Science: Studies in Agent-Based Computational Modeling JOSHUA M. EPSTEIN PRINCETON UNIVERSITY PRESS, PRINCETON, NJ, 2007 352 PP. CLOTH $49.50 REVIEWED BY ERIC C. JONES This book calls for a generative social science. Generative social science rests on the idea that you cannot explain current phenomena without describing the rules or preceding conditions that produced these current phenomena. In other words, the author believes that we must not only explore causality in terms of 'A affects B,' but also in terms of how a specific suite of physical, biological, social or cultural tendencies play out across time for a given population, producing some observed state or phenomenon. Epstein argues that anything short of being able to model the flow between prior and present conditions is mere description. He says his naming of the Generative approach took inspiration from Chomsky's generative syntactic structures. Generative social science is tightly wed to the methodology of Agent-Based Modeling made more feasible lately by faster computers. However, Epstein warns against its identification solely as a computer-driven technique. His point is that past behavior of individuals, households, firms or other agents must be accounted for when understanding a phenomenon. Following the lead of mathematicians and most modelers, the author seeks parsimonious or small sets of rules to explain the arrival at any current condition. This 'new' kind of social science is probably too mathematical for most ethnographically oriented social scientists to adopt, although this historicist/ evolutionary approach is one that must regularly be injected into the social sciences in order to augment the complimentary yet more dominant functionalist and ideationist approaches. Ecosystem researchers would certainly be able to make use of the agentbased modeling approach, perhaps even being able to better account for the individual agents in their systems. Population researchers similarly could better develop models and parameters for animal/plant/ agent behaviors. Generative Social Science is generally an update to the 1996 book Growing Artificial Societies (Brookings Institution and MIT Press) by Epstein and Robert Axtell, although this new book is a compilation of works with all but three chapters (Introduction, Chapters 2 and 13) published separately elsewhere in books or journals. Preludes by Epstein for each chapter make the flow awkward, but provide contextual insights or connections between chapters. All chapters have Epstein as an author-typically the primary author-and half of the chapters are single-authored by Epstein; as such, the publisher considers the book a single-authored work. A CD with several of the models accompanies the book, so that you can change a few of the parameters and graphically view the results (hundreds of colored pixels on a square space). The agent-based modeling technique is one way to bridge the micro-macro gulf, producing non-intuitive macro results along the way. Epstein is careful to define such emergence as the computable result of agent actions, and not as the old (and even contemporary, in some cases) idea of emergence as something that can never be reduced to its parts. Despite proposing this form of reductionism, the book allows that emergent properties maybe something that the individuals themselves might not possess, so emergence is not so much a sum of parts as a product of parts. Different agent-based models with different suites of variables might produce the same social phenomena, in which case field data and theoretical plausibility assist in determining which model to pursue. Models can also be used to find out which rules will not account for observed behavior. The first three chapters constitute the introductory material, primarily advocacy for the approach as well as delimiting the domain. The domain of generative social science is based upon the following: heterogeneous agents, bounded rationality, explicit/ geographic space, local interactions, non-equilibrium dynamics and initial autonomy of agents. …
962 citations
01 Jan 2005
TL;DR: The Jadex reasoning engine is presented, which supports cognitive agents by exploiting the BDI model and is realized as adaptable extension for agent middleware such as the widely used JADE platform.
Abstract: Nowadays a multitude of different agent platforms exist that aim to support the software engineer in developing multi-agent systems. Nevertheless, most of these platforms concentrate on specific objectives and therefore cannot address all important aspects of agent technology equally well. A broad distinction in this field can be made between middleware- and reasoning-oriented systems. The first category is mostly concerned with FIPA-related issues like interoperability, security and maintainability whereas the latter one emphasizes rationality and goal-directedness. In this paper the Jadex reasoning engine is presented, which supports cognitive agents by exploiting the BDI model and is realized as adaptable extension for agent middleware such as the widely used JADE platform.
165 citations