Arrow Research search

Author name cluster

Danny Weyns

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

26 papers
2 author rows

Possible papers

26

TAAS Journal 2025 Journal Article

An Architectural Viewpoint for Benefit-Cost-Risk-Aware Decision-Making in Self-Adaptive Systems

  • Danny Weyns
  • Sara M. Hezavehi
  • Paris Avgeriou
  • Radu Calinescu
  • Raffaela Mirandola
  • Diego Perez-Palacin

Self-adaptation equips a software system with a feedback loop that resolves uncertainties during operation and adapts the system to deal with them when necessary. Most self-adaptation approaches today use decision-making mechanisms that select for execution the adaptation option with the best-estimated benefit expressed as a set of adaptation goals. A few approaches also consider the estimated (one-off) cost of executing the candidate adaptation options. We argue that besides benefit and cost, decision-making in self-adaptive systems should also consider the estimated risk the system or its users would be exposed to if an adaptation option were selected for execution. Balancing all three concerns when evaluating the options for adaptation to mitigate uncertainty is essential for satisfying stakeholders’ concerns and ensuring the safety and public acceptance of self-adaptive systems. In this article, we present a reference model for decision-making in self-adaptation that considers the estimated benefit, cost, and risk as core concerns of each adaptation option. Leveraging this model, we then present an ISO/IEC/IEEE 42010 compatible architectural viewpoint that aims at supporting software architects responsible for designing robust decision-making mechanisms for self-adaptive systems. We demonstrate the applicability, usefulness, and understandability of the viewpoint through a case study where participants with experience in the engineering of self-adaptive systems performed a set of design tasks in DeltaIoT, an Internet-of-Things exemplar for research on self-adaptive systems.

Details DOI

TAAS Journal 2025 Journal Article

Reflection, Quick Reference Guide and Future Outlook to Autonomous and Adaptive Systems

  • Danny Weyns

Researchers and engineers of autonomous and adaptive systems have always been fascinated by computing systems that have the ability to solve problems independently. In this reflection article, we look at three main families of such computing systems that emerged over the past two decades: Self-Organizing Systems, Self-Adaptive Systems, and Symbiotic HAI Systems that leverage the collective capabilities of Humans and AI-empowered autonomous and adaptive systems. For each family of these systems, we highlight the foundations, principles, and approaches, as well as system engineering. To conclude, we look at a number of challenges for future research in three key areas of this fascinating field: technical, ethical, and regulatory. These challenges open new fundamental and engineering problems for autonomous and adaptive systems.

Details DOI

TAAS Journal 2024 Journal Article

Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems Using Lifelong Self-Adaptation

  • Omid Gheibi
  • Danny Weyns

Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this article, we focus on a particularly important challenge for learning-based self-adaptive systems: drift in adaptation spaces. With adaptation space, we refer to the set of adaptation options a self-adaptive system can select from to adapt at a given time based on the estimated quality properties of the adaptation options. A drift of adaptation spaces originates from uncertainties, affecting the quality properties of the adaptation options. Such drift may imply that the quality of the system may deteriorate, eventually, no adaptation option may satisfy the initial set of adaptation goals, or adaptation options may emerge that allow enhancing the adaptation goals. In ML, such a shift corresponds to a novel class appearance, a type of concept drift in target data that common ML techniques have problems dealing with. To tackle this problem, we present a novel approach to self-adaptation that enhances learning-based self-adaptive systems with a lifelong ML layer. We refer to this approach as lifelong self-adaptation. The lifelong ML layer tracks the system and its environment, associates this knowledge with the current learning tasks, identifies new tasks based on differences, and updates the learning models of the self-adaptive system accordingly. A human stakeholder may be involved to support the learning process and adjust the learning and goal models. We present a general architecture for lifelong self-adaptation and apply it to the case of drift of adaptation spaces that affects the decision-making in self-adaptation. We validate the approach for a series of scenarios with a drift of adaptation spaces using the DeltaIoT exemplar.

Details DOI

TAAS Journal 2024 Journal Article

Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap

  • Jialong Li
  • Mingyue Zhang
  • Nianyu Li
  • Danny Weyns
  • Zhi Jin
  • Kenji Tei

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this article aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI’s within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies. †

Details DOI

TAAS Journal 2023 Journal Article

Self-Adaptation in Industry: A Survey

  • Danny Weyns
  • Ilias Gerostathopoulos
  • Nadeem Abbas
  • Jesper Andersson
  • Stefan Biffl
  • Premek Brada
  • Tomas Bures
  • Amleto Di Salle

Computing systems form the backbone of many areas in our society, from manufacturing to traffic control, healthcare, and financial systems. When software plays a vital role in the design, construction, and operation, these systems are referred to as software-intensive systems. Self-adaptation equips a software-intensive system with a feedback loop that either automates tasks that otherwise need to be performed by human operators or deals with uncertain conditions. Such feedback loops have found their way to a variety of practical applications; typical examples are an elastic cloud to adapt computing resources and automated server management to respond quickly to business needs. To gain insight into the motivations for applying self-adaptation in practice, the problems solved using self-adaptation and how these problems are solved, and the difficulties and risks that industry faces in adopting self-adaptation, we performed a large-scale survey. We received 184 valid responses from practitioners spread over 21 countries. Based on the analysis of the survey data, we provide an empirically grounded overview the of state of the practice in the application of self-adaptation. From that, we derive insights for researchers to check their current research with industrial needs, and for practitioners to compare their current practice in applying self-adaptation. These insights also provide opportunities for applying self-adaptation in practice and pave the way for future industry-research collaborations.

Details DOI

TAAS Journal 2022 Journal Article

Deep Learning for Effective and Efficient Reduction of Large Adaptation Spaces in Self-adaptive Systems

  • Danny Weyns
  • Omid Gheibi
  • Federico Quin
  • Jeroen Van Der Donckt

Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize the system goals. Self-adaptation is a common approach to tackle such uncertainties. When the system goals may be compromised, the self-adaptive system has to select the best adaptation option to reconfigure by analyzing the possible adaptation options, i.e., the adaptation space. Yet, analyzing large adaptation spaces using rigorous methods can be resource- and time-consuming, or even be infeasible. One approach to tackle this problem is by using online machine learning to reduce adaptation spaces. However, existing approaches require domain expertise to perform feature engineering to define the learner and support online adaptation space reduction only for specific goals. To tackle these limitations, we present “Deep Learning for Adaptation Space Reduction Plus”—DLASeR+ for short. DLASeR+ offers an extendable learning framework for online adaptation space reduction that does not require feature engineering, while supporting three common types of adaptation goals: threshold, optimization, and set-point goals. We evaluate DLASeR+ on two instances of an Internet-of-Things application with increasing sizes of adaptation spaces for different combinations of adaptation goals. We compare DLASeR+ with a baseline that applies exhaustive analysis and two state-of-the-art approaches for adaptation space reduction that rely on learning. Results show that DLASeR+ is effective with a negligible effect on the realization of the adaptation goals compared to an exhaustive analysis approach and supports three common types of adaptation goals beyond the state-of-the-art approaches.

Details DOI

TAAS Journal 2020 Journal Article

Applying Machine Learning in Self-adaptive Systems

  • Omid Gheibi
  • Danny Weyns
  • Federico Quin

Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

Details DOI

TAAS Journal 2020 Journal Article

Uncertainty in Self-adaptive Systems: A Research Community Perspective

  • Sara M. Hezavehi
  • Danny Weyns
  • Paris Avgeriou
  • Radu Calinescu
  • Raffaela Mirandola
  • Diego Perez-Palacin

One of the primary drivers for self-adaptation is ensuring that systems achieve their goals regardless of the uncertainties they face during operation. Nevertheless, the concept of uncertainty in self-adaptive systems is still insufficiently understood. Several taxonomies of uncertainty have been proposed, and a substantial body of work exists on methods to tame uncertainty. Yet, these taxonomies and methods do not fully convey the research community’s perception on what constitutes uncertainty in self-adaptive systems and on the key characteristics of the approaches needed to tackle uncertainty. To understand this perception and learn from it, we conducted a survey comprising two complementary stages in which we collected the views of 54 and 51 participants, respectively. In the first stage, we focused on current research and development, exploring how the concept of uncertainty is understood in the community and how uncertainty is currently handled in the engineering of self-adaptive systems. In the second stage, we focused on directions for future research to identify potential approaches to dealing with unanticipated changes and other open challenges in handling uncertainty in self-adaptive systems. The key findings of the first stage are: (a) an overview of uncertainty sources considered in self-adaptive systems, (b) an overview of existing methods used to tackle uncertainty in concrete applications, (c) insights into the impact of uncertainty on non-functional requirements, (d) insights into different opinions in the perception of uncertainty within the community and the need for standardised uncertainty-handling processes to facilitate uncertainty management in self-adaptive systems. The key findings of the second stage are: (a) the insight that over 70% of the participants believe that self-adaptive systems can be engineered to cope with unanticipated change, (b) a set of potential approaches for dealing with unanticipated change, (c) a set of open challenges in mitigating uncertainty in self-adaptive systems, in particular in those with safety-critical requirements. From these findings, we outline an initial reference process to manage uncertainty in self-adaptive systems. We anticipate that the insights on uncertainty obtained from the community and our proposed reference process will inspire valuable future research on self-adaptive systems.

Details DOI

TAAS Journal 2018 Journal Article

SimCA*

  • Stepan Shevtsov
  • Danny Weyns
  • Martina Maggio

Self-adaptation provides a principled way to deal with software systems’ uncertainty during operation. Examples of such uncertainties are disturbances in the environment, variations in sensor readings, and changes in user requirements. As more systems with strict goals require self-adaptation, the need for formal guarantees in self-adaptive systems is becoming a high-priority concern. Designing self-adaptive software using principles from control theory has been identified as one of the approaches to provide guarantees. In general, self-adaptation covers a wide range of approaches to maintain system requirements under uncertainty, ranging from dynamic adaptation of system parameters to runtime architectural reconfiguration. Existing control-theoretic approaches have mainly focused on handling requirements in the form of setpoint values or as quantities to be optimized. Furthermore, existing research primarily focuses on handling uncertainty in the execution environment. This article presents SimCA*, which provides two contributions to the state-of-the-art in control-theoretic adaptation: (i) it supports requirements that keep a value above and below a required threshold, in addition to setpoint and optimization requirements; and (ii) it deals with uncertainty in system parameters, component interactions, system requirements, in addition to uncertainty in the environment. SimCA* provides guarantees for the three types of requirements of the system that is subject to different types of uncertainties. We evaluate SimCA* for two systems with strict requirements from different domains: an Unmanned Underwater Vehicle system used for oceanic surveillance and an Internet of Things application for monitoring a geographical area. The test results confirm that SimCA* can satisfy the three types of requirements in the presence of different types of uncertainty.

Details DOI

TAAS Journal 2015 Journal Article

MAPE-K Formal Templates to Rigorously Design Behaviors for Self-Adaptive Systems

  • Didac Gil De La Iglesia
  • Danny Weyns

Designing software systems that have to deal with dynamic operating conditions, such as changing availability of resources and faults that are difficult to predict, is complex. A promising approach to handle such dynamics is self-adaptation that can be realized by a MAPE-K feedback loop (Monitor-Analyze-Plan-Execute plus Knowledge). To provide evidence that the system goals are satisfied, given the changing conditions, the state of the art advocates the use of formal methods. However, little research has been done on consolidating design knowledge of self-adaptive systems. To support designers, this paper contributes with a set of formally specified MAPE-K templates that encode design expertise for a family of self-adaptive systems. The templates comprise: (1) behavior specification templates for modeling the different components of a MAPE-K feedback loop (based on networks of timed automata), and (2) property specification templates that support verification of the correctness of the adaptation behaviors (based on timed computation tree logic). To demonstrate the reusability of the formal templates, we performed four case studies in which final-year Masters students used the templates to design different self-adaptive systems.

Details DOI

AAMAS Conference 2013 Conference Paper

SA-MAS: Self-Adaptation to Enhance Software Qualities in Multi-Agent Systems

  • Didac Gil De La Iglesia
  • Danny Weyns

Engineering multi-agent systems (MAS) is known to be a complex task. One of the reasons lays in the complexity to combine multiple concerns that a MAS has to address, such as system functionality, coordination, robustness, etc. A well-recognized approach to manage system complexity is the use of self-adaptation (SA). Self-adaptation extends a system with support to monitor and adapt itself to realize a concern of interest (optimization, fault-tolerance, etc.). We present SA-MAS, an architectural approach that integrates MAS with SA. We present a reference model for SA-MAS and illustrate it with an excerpt from our research.

PDF

TAAS Journal 2012 Journal Article

FORMS

  • Danny Weyns
  • Sam Malek
  • Jesper Andersson

The challenges of pervasive and mobile computing environments, which are highly dynamic and unpredictable, have motivated the development of self-adaptive software systems. Although noteworthy successes have been achieved on many fronts, the construction of such systems remains significantly more challenging than traditional systems. We argue this is partially because researchers and practitioners have been struggling with the lack of a precise vocabulary for describing and reasoning about the key architectural characteristics of self-adaptive systems. Further exacerbating the situation is the fact that existing frameworks and guidelines do not provide an encompassing perspective of the different types of concerns in this setting. In this article, we present a comprehensive reference model, entitled FOrmal Reference Model for Self-adaptation (FORMS), that targets both issues. FORMS provides rigor in the manner such systems can be described and reasoned about. It consists of a small number of formally specified modeling elements that correspond to the key concerns in the design of self-adaptive software systems, and a set of relationships that guide their composition. We demonstrate FORMS's ability to precisely describe and reason about the architectural characteristics of distributed self-adaptive software systems through its application to several existing systems. FORMS's expressive power gives it a potential for documenting reusable architectural solutions (e.g., architectural patterns) to commonly encountered problems in this area.

Details DOI

TAAS Journal 2010 Journal Article

The MACODO middleware for context-driven dynamic agent organizations

  • Danny Weyns
  • Robrecht Haesevoets
  • Alexander Helleboogh
  • Tom Holvoet
  • Wouter Joosen

One of the major challenges in engineering distributed multiagent systems is the coordination necessary to align the behavior of different agents. Decentralization of control implies a style of coordination in which the agents cooperate as peers with respect to each other and no agent has global control over the system, or global knowledge about the system. The dynamic interactions and collaborations among agents are usually structured and managed by means of roles and organizations. In existing approaches agents typically have a dual responsibility: on the one hand playing roles within the organization, on the other hand managing the life-cycle of the organization itself, for example, setting up the organization and managing organization dynamics. Engineering realistic multiagent systems in which agents encapsulate this dual responsibility is a complex task. In this article, we present a middleware for context-driven dynamic agent organizations. The middleware is part of an integrated approach, called MACODO: Middleware Architecture for COntext-driven Dynamic agent Organizations. The complementary part of the MACODO approach is an organization model that defines abstractions to support application developers in describing dynamic organizations, as described in Weyns et al. [2010]. The MACODO middleware offers the life-cycle management of dynamic organizations as a reusable service separated from the agents, which makes it easier to understand, design, and manage dynamic organizations in multiagent systems. We give a detailed description of the software architecture of the MADOCO middleware. The software architecture describes the essential building blocks of a distributed middleware platform that supports the MACODO organization model. We used the middleware architecture to develop a prototype middleware platform for a traffic monitoring application. We evaluate the MACODO middeware architecture by assessing the adaptability, scalability, and robustness of the prototype platform.

Details DOI

TAAS Journal 2010 Journal Article

The MACODO organization model for context-driven dynamic agent organizations

  • Danny Weyns
  • Robrecht Haesevoets
  • Alexander Helleboogh

Today's distributed applications such as sensor networks, mobile multimedia applications, and intelligent transportation systems pose huge engineering challenges. Such systems often comprise different components that interact with each other as peers, as such forming a decentralized system. The system components and collaborations change over time, often in unanticipated ways. Multiagent systems belong to a class of decentralized systems that are known for realizing qualities such as adaptability, robustness, and scalability in such environments. A typical way to structure and manage interactions among agents is by means of organizations. Existing approaches usually endow agents with a dual responsibility: on the one hand agents have to play roles providing the associated functionality in the organization, on the other hand agents are responsible for setting up organizations and managing organization dynamics. Engineering realistic multiagent systems in which agents encapsulate this dual responsibility is a complex task. In this article, we present an organization model for context-driven dynamic agent organizations. The model defines abstractions that support application developers to describe dynamic organizations. The organization model is part of an integrated approach, called MACODO: Middleware Architecture for COntext-driven Dynamic agent Organizations. The complementary part of the MACODO approach is a middleware platform that supports the distributed execution of dynamic organizations specified using the abstractions, as described in Weyns et al. [2009]. In the model, the life-cycle management of dynamic organizations is separated from the agents: organizations are first-class citizens, and their dynamics are governed by laws. The laws specify how changes in the system (e.g., an agent joins an organization) and changes in the context (e.g., information observed in the environment) lead to dynamic reorganizations. As such, the model makes it easier to understand and specify dynamic organizations in multiagent systems, and promotes reusing the life-cycle management of dynamic organizations. The organization model is formally described to specify the semantics of the abstractions, and ensure its type safety. We apply the organization model to specify dynamic organizations for a traffic monitoring application.

Details DOI

JAAMAS Journal 2008 Journal Article

A field-based versus a protocol-based approach for adaptive task assignment

  • Danny Weyns
  • Nelis Boucké
  • Tom Holvoet

Abstract Task assignment in multi-agent systems is a complex coordination problem, in particular in systems that are subject to dynamic and changing operating conditions. To enable agents to deal with dynamism and change, adaptive task assignment approaches are needed. In this paper, we study two approaches for adaptive task assignment that are characteristic for two classical families of task assignment approaches. FiTA is a field-based approach in which tasks emit fields in the environment that guide idle agents to tasks. DynCNET is a protocol-based approach that extends Standard Contract Net (CNET). In DynCNET, agents use explicit negotiation to assign tasks. We compare both approaches in a simulation of an industrial automated transportation system. Our experiences show that: (1) the performance of DynCNET and FiTA are similar, while both outperform CNET; (2) the complexity to engineer DynCNET is similar to FiTA but much more complex than CNET; (3) whereas task assignment with FiTA is an emergent solution, DynCNET specifies the interaction among agents explicitly allowing engineers to reason on the assignment of tasks, (4) FiTA is inherently robust to message loss while DynCNET requires substantial additional support. The tradeoff between (3) and (4) is an important criteria for the selection of an adaptive task assignment approach in practice.

Details DOI

EUMAS Conference 2006 Conference Paper

DynCNET: A Protocol for Flexible Task Assignment Applied in an AGV Transportation System

  • Danny Weyns
  • Nelis Boucké
  • Tom Holvoet
  • Kurt Schelfthout

The work presented in this paper is part of an ongoing effort to study suitable task assignment mechanisms for decentralized MAS. Our focus is on systems that are characterized by tasks with delayed commencement. Such a task requires a preceding effort before the agent can start executing the task. An example is a robot that first has to move to the location of a task before it can start executing that task. Important quality requirements for assigning tasks with delayed commencement are flexibility (enable agents to adapt task assignment with changing circumstances) and openness (enable agents to take into account other agents that come and go during the process of task assignment). In previous work, we have studied Contract Net (CNET) and a field-based approach for task assignment (FiTA). CNET does not provide the required flexibility and openness. FiTA satisfies the required qualities, however, the field-based approach provides an emergent solution for task assignment. It is well known that emergent solutions are difficult to engineer and reason about. This raises the question whether it is not easier to extend CNET to take dynamics and changes into account. This paper presents the DynCNET protocol. DynCNET is an extention of CNET, with “Dyn” referring to support for dynamic task assignment. DynCNET provides flexibility and openness for assigning tasks with delayed commencement. We compare the DynCNET protocol with CNET and FiTA in an AGV transportation system. Our experiences in this real-world setting show that: (1) the performance of DynCNET and FiTA are similar, while both outperform CNET; (2) extending CNET to deal with delayed commencement of tasks is not obvious; the complexity to engineer DynCNET is similar to FiTA but much more complex than CNET; (3) whereas task assignment with FiTA is an emergent solution, DynCNET explicitly specifies the interaction among agents allowing engineers to reason on the assignment of tasks. This latter property may be of overriding importance in the selection of an agent-based approach for task assignment in practice.

Details

JAAMAS Journal 2006 Journal Article

Environment as a first class abstraction in multiagent systems

  • Danny Weyns
  • Andrea Omicini
  • James Odell

Abstract The current practice in multiagent systems typically associates the environment with resources that are external to agents and their communication infrastructure. Advanced uses of the environment include infrastructures for indirect coordination, such as digital pheromones, or support for governed interaction in electronic institutions. Yet, in general, the notion of environment is not well defined. Functionalities of the environment are often dealt with implicitly or in an ad hoc manner. This is not only poor engineering practice, it also hinders engineers to exploit the full potential of the environment in multiagent systems. In this paper, we put forward the environment as an explicit part of multiagent systems. We give a definition stating that the environment in a multiagent system is a first-class abstraction with dual roles: (1) the environment provides the surrounding conditions for agents to exist, which implies that the environment is an essential part of every multiagent system, and (2) the environment provides an exploitable design abstraction for building multiagent system applications. We discuss the responsibilities of such an environment in multiagent systems and we present a reference model for the environment that can serve as a basis for environment engineering. To illustrate the power of the environment as a design abstraction, we show how the environment is successfully exploited in a real world application. Considering the environment as a first-class abstraction in multiagent systems opens up new horizons for research and development in multiagent systems.

Details DOI

KER Journal 2005 Journal Article

Environments in multiagent systems

  • Danny Weyns
  • Michael Schumacher
  • Alessandro Ricci
  • Mirko Viroli
  • Tom Holvoet

There is a growing awareness in the multiagent systems research community that the environment plays a prominent role in multiagent systems. Originating from research on behavior-based agent systems and situated multiagent systems, the importance of the environment is now gradually being accepted in the multiagent system community in general. In this paper, we put forward the environment as a first-order abstraction in multiagent systems. This position is motivated by the fact that several aspects of multiagent systems that conceptually do not belong to agents themselves should not be assigned to, or hosted inside the agents. Examples are infrastructure for communication, the topology of a spatial domain or support for the action model. These and other aspects should be considered explicitly. The environment is the natural candidate to encapsulate these aspects. We elaborate on environment engineering, and we illustrate how the environment plays a central role in a real-world multiagent system application.

Details DOI
v2026.09.13