Agent-based simulation of electricity markets: a survey of tools

Agent-based simulation has been a popular technique in modeling and analyzing electricity markets in recent years. The main objective of this paper is to study existing agent-based simulation packages for electricity markets. We first provide an overview of electricity markets and briefly introduce the agent-based simulation technique. We then investigate several general-purpose agent-based simulation tools. Next, we review four popular agent-based simulation packages developed for electricity markets and several agent-based simulation models reported in the literature. We compare all the reviewed packages and models and identify their common features and design issues. Based on the study, we describe an agent-based simulation framework for electricity markets to facilitate the development of future models for electricity markets.     

Care HPS: A high performance simulation tool for parallel and distributed agent-based modeling

Parallel and distributed simulation is a powerful tool for developing complex agent-based simulation. Complex simulations require parallel and distributed high performance computing solutions. It is necessary because their sequential solutions are not able to give answers in a feasible total execution time. Therefore, for the advance of computing science, it is important that High Performance Computing (HPC) techniques and solutions be proposed and studied. In literature, we can find some agent-based modeling and simulation tools that use HPC. However, none of these tools are designed to enable the HPC expert to be able to propose new techniques and solutions without great effort. In this paper, we introduce Care High Performance Simulation (HPS), which is a scientific instrument that enables researchers to: (1) develop techniques and solutions of high performance distributed simulations for agent-based models; and, (2) study, design and implement complex agent-based models that require HPC solutions. Care HPS was designed to easily and quickly develop new agent-based models. It was also designed to extend and implement new solutions for the main issues of parallel and distributed solutions such as: synchronization, communication, load and computing balancing, and partitioning algorithms. We conducted some experiments with the aim of showing the completeness and functionality of Care HPS. As a result, we show that Care HPS can be used as a scientific instrument for the advance of the agent-based parallel and distributed simulations field.     

Agent-based modelling and simulation for the analysis of social patterns

Agent-based modelling facilitates the implementation of tools for the analysis of social patterns. This comes from the fact that agent related concepts allow the representation of organizational and behavioural aspects of individuals in a society and their interactions. An agent can characterize an individual with capabilities to perceive and react to events in the environment, taking into account its mental state (beliefs, goals), and to interact with other agents in its social environment. There are already tools to perform agent-based social simulation but these are usually hard to use by social scientists, as they require a good expertise in computer programming. In order to cope with such difficulty, we propose the use of agent-based graphical modelling languages, which can help to specify social systems as multi-agent systems in a more convenient way. This is complemented with transformation tools to be able to analyse and derive emergent social behavioural patterns by using the capabilities of existing simulation platforms. In this way, this framework can facilitate the specification and analysis of complex behavioural patterns that may emerge in social systems.     

A GPU-assisted hybrid model for real-time crowd simulations

In this paper, we propose two new techniques for real-time crowd simulations; the first one is the clustering of agents on the GPU and the second one is incorporating the global cluster information into the existing microscopic navigation technique. The proposed model combines the agent-based models with macroscopic information (agent clusters) into a single framework. The global cluster information is determined on the GPU, and based on the agents' positions and velocities. Then, this information is used as input for the existing agent-based models (velocity obstacles, rule-based steering and social forces). The proposed hybrid model not only considers the nearby agents but also the distant agent configurations. Our test scenarios indicate that, in very dense circumstances, agents that use the proposed hybrid model navigate the environment with actual speeds closer to their intended speeds (less stuck) than the agents that are using only the agent-based models.     

Agent-based ontology mapping and integration towards interoperability

Abstract: Interoperability is an important issue in ontology research. In this paper, a novel agent-based framework for managing ontologies in a dynamic environment is developed. The framework has several key characteristics such as flexibility and extensibility that differentiate this research from others. Based on the proposed framework, ontology mapping and integration are investigated. It is believed that inter-ontology processes like ontology mapping with logical semantics are foundations of ontology-based applications. Accordingly, several types of semantic relations are proposed and corresponding mapping mechanisms are developed. Based on mapping results, ontology integration is developed to provide abstract views for participating organizations in the presence of a variety of ontologies. A prototype is built to demonstrate the design and functionalities and is applied to beer ontologies. The prototype shows that the framework is not only flexible but also practical. All agents derived from the framework exhibit their behaviours as expected.     

Formalizing the construction of populations in multi-agent simulations

In individual-centered simulations, the variety and consistency of agents' behaviors reinforce the realism and validity of the simulation. Variety increases the diversity of behaviors that users meet during the simulation. Consistency ensures that these behaviors improve the users' feeling of immersion. In this work, we address the issue of the simultaneous influence of these two elements. We propose a formalization of the construction of populations for agent-based simulations, which provides the basis for a generic and non-intrusive tool allowing an out-of-the-agent design. First, the model uses behavioral patterns to describe standards of behaviors for the agents. They provide a behavioral archetype during agents' creation, and are also a compliance reference, that allows to detect deviant behaviors and address them. Then, a specific process instantiates the agents by using the specification provided by the patterns. Finally, inference enables to automate behavioral patterns configuration from real or simulated data. This formalization allows for the easy introduction of variety in agents' behaviors, while controlling the conformity to specifications. We applied the model to traffic simulation, in order to introduce driving styles specified using behavioral patterns (e.g. cautious or aggressive drivers). The behavioral realism of the traffic was therefore improved, and the experimentations we conducted show how the model contributes to increase the variety and the representativeness of the behaviors.     

网格环境下的航空发动机集成设计与分布仿真研究

建立了一个开发航空发动机分布仿真的系统环境．该环境中子模型具有自治性,子模型的设计者可以根据自己的需要选择所使用的工具,定义变量以及他们与其他设计者之间的关系．应用新兴的网格技术建立了一个集成设计和分布仿真的环境框架,它可以灵活地建立和修改基于部件对象模型建立起来的航空发动机模型,并尝试解决仿真中存在的多学科耦合以及大计算量的问题．该框架具有图形化界面,可以方便地更改发动机的参数和结构,其所具有网格技术的易扩展性也为今后建立更复杂的发动机模型提供了良好的平台．研究人员可以应用该系统提供的可扩展设计和仿真环境,灵活地组建新的航空发动机模型并进行仿真．     

Computing with the social fabric: The evolution of social intelligence within a cultural framework

Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems using an influence function inspired by the marginal value theorem from population biology. We briefly describe an implementation of this approach, the cultural algorithms toolkit (CAT) in the Repast agent-based simulation environment. Next we introduce the notion of "social fabric" which provides a framework in which the knowledge sources can access the social networks to which individuals can belong. A computational version of the social fabric idea is then implemented as an extension of the influence function in the CAT system. We then apply the enhanced system to a problem in engineering design, the "pressure vessel problem". For this problem, we show that the enhanced system with the social fabric outperforms the CAT system without it. We demonstrate also that the frequency with which the knowledge sources are able to access the network can affect the problem solving process.     

Agent-based framework for integrated facility engineering

An agent-based framework for the development of integrated facility engineering environments in support of collaborative design is introduced. This framework aims at integrating design software by allowing better software interoperability. Within their framework, design agents represent various existing design and planning systems that communicate their design information and knowledge partially and incrementally using the Agent Communication Language (ACL). ACL is a formal language proposed as a communication standard for disparate software. It is based on a logic-based language called Knowledge Interchange Format (KIF) and a message protocol called Knowledge Query Manipulation Language (KQML). Design agents are linked and their communication of design information is coordinated via system programs called facilitators in a federation architecture. The federation architecture specifies the way design agents and facilitators communicate in an integrated software environment. In concert with pursuing fundamental research concepts, we have been developing an integrated design software environment that spans different phases of the facility life cycle. This environment serves to demonstrate the primary aspects of this research methodology. In this paper, we first discuss the integration problem and review related research projects. We then present the major aspects of agent-based software engineering methodology and its application to integrated facility engineering. A highlight of the current integrated design environment development is given to illustrate the advantages of this approach. Finally, we summarize and discuss some of the important research issues in light of previous research.     

I-ABM: combining institutional frameworks and agent-based modelling for the design of enforcement policies

Computer science advocates institutional frameworks as an effective tool for modelling policies and reasoning about their interplay. In practice, the rules or policies, of which the institutional framework consists, are often specified using a formal language, which allows for the full verification and validation of the framework (e.g. the consistency of policies) and the interplay between the policies and actors (e.g. violations). However, when modelling large-scale realistic systems, with numerous decision-making entities, scalability and complexity issues arise making it possible only to verify certain portions of the problem without reducing the scale. In the social sciences, agent-based modelling is a popular tool for analysing how entities interact within a system and react to the system properties. Agent-based modelling allows the specification of complex decision-making entities and experimentation with large numbers of different parameter sets for these entities in order to explore their effects on overall system performance. In this paper we describe how to achieve the best of both worlds, namely verification of a formal specification combined with the testing of large-scale systems with numerous different actor configurations. Hence, we offer an approach that allows for reasoning about policies, policy making and their consequences on a more comprehensive level than has been possible to date. We present the institutional agent-based model methodology to combine institutional frameworks with agent-based simulations). We furthermore present J-InstAL, a prototypical implementation of this methodology using the InstAL institutional framework whose specifications can be translated into a computational model under the answer set semantics, and an agent-based simulation based on the jason tool. Using a simplified contract enforcement example, we demonstrate the functionalities of this prototype and show how it can help to assess an appropriate fine level in case of contract violations.     

Two distribution methods for multiagent traffic simulations

Modeling and simulation play an important role in transportation networks analysis. With the widespread use of personalized real-time information sources, the behavior of the simulation depends heavily on individual travelers reactions to the received information. As a consequence, it is relevant for the simulation model to be individual-centered, and multiagent simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we define two multiagent simulation models representing the existing sequential multiagent traffic simulations. The first model is fundamental diagram-based model, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is car-following based, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. Then we define patterns to distribute these simulations in a high-performance environment. The first is agent-based and distributes agents equally between available computation units. The second pattern is environment-based and partitions the environment over the different units. The results show that agent-based distribution is more efficient with fundamental diagram-based model simulations while environment-based distribution is more efficient with car following-based simulations.     

Model-driven agent-based simulation development: A modeling language and empirical evaluation in the adaptive traffic signal control domain

Model-driven development (MDD) is an approach for supporting the development of software systems, in which high-level modeling artifacts drive the production of time and effort-consuming low-level artifacts, such as the source code. Previous studies of the MDD effectiveness showed that it significantly increases development productivity, because the development effort is focused on the business domain rather than technical issues. However, MDD was exploited in the context of agent-based development in a limited way, and most of the existing proposals demonstrated the effectiveness of using MDD in this context by argumentation or examples, lacking disciplined empirical analyses. In this paper, we explore the use of MDD for agent-based modeling and simulation in the adaptive traffic signal control (ATSC) domain, in which autonomous agents are in charge of managing traffic light indicators to optimize traffic flow. We propose an MDD approach, composed of a modeling language and model-to-code transformations for producing runnable simulations automatically. In order to analyze the productivity gains of our MDD approach, we compared the amount of design and implementation artifacts produced using our approach and traditional simulation platforms. Results indicate that our approach reduces the workload to develop agent-based simulations in the ATSC domain.     

DEVS methodology for evaluating time-constrained message routing policies

Due to its ability to support temporal issues of systems, discrete event simulation is widely applicable to real-time system design. This paper presents a methodology for the modeling and simulation of time-constrained message routing policies for hypercube interconnected real-time systems. The methodology is based on a framework called the DEVS (discrete event systems specification) formalism which supports modular and hierarchical specification of discrete event models. Within the methodology, we first develop DEVS specification for models for hypercube computers and experimental frames to measure the performance of alternative message routing policies. We then implement such specification in DEVSIM++, a C++-based modeling/simulation environment that implements the DEVS formalism. Simulations of various message routing policies are performed, and the performances of such policies are compared.     

An agent-based simulation model of enterprises financial distress for the enterprise of different life cycle stage

This paper proposes a new agent-based simulation model to simulate the causes and processes of enterprise financial distress. The general framework of the model including four agents, which are enterprise, product, bank and macro environment is described. By investigating the different causes of financial distress in enterprise’s different life cycle stages, we implement the simulation model to four specific cases which belong to start-up, growth, mature and decline life cycle stages respectively. Then a comparative analysis between our simulation results and real situation in four proposed specific cases is conducted, demonstrating that the proposed simulation model is a promising tool for comprehensively analyzing the causes and processes of financial distress.     

Modelling a Society of Simple Agents: From Conceptual Specification to Experimentation

Much research concerning the design of multi-agent systems (at a conceptual level) addresses complex agents that exhibit complex interaction patterns. Due to this complexity, it is difficult to perform rigorous experimentation. On the other hand, systematic experimental work regarding behaviour of societies of more simple agents, while reporting valuable results, often lacks conceptual specification of the system under consideration. In this paper, the compositional multi-agent modelling framework DESIRE is not only successfully used to develop a conceptual specification of the simple agents discussed by A. Cesta, M. Miceli and P. Rizzo (Lecture Notes in Artificial Intelligence, vol. 1038, Springer-Verlag: Berlin, pp. 128–138, 1996), but also to simulate the behaviour in a dynamical environment. In the DESIRE framework, a conceptual specification, which provides a high-level view of an agent, has enough detail for automatic prototype generation. The prototype implementation of the con-ceptual specification of the simple agents has been used to replicate, and extend, one of the experiments reported by Cesta et al.     

Distributed Social Dilemma With Competitive Meta-players

In our research, we propose the solution to the 'Distributed Social Dilemma' as an extended social dilemma model. In this model, in order to prevent the players from becoming freeloaders, we introduce competitive meta-players. Our purpose is to evaluate, through agent-based simulation, how the meta-players effect to increase cooperation amongst players in a distributed social dilemma. In order to examine our proposed solution, we introduce the preference of the players to cooperate as the measure of the effect of the meta-players. On the basis of the result of the simulations, we show the degree of the effect of the competitive meta-players on the distribution social dilemma.     

Internet scheduling environment with market-driven agents

This paper describes a new generation scheduling paradigm, the Internet scheduling environment. It is formed by a group of Internet scheduling agents which share computational resources to solve scheduling problems in a distributed and collaborative manner. We propose a migration scheme to transform existing standalone scheduling systems to Internet scheduling agents that can communicate with each other and solve problems beyond individual capabilities. To coordinate computational resource collaboration among agents, we introduce the market-based control mechanism in which self-interested agents initiate or participate in auctions to sell or buy scheduling problems. Efficient allocation of computational resources is achieved through the auctions. This paper also describes a prototype Internet scheduling environment named LekiNET, which is migrated from LEKIN/spl reg/, a flexible job shop scheduling system. The experiments on the LekiNET testbed demonstrate that the agent-based market-driven Internet scheduling environment is feasible and advantageous to future scheduling research and development.     

Integrating BDI Agents with Agent-Based Simulation Platforms

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.     

A knowledge-based simulation environment for hierarchical flexiblemanufacturing

This article presents an approach to embedding expert systems within an object oriented simulation environment. The basic idea is to create classes of expert system models that can be interfaced with other model classes. An expert system shell is developed within a knowledge-based design and simulation environment which combines artificial intelligence and systems modeling concepts. In the given framework, interruptible and distributed expert systems can be defined as components of simulations models. This facilitates simulation modeling of knowledge-based controls for flexible manufacturing and many other autonomous intelligent systems. Moreover, the structure of a system can be specified using a recursive system entity structure (SES) and unfolded to generate a family of hierarchical structures using an extension of SES pruning called recursive pruning. This recursive generation of hierarchical structures is especially appropriate for design of multilevel flexible factories. The article illustrates the utility of the proposed framework within the flexible manufacturing context     

Agent-based intelligent system development for decision support in chemical process industry

This paper presents an agent-based intelligent system to support coordinate manufacturing execution and decision-making in chemical process industry. A multi-agent system (MAS) framework is developed to provide a flexible infrastructure for the integration of chemical process information and process models. The system comprise of a process knowledge base and a group of functional agents. Agents in the system can communicate and cooperate with each other to exchange and share information, and to achieve timely decisions in dealing with various scenarios in process operations and manufacturing management. Process simulation, artificial intelligent technique, rule-based decision supports are integrated in this system for process analysis, process monitoring, process performance prediction and operation suggestion. The implementation of this agent-based system was illustrated with two case studies, including one application in process monitoring and process performance prediction for a chemical process and one application in de-bottlenecking of a site utility system.