Agent-based Applications Research Articles

Destination choice modeling with spatially distributed constraints

Destination choice models are a key component of any transport and land-use model. Applications in agent-based models allow for destination choice on an individual level including personal variables, like trip purpose, or situational variables. Commonly applied methodologies stem from econometrics, discrete choice theory and utility maximization using either revealed or stated preference data. This paper presents a framework to integrate cross-section flows between distinct geographic areas, which can be obtained from cordon surveys or mobile phone data. Proposed optimization methodology—based on extended shadow price theory—accommodates these complementary data sources as spatially distributed constraints, in addition to the destination capacity constraints such as workplaces. The new generic and robust optimization methodology accounts for constraints as observed on cross-section flows and destination capacities while maintaining econometric choice model theory. As a proof of concept, the suggested methodology is successfully applied in a real-case, agent-based application covering the tri-national Basel region with about 2 million residents, and a large set of 2 · 104 distinct destination alternatives. Due to different wage levels in all three countries and other reasons, the region’s cross-border commuter flows are highly asymmetric. Including data on cross-border flows obtained from a cordon survey, the choice model’s mean deviation declines by 20% and more on a cross-section level and even more so on a choice alternative level, compared to calculations ignoring shadow prices. Moreover, multiple scenario calculations show considerable improvements in planning and forecasting applications. The results demonstrate the suitability and relevance of the proposed approach to optimize destination choice models with limited destination capacities in geographical regions usually characterized by travel demand asymmetries.

The WOOL Dialogue Platform: Enabling Interdisciplinary User-Friendly Development of Dialogue for Conversational Agents

Health coaching applications can include (embodied) conversational agents as coaches. The development of these agents requires an interdisciplinary cooperation between eHealth application developers, interaction designers and domain experts. Therefore, proper dialogue authoring tools and tools to integrate these dialogues in a conversational agent system are essential in the process of creating successful agent-based applications. However, we found no existing open source, easy-to-use authoring tools that support multidisciplinary agent development. To that end, we developed the WOOL Dialogue Platform. The WOOL Dialogue Platform provides the eHealth and conversational agent communities with an open source platform, consisting of a set of easy to use tools that facilitate virtual agent development. The platform consists of a dialogue definition language, an editor, application development libraries and a web service. To illustrate the platform’s possibilities and use in practice, we describe two use cases from EU Horizon 2020 research projects. The WOOL Dialogue Platform is an ‘easy to use, and powerful if needed’ platform for the development of conversational agent applications that is seeing a slow but steady increase in uptake in the eHealth community. Developed to support dialogue authoring for embodied conversational agents in the health coaching domain, this platform’s strong points are its ease of use and ability to let domain experts and agents technology experts work together by providing all parties with tools that support their work effectively.

Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems

Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA’s value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications.

The FLAME-accelerated signalling tool (FaST) for facile parallelisation of flexible agent-based models of cell signalling

Agent-based modelling is particularly adept at modelling complex features of cell signalling pathways, where heterogeneity, stochastic and spatial effects are important, thus increasing our understanding of decision processes in biology in such scenarios. However, agent-based modelling often is computationally prohibitive to implement. Parallel computing, either on central processing units (CPUs) or graphical processing units (GPUs), can provide a means to improve computational feasibility of agent-based applications but generally requires specialist coding knowledge and extensive optimisation. In this paper, we address these challenges through the development and implementation of the FLAME-accelerated signalling tool (FaST), a software that permits easy creation and parallelisation of agent-based models of cell signalling, on CPUs or GPUs. FaST incorporates validated new agent-based methods, for accurate modelling of reaction kinetics and, as proof of concept, successfully converted an ordinary differential equation (ODE) model of apoptosis execution into an agent-based model. We finally parallelised this model through FaST on CPUs and GPUs resulting in an increase in performance of 5.8× (16 CPUs) and 53.9×, respectively. The FaST takes advantage of the communicating X-machine approach used by FLAME and FLAME GPU to allow easy alteration or addition of functionality to parallel applications, but still includes inherent parallelisation optimisation. The FaST, therefore, represents a new and innovative tool to easily create and parallelise bespoke, robust, agent-based models of cell signalling.

How Long Does It Last to Systematically Make Bad Decisions? An Agent-Based Application for Dividend Policy

Bad decisions have harmful effects on the quality of human life and an increase of their duration expands these undesirable effects. Systematic bad decisions related to dividend policy can affect the investors’ quality of life in the long-term. We propose an agent-based model for the estimation of the duration of systematically making bad decisions, with an application on dividend policy. We propose an algorithm that can be used in modelling the interaction between different classes of shareholders and for predicting this duration. We perform numerical simulations based on this model using NetLogo 6.0.4. We prove that, as a result of agents’ interaction, in some conditions, the duration of systematically making bad decisions can be very long: some numerical simulations suggest that, in some circumstances, this duration can significantly exceed the human lifetime. Additionally, in some conditions, the company can fail before the power is switched. This duration can increase dramatically if the shareholders have a great level of trust in the management’s decisions. As an implication, a greater concern for the quality of financial education, and more performant instruments for controlling the power’s decisions are required.

Multi-Agent Systems

With the current advance of technology, agent-based applications are becoming a standard in a great variety of domains such as e-commerce, logistics, supply chain management, telecommunications, healthcare, and manufacturing. Another reason for the widespread interest in multi-agent systems is that these systems are seen as a technology and a tool that helps in the analysis and development of new models and theories in large-scale distributed systems or in human-centered systems. This last aspect is currently of great interest due to the need for democratization in the use of technology that allows people without technical preparation to interact with the devices in a simple and coherent way. In this Special Issue, different interesting approaches that advance this research discipline have been selected and presented.

An agent-based approach to evaluate collaborative strategies in milk-run OEM operations

The management of transport operations is a promising field for agent-based applications. Acting in a collaborative way, the agents can be seen as a multi-agent system that enables vehicles to handle new information perceived during the operation along cargo collecting routes. Traditional research in this field usually focus on the servicing of new transportation demands, in this paper the agents could detect the presence of unexpected traffic congestions and deciding about the transference of tasks to others vehicles. This article evaluates collaborative strategies for applying the agent-based approach to a dynamic vehicle routing formulation, where vehicles are assigned to picking-up components and parts from Original Equipment Manufacturer suppliers. A sequential probabilistic ratio test is used to predict eventual unperformed collecting tasks. Two evaluating criteria are simultaneously used to select a specific collaborative strategy: the service level, represented by the average percentage of performed tasks, plus the average daily operating cost of the vehicle fleet. The evaluation of collaboration strategies between vehicles allowed understand the functioning of distributed decision making, contributing to the adoption of autonomous control methods of transportation operations.

Towards Data-Driven Simulation Modeling for Mobile Agent-Based Systems

Simulation models are widely used to study complex systems. Current simulation models are generally handcrafted using expert knowledge (knowledge-driven); however, this process is slow and introduces modeler bias. This article presents an approach towards data-driven simulation modeling by developing a framework that discovers simulation models in an automated way for mobile agent-based applications. The framework is comprised of three components: (1) a model space specification, (2) a search method (genetic algorithm), and (3) framework measurement metrics. The model space specification provides a formal specification for the general model structure from which various models can be generated. The search method is used to efficiently search the model space for candidate models that exhibit desired behavior patterns. The five framework measurement metrics: flexibility, comprehensibility, controllability, composability, and robustness, are developed to evaluate the overall framework. The results demonstrate that it is possible to discover a variety of interesting models using the framework.

Designing a benchmark for the performance evaluation of agent-based simulation applications on HPC

Agent-based modeling and simulation (ABMS) is a class of computational models for simulating the actions and interactions of autonomous agents with the goal of assessing their effects on a system as a whole. Several frameworks for generating parallel ABMS applications have been developed taking advantage of their common characteristics, but there is a lack of a general benchmark for comparing the performance of the generated applications. We propose and design a benchmark that takes into consideration the most common characteristics of this type of applications and includes parameters for influencing their relevant performance aspects. We provide an initial implementation of the benchmark for FLAME, FLAME GPU, Repast HPC and EcoLab, some of the most popular parallel ABMS platforms, and use it for comparing the applications generated by these platforms. The obtained results are mostly in agreement with previous studies, but the designed and implemented specification has allowed for testing a wider set of aspects, such as the number of interacting agents, the amount of interchanged data or the evolution of the workload and obtaining more reliable results.

Multi-agents features on Android platforms

The current paper shows the multi-agents capabilities for valid and flexible applications when using a framework. Agent-based functions were used within JADE framework for an Android messaging application with all requirements included. In the paper are described the architecture, the main functions and the databases integration of a user friendly agent-based application. There are included existing and possible multi-agents characteristics to provide integration with mobile platforms and storage challenges to improve the user experience through data mining.

Using Agent-Based Methodologies in Healthcare Information Systems

Abstract This paper carries out a comparative analysis to determine the advantages and the stages of two agent-based methodologies: Multi-agent Systems Engineering (MaSE) methodology, which is designed specifically for an agent-based and complete lifecycle approach, while also being appropriate for understanding and developing complex open systems; Agent Systems Methodology (ASEME) suggests a modular Multi-Agent System (MAS) development approach and uses the concept of intra-agent control. We also examine the strengths and weaknesses of these methodologies and the dependencies between their models and their processes. Both methodologies are applied to develop The Guardian Angle: Patient-Centered Health Information System (GA: PCHIS), which is an example of agent-based applications used to improve health care information systems.

Network on Chip Architecture for Multi-Agent Systems in FPGA

A system of interacting agents is, by definition, very demanding in terms of computational resources. Although multi-agent systems have been used to solve complex problems in many areas, it is usually very difficult to perform large-scale simulations in their targeted serial computing platforms. Reconfigurable hardware, in particular Field Programmable Gate Arrays devices, have been successfully used in High Performance Computing applications due to their inherent flexibility, data parallelism, and algorithm acceleration capabilities. Indeed, reconfigurable hardware seems to be the next logical step in the agency paradigm, but only a few attempts have been successful in implementing multi-agent systems in these platforms. This article discusses the problem of inter-agent communications in Field Programmable Gate Arrays. It proposes a Network-on-Chip in a hierarchical star topology to enable agents’ transactions through message broadcasting using the Open Core Protocol as an interface between hardware modules. A customizable router microarchitecture is described and a multi-agent system is created to simulate and analyse message exchanges in a generic heavy traffic load agent-based application. Experiments have shown a throughput of 1.6Gbps per port at 100MHz without packet loss and seamless scalability characteristics.

Run-time environment for the SARL agent-programming language: the example of the Janus platform

SARL is a general-purpose agent-oriented programming language. This language aims at providing the fundamental abstractions for dealing with concurrency, distribution, interaction, decentralization, reactivity, autonomy and dynamic reconfiguration that are usually considered as essential for implementing agent-based applications. Every programming language specifies an execution model. For SARL, this run-time model is supported by a SARL run-time environment. The goals of this paper are to highlight the key principles for creating a SARL run-time environment, and its concrete implementation into the Janus agent platform.

Towards a Hybrid MAS Organizational Model

The organizational aspects are currently getting a great attention within the multi-agent systems (MAS) community. The motivation towards this trend is finding a way to handle the increasing complexity and distribution of modern agent-based applications using higher order abstractions such as agent organizations. It is a transition from concerning the micro level (individual agents) to concerning the macro level (the whole system) to handle complexity. A large number of MAS organizational models can be found in MAS literature. Some of them adopt the ACMAS (Agent-Centered MAS) viewpoint and others adopt the OCMAS (Organizational-Centered MAS) viewpoint. Each of the ACMAS and OCMAS viewpoints has its advantages and disadvantages; therefore, combining them into a hybrid model is expected to give us the chance to take benefit of their advantages and avoid their disadvantages. This chapter presents our recent work towards the conceptual design of a hybrid MAS organizational model that combines both of the ACMAS and OCMAS viewpoints.

Mobile Agents System Security

A pivotal cause for the boom of Mobile Agent paradigm relies on the competence to ward off security attacks. This article surveys the prevalent attacks on the mobile agents and the agent platforms; the existing countermeasures and their curbs, catering threefold benefaction: First, to acquaint the researchers with numerous security requirements and the objectives. Second, to analyze the different detection and prevention mechanisms mitigating the security bottlenecks in Mobile Agents System. Third, to address the future security challenges. In a nutshell, this survey hands over a key to researchers who primarily target the security concerns of the mobile agent-based applications.

A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

A generic constraint handling framework for use with any swarm-based optimization algorithm is presented. For swarm optimizers to solve constrained optimization problems effectively modifications have to be made to the optimizers to handle the constraints, however, these constraint handling frameworks are often not universally applicable to all swarm algorithms. A constraint handling framework is therefore presented in this paper that is compatible with any swarm optimizer, such that a user can wrap it around a chosen swarm algorithm and perform constrained optimization. The method, called separation-sub-swarm, works by dividing the population based on the feasibility of individual agents. This allows all feasible agents to move by existing swarm optimizer algorithms, hence promoting good performance and convergence characteristics of individual swarm algorithms. The framework is tested on a suite of analytical test function and a number of engineering benchmark problems, and compared to other generic constraint handling frameworks using four different swarm optimizers; particle swarm, gravitational search, a hybrid algorithm and differential evolution. It is shown that the new framework produces superior results compared to the established frameworks for all four swarm algorithms tested. Finally, the framework is applied to an aerodynamic shape optimization design problem where a shock-free solution is obtained.

Mobile Agent-Based Software Systems Modeling Approaches: A Comparative Study

agent-based applications are special type of software systems which take the advantages of mobile agents in order to provide a new beneficial paradigm to solve multiple complex problems in several fields and areas such as network management, e-commerce, e-learning, etc. Likewise, we notice lack of real applications based on this paradigm and lack of serious evaluations of their modeling approaches. Hence, this paper provides a comparative study of modeling approaches of mobile agent-based software systems. The objective is to give the reader an overview and a thorough understanding of the work that has been done and where the gaps in the research are. ACM CCS (2012) Classification : Computing methodologies → Artificial intelligence → Distributed artificial intelligence → agents *To cite this article: A. Belghiat et al ., Mobile Agent-Based Software Systems Modeling Approaches: A Comparative Study, CIT. Journal of Computing and Information Technology , vol. 24, no. 2, pp. 149–163, 2016.

A Performance Evaluation of a Multi-Agent Mobile Learning System

Mobile devices have emerged as our daily companion whose applicability evolves as the day unfolds. One of such applications is in the area of learning, called mobile learning (M-learning). However, as with all new technologies, M-learning is faced with the issues of standard, content packing, and deployment. And like other distributed applications on ubiquitous networks, M-learning is challenged with performance issues. This work shows the implementation and evaluation of a model for intelligent mobile learning system (IMLS) using a multi-agent system (MAS), such as the Java Agent Development Environment (JADE) framework. Two M-learning applications (agent-based and non-agent) were developed, deployed and tested. The agent-based application is deployed using an HTTP-based Message Transfer Protocol (MTP). The results suggest that agents can increase the performance of an M-learning application up to eight times.

Secure Data Storage in Cloud Environment using MAS

Objectives: The objective is to introduce multi-agent systems to enhance the security rules through the access right to build a distributed warehouse in the cloud environment secure manner. The work aim is to take a smart decision making using MAS in a timely manner. Analysis: Cloud computing is a very powerful, and predictable in computing infrastructure for implementing complex agent-based applications. Cloud computing in Multi-Agent System (MAS) appears as an approach to current challenges in many areas. The distributed data warehousing is used to do based on how data’s are distributed in the multiple servers.Its main problemishow the Collaborative work of a multi-agent system designed for distributed data warehousing in the cloud environment. Findings: In the existing approach, a major issue is to retrieve the relevant data from the cloud storage is a very difficult task. Other issues in attributes are increasing the network loads (traffics) and response time, the need of secure data storage, data retrieval from the cloud environment and the database updating is very slow.Cloud infrastructures provide a platform to run the MAS in the real-time, because it takes large execution time by havinga large amount of data processing and dynamic memory. Improvement: In this paper, we introduce data warehouse in the cloud computing through the multi-agent system technology. This enables cost and time saving. The technique based on data warehouse in the cloud environment using Multi-Agent Systems (MAS) technology to consider security and privacy in data storage and transmitted. The proposed system is the use of an MAS in the cloud environment, introduces autonomous decision making in the critical situation to speed up the execution time, response time, database updating and security enhancing. We apply this propose system in any application like e-banking, hospital management, election department, etc.

DESIGNING A BDI AGENT REACTANT MODEL OF BEHAVIOURAL CHANGE INTERVENTION

Belief-Desire-Intention (BDI) model is well suited for describing agent’s mental state. The BDI of an agent represents its motivational stance and are the main determinant of agent’s actions. Therefore, explicit understanding of the representation and modelling of such motivational stance plays a central role in designing BDI agent with successful behavioural change interventions. Nevertheless, existing BDI agent models do not represent agent’s behavioural factors explicitly. This leads to a gap between design and implementation where psychological reactance has being identified as the cause of BDI agent behavioural change interventions failure. Hence, this paper presents a generic representation of BDI agent model based on behavioural change and psychological theories. Also, using mathematical analysis the model was evaluated. The objective of the proposed BDI agent model is to bridge the gap between agent design and implementation for successful agent-based interventions. The model will be realized in an agent-based application that motivates children towards oral hygiene. The study explicitly depicts how agent’s behavioural factors interact to enhance behaviour change which will assist agent-based intervention designers to be able to design intervention that will be void of reactance.