Accurate and interpretable water quality prediction is crucial for environmental monitoring and public health. This study evaluates six machine learning models—Random Forest, Long Short-Term Memory (LSTM), K-Nearest Neighbors (KNN), Linear Regression, Ridge Regression, and Support Vector Regression (SVR)—using real-world groundwater data from ARPAE. Model performance was assessed via Mean Absolute Error (MAE) and Mean Squared Error (MSE), while SHAP values were employed for feature-level interpretability. Results indicate that Random Forest outperforms all models in both accuracy and explainability, whereas SVR demonstrates poor predictive capability and lacks meaningful interpretability. The study highlights the trade-offs between predictive power and transparency, offering insights for selecting appropriate models in water quality monitoring systems.

Traffic congestion and vehicular emissions remain critical challenges in urban mobility. While reinforcement learning (RL) has shown promise in adaptive traffic signal control, conventional models may inadvertently encourage private vehicle use by merely reducing delay. In this study, we present a Q-learning-based traffic signal control framework enhanced with a vehicle prioritization mechanism for public transport and emergency vehicles. Implemented using the Simulation of Urban Mobility (SUMO), our approach is evaluated on a four-arm intersection scenario. Compared to fixed-time control, the standard Q-learning model achieves an $80 \%$ reduction in average vehicle delay and over $80 \%$ decrease in $C O_{2}$ emissions. The prioritized Q-learning variant further improves delay and emissions metrics while providing preferential treatment to high-impact vehicle categories. Crucially, this prioritization strategy helps incentivize public transport usage, mitigating the risk of increased private car dependence that often follows general congestion reduction efforts. Our results demonstrate that integrating vehicle prioritization into RL-based traffic control supports both sustainability and modal shift goals in intelligent transportation systems.

Digital Product Passports (DPPs) are emerging as foundational tools for transparency, traceability, and sustainability in global supply chains. As regulatory initiatives such as the European Union’s Ecodesign for Sustainable Products Regulation (ESPR) gain momentum, there is increasing demand for technical solutions that support decentralised management and retrieval of product lifecycle data. This paper proposes a lightweight, extensible architecture centred on the DPP Protocol—a general-purpose communication mechanism for interoperable access to product information. The approach is particularly suited to fragmented or highly variable production contexts, such as fashion and other sectors characterised by short product lifecycles, complex supply networks, and limited digital infrastructure. We present two complementary implementations: the DPP Software, a server-side system for managing product data, and the DPP Browser, a stateless client for visualising and querying digital passports. Together, these components demonstrate the feasibility and versatility of the proposed protocol. The solution emphasises interoperability, recursive querying, and role-based access control, offering a foundation for scalable adoption across diverse industrial and regulatory settings.

In this paper, we provide a structured summary of initiatives and a survey on the work done in exploitation of DT in Smart Manufacturing (SM) and its very close domains, and offer our points of view in the field.

In recent years, Smart Cities have become a focal point for integrating advanced technologies and intelligent systems to create sustainable urban environments. A key issue in these cities is reducing emissions, and smart traffic lights have emerged as an important tool in addressing this challenge. This work builds on a previously proposed system designed to reduce pollution through smart traffic lights, which was initially limited to three-way intersections and tested on a single traffic signal. We extend the system to manage various types of signalized intersections and evaluate its performance across multiple intersections in a city. Our findings show that smart traffic lights can reduce pollution even without coordination between traffic lights and vehicles.

The digital twin has emerged as an enhancing technology incorporated in several industries including the water industry, also ecological aspects such as water cycles. In this paper we propose a review of the major works done in this field, in addition, we present our proposed future works.

This paper proposes RVElastic, a Runtime Verification prototype framework for monitoring microservice systems. We present its general architecture and report a possible instantiation wherein Apache Kafka is utilised to create the system and its instrumentation, while OpenSearch is employed as a means to analyse and visualise the verification results. We experiment with RVElastic on a simple case study as a proof of concept and report the results in terms of the overhead introduced by the addition of monitors in the microservice system.

The 19thedition of the “Adaptive Computing (and Agents) for Enhanced Collaboration” (ACEC) track at WETICE 2021 targets the relevant topic of adaptation in relation to tools, methods and techniques for the enterprise. The aim of the track is to bring together researches and practitioners, working on agentbased software systems and/or adaptive computing, to present results and discuss innovative ideas. This report outlines the content of the papers accepted for presentation at the track.

Our streets will be soon populated by multitudes of autonomous (i.e., self-driving) vehicles, calling for appropriate solutions to coordinate their collective movements in order to ensure safety and efficiency. In particular, crossing intersections can be based on a number of different coordination approaches, from traditional ones (e.g., traffic lights) to innovative ones (e.g., based on dynamic negotiations between vehicles). In this paper, after having introduced the general issues associated to intersection management in the presence of autonomous vehicles, we show by simulation experiments that no single approach exhibits the best behaviour for all traffic conditions and for all performance indicators. On this basis, we introduce an adaptation mechanism that enables an intersection to dynamically select the most proper coordination approach depending on traffic conditions and the performance indicator to be optimized. Simulation experiments show the effectiveness of such adaptive approach.

In this paper, we describe our experience in using the TLA+formal language for modelling and analysis of a concrete scenario from the railway domain. A train status alert system has been specified and verified using the TLA+by formally verifying abstract specifications of the real system. The verification of TLA+models enabled us to detected ambiguity in machine transitions before implementing the real system. A translation algorithm is proposed to produce relay logic from the state-machine model in order to verify interlocking components. Our contribution helps to integrate formal methods into existing railway development process without needing to update the legacy railway computer-based systems.

Cloud manufacturing is a paradigm that represents distributed cyber-physical systems in which software abstractions and services work in strict connection to manufacturing equipment and machines. In cloud manufacturing, the cloud platform receives high-level tasks that are decomposed into subtasks, which are fulfilled by appropriate services. Despite different approaches have been proposed to compose services in order to accomplish manufacturing tasks, physical constraints have not been considered. In this paper, we propose an approach that enacts a trade-off between quality of service and physical constraints of manufacturing services in order to adapt to equipment constraints. To show the effectiveness of the proposed approach, we quantitatively compare it with a straightforward approach which does not consider any physical constraint. Experimental results of our algorithm show that the trade-off between quality of service and capacity of physical manufacturing equipment is acceptable for efficient cloud service compositions.

Digital factories represent an abstraction of real factories, which is useful to manage at a high level the processes as well as the interactions inside the factories but also the interactions between factories. This abstraction can automatize several processes and can enable to dynamically adapt the factory production to unexpected situations. Software agents can meet the requirements of digital factories by means of their features of autonomy, reactivity, proactivity and sociality. In this paper, we survey the use of software agents in the context of digital factories, showing how they can be exploited. A discussion about the advantages brought by software agents and the limitation of agent-based approaches completes the paper.

The 17th edition of the "Adaptive Computing (and Agents) for Enhanced Collaboration" (ACEC) track at WETICE 2019 focuses mostly on the area of adaptive agent-based techniques for the enterprise. The aim of the track is to bring together researches and practitioners from the fields of software agents and adaptive computing to present results and discuss innovative ideas. This report outlines the content of papers accepted for presentation at the track.

The 16th edition of the "Adaptive Computing (and Agents) for Enhanced Collaboration" (ACEC) track at WETICE 2018 focuses mostly on the area of adaptive agent-based techniques for the enterprise. The objective of the track is to bring together researches from the fields of software agents and adaptive computing to present results and discuss in the context of adaptive techniques and enterprise collaboration. This report briefly reports on the content of accepted papers.

This article describes how in addition to general purposes search engines, specialized search engines have appeared and have gained their part of the market. An enterprise search engine enables the search inside the enterprise information, mainly web pages but also other kinds of documents; the search is performed by people inside the enterprise or by customers. This article proposes an enterprise search engine called AMBIT1-SE that relies on two enhancements: first, it is user-aware in the sense that it takes into consideration the profile of the users that perform the query; second, it exploits semantic techniques to consider not only exact matches but also synonyms and related terms. It performs two main activities: (1) information processing to analyse the documents and build the user profile and (2) search and retrieval to search for information that matches user's query and profile. An experimental evaluation of the proposed approach is performed on different real websites, showing its benefits over other well-established approaches.

The 15th edition of the "Adaptive Computing (and Agents) for Enhanced Collaboration" track at WETICE 2017 focuses on the areas of adaptive agent-based services and adaptive techniques for enterprises. The purpose of the track is to bring together researches in the fields of software agents and adaptive computing as they relate to the context of adaptive techniques and enterprise collaboration. This report briefly discusses the content of the papers accepted for presentation in the track.

As autonomous cars are entering mainstream, new research directions are opening involving several domains, from hardware design to control systems, from energy efficiency to computer vision. An exciting direction of research is represented by the coordination of the different vehicles, moving the focus from the single one to a collective system. In this paper we propose some challenging examples thatshow the motivations for a coordination approach in autonomous driving. Moreover, we present some techniques borrowed from distributed artificial intelligence that can be exploited to tackle the previously mentioned challenges.

The IEEE International Conference on Self-Adapting and Self-Organizing Systems (SASO) is the main forum for studying and discussing the foundations of a principled approach to engineering systems, networks, and services based on self-adaptation and self-organization. Over the past decade, it has consolidated as the primary scientific conference for sharing ideas on algorithms, technologies, tools, and applications across a wide range of scientific fields. In 2016, the conference was hosted by the University of Augsburg, in Augsburg, Germany; its scientific program comprised full papers, short papers, poster and demo presentations, workshops, doctoral symposium and tutorials. This special issue of ACM TAAS champions some of the most solid research results of SASO 2016, presenting selected, revised, and extended best articles.

Nowadays, change is an important aspect of the world. Complexity and change in requirements and environments bring us to Autonomic Systems as a solution. Like any other kind of software system, better implementation of a system needs a proper evaluation method for system implementation. To evaluate a Self-Adaptive system, appropriate quality factors are needed for evaluation. This research tries to use non-adaptive system's evaluation methods to evaluate Self-Adaptive ones. The qualitative factors for self-adaptive systems have been extracted from a literature review (as Self-Adaptive System Qualitative Factors or for abbreviation SAQFs). Hence, there is no explicit or even implicit way for measuring most of the SAQFs, This research has tried to measure them through some measurable Qualitative Criterion. These Qualitative Criteria (we call them QCs) consist of some self-adaptive systems attributes and also some non-adaptive systems' qualitative factors. A map between these SAQFs and software system's QCs (which are more measurable) have been introduced. For each QC, sufficient metrics for measuring could be dug up based on the problem context. For better knowing about the influence of qualitative factors on each other, a prerequisite and post-requisite graph from relations among SAQFs have been introduced. This relational graph shows the importance and impact of each factors measurement on measuring the systems from self-adaptive viewpoint. For evaluating the method, we have proposed a questionnaire to experts about the model the correctness of these impacts and influences have been verified. In addition, a case study on a system in changing environment evaluated with proposed method and the applicability of the method have been reviewed.

Neuroevolution in robot controllers through objective-based genetic and evolutionary algorithms is a well-known methodology for studying the dynamics of evolution in swarms of simple robots. A robot within a swarm is able to evolve the simple neural network embedded as its controller by also taking into account how other robots are performing the task at hand. In online scenarios, this is obtained through inter-robot communications of the best performing genomes (i.e. representation of the weights of their embedded neural network). While many experiments from previous work have shown the soundness of this approach, we aim to extend this methodology using a novelty-based metric, so to be able to analyze different genome exchange strategies within a simulated swarm of robots in deceptive tasks or scenarios in which it is difficult to model a proper objective function to drive evolution. In particular, we want to study how different information sharing approaches affect the evolution. To do so we developed and tested three different ways to exchange genomes and information between robots using novelty driven evolution and we compared them using a recent variation of the mEDEA (minimal Environment-driven Distributed Evolutionary Algorithm) algorithm. As the deceptiveness and the complexity of the task increases, our proposed novelty-driven strategies display better performance in foraging scenarios.

Search engines represent one of the most exploited tools both in our everyday life and in our work.In this paper we propose a user-aware semantic enterprise search engine called AMBIT-SE.It is "enterprise" in the sense that it is focused on the search in enterprise websites; the "semantic" aspect is related to the fact that it exploits not an exact word match, but relies also on the meaning of the words by means of synonyms and related terms; finally, to produce query results it takes into account also the user information, which turns out to be very useful to improve the search.We explain how our system works and report the results of experiments on different websites. 98

This report describes the culmination of the 14th installment of the "Adaptive Computing (and Agents) for Enhanced Collaboration" (ACEC) track. ACEC focuses on collaborative computing that occurs dynamically among web-accessible software systems and devices. The articulation of these network artifacts must be controlled by adaptive sometimes intelligent control mechanisms, sometimes realizes as agents. ACEC have for over 14 years brought together researchers from multiple disciplines to address the challenges associated with effective, just-in-time collaboration. The track this year showcases perspective papers that address adaptive approaches for socio-technical systems, in different contexts such as smart spaces and crowd sourcing.

The widespread availability of mobile devices equipped with sensors on board is strongly pushing towards the development of smart spaces -- offices, streets, hospital, airports, homes and shops equipped with sensing systems to help people find relevant information quickly and use services comfortably, often in a collaborative way. A common aspect in these scenarios is that the technology complexity is amplified by the organisational and procedural complexity of the application domain: this is in fact typical of socio-technical systems -- kinds of systems that inherently need to be conceived, designed and developed taking into account both the technological and the human/organisational aspects from the earliest stages. In this paper, we select and discuss some challenges in the definition and development of adaptive socio-technical systems for smart spaces, the selected challenges aim at focusing in particular on (i) a socio-technical model and process, (ii) a multi-level integration framework, and (iii) methods and system analysis techniques for runtime adaptation. In fact, designing such systems requires the suitable combination of enabling technologies into an operational framework, integrating and coordinating a multiplicity of processes managed by complex organisations, each made up of independent and autonomous units.

The large number of available services, provided by different means such as the Web, smartphone apps, and wearable devices, provides users a valuable support for their everyday activities, but at the same time introduces the need for a tailored choice and exploitation of them. Several approaches have been proposed that take into account users' preferences, but a comprehensive user-aware approach is still missing. In a previous work we proposed an approach that addressed the user-aware composition of services, in this paper we propose to extend the previous approach by considering also semantic techniques and simple collaboration aspects. So, we propose the definition of a middleware for composing and exploiting services that exhibits some key features: (i) it considers the profile of the users that exploit the service to choose appropriate services for them, (ii) it exploits techniques of semantic similarity between user and service descriptions to make the choice more effective, and (iii) it enables the collaboration among users. By means of a case study we present a possible scenario that can take advantage of our middleware, and we show how it can be exploited.

Distributed autonomous systems consisting of large numbers of components with no central control point need to be able to dynamically adapt their control mechanisms to deal with an unpredictable and changing environment. Existing frameworks for engineering self-adaptive systems fail to account for the need to incorporate self-expression—that is, the capability of a system to dynamically adapt its coordination pattern during runtime. Although the benefits of incorporating self-expression are well known, currently there is no principled means of enabling this during system design. We propose a conceptual framework for principled design of systems that exhibit self-expression, based on inspiration from the natural immune system. The framework is described as a set of design principles and customizable algorithms and then is instantiated in three case studies, including two from robotics and one from artificial chemistry. We show that it enables self-expression in each case, resulting in systems that are able to adapt their choice of coordination pattern during runtime to optimize functional and nonfunctional goals, as well as to discover novel patterns and architectures.

This report describes the culmination of the 13th installment of the “Adaptive Computing (and Agents) for Enhanced Collaboration” (ACEC) track. ACEC focuses on collaborative computing that occurs dynamically among Web-accessible software systems and devices. The articulation of these network artifacts must be controlled by adaptive, intelligent control mechanisms, sometimes realized as agents. ACEC has for over 13 years brought together researchers from multiple disciplines to address the challenges associated with effective, just-in-time collaboration. The track this year showcases papers that explore emerging models and design patterns for adaptive architectures for a varied set of domains such as robotic and crowdsourcing.

Summary Multiagent systems are commonly used for simulation of new paradigms of energy distribution. Especially when considering Smart Grids, the autonomicity deployed by goal‐driven agents implies the need for being aware of multiple aspects connected to the energy distribution context. With ‘context’, we refer to the outside world variables such as weather, stock market trends, location of the users, government actions, and so on; therefore, an architecture highly context‐aware is needed. We propose a model in which every important factor concerning the electric energy distribution is presented by modeling context‐aware agents able to identify the impact of these factors. Moreover, some tests have been performed regarding the web service integration in which agents contracting energy will automatically retrieve data to be used in adaptive and collaborative aspects; an explicative example is represented by the retrieval of weather forecasting that provides input on ongoing demand and data for the predicted availability (in case of photovoltaic or wind powered environments). Copyright © 2013 John Wiley & Sons, Ltd.

We propose a novel method for evolving adaptive locomotive strategies for virtual limbless creatures that addresses both functional and non-functional requirements, respectively the ability to avoid obstacles and to minimise spent energy. We describe an approach inspired by artificial immune systems, based on a dual-layer idiotypic network that results in a completely decentralised controller. Starting from a system initialised with five non-adaptive locomotion strategies, we show that an adaptive controller can evolve that both min- imises energy requirements and maximises distance covered when compared to the initial strategies.

Self-adaptive systems are distributed computing systems composed of different components that can adapt their behavior to different kinds of conditions. This adaptation does not concern the single components only, but the entire system. In a previous work we have identified several patterns for self-adaptation, classifying them by means of a taxonomy, which aims at being a support for developers of self-adaptive systems. Starting from that theoretical work, we have simulated the described self-adaptation patterns, in order to better understand the concrete and real features of each pattern. The contribution of this paper is to report about the simulation work, detailing how it was carried out, and to present a “table of applicability” that complete the initial taxonomy of patterns and provides a further support for the developers.

Given the complexity of modelling actors and interactions of the deregulated electric energy market, the Multi-Agent System approach can be used for both simulation and applications of critical aspects in the Smart Grid. In particular, balancing demand and offer and handling negotiation among peers: now, even a domestic environment that features photovoltaic and/or wind turbines modules can decide to enter the deregulated market as a small-scale seller, thus making the requirement of having such an architecture to be autonomous by deploying Self-* properties such as Self-Organization, Self-Repairing, Self-Adaptation. To be more specific about the presented case study, we propose a model in which small-scale seller agents dynamically decide from to time to time, either to address the market as lone operators or by aggregating into Virtual Power Plants. This iterated decisional process depends on highly variable market related factors, thus the goal to design a net of agents able to autonomously react to this dynamic environment.

Private houses are more and more enabled with devices that can produce renewable energy, and the not so remote chance of selling the surplus energy makes them new players in the energy market. This market is likely to become deregulated since each energy home-producer can negotiate the energy price with consumers, typically by means of an auction; on the other hand, consumers can always rely on energy companies, even if their energy is more expensive. This scenario could lead to advantages for users, but it is certainly complex and dynamic, and needs an appropriate management. To this purpose, in this paper we propose an agent-based application to deal with the negotiation among different parties producing and consuming energy. Software agents, thanks to their autonomy in taking decisions, well suit the requirements of the proposed scenario. For our application, we adopt a strategy derived from game theory, in order to optimize energy production and supply costs by means of negotiation and learning. The effectiveness of our approach is proved by simulation results of a situation involving energy buyers, energy producers using renewable micro-generation facilities and large-scale traditional electricity companies.

This article summarizes five relevant methods for developing self-organizing multiagent systems. It identifies their most relevant aspects and provides a description of each one under the form of method fragments expressed using SPEM (Software and System Process Engineering Metamodel). The use of a “metamodel” to describe fragments facilitates the comparison of the methods and their respective fragments. These fragments can be combined and be part of a more general ad hoc methodology, created according to the needs of the designer. Self-organizing traffic lights controllers and self-organizing displays are chosen as case studies to illustrate the methods and to underline which fragments are important for self-organizing systems. Finally, we illustrate how to augment PASSI2, an agent-based methodology which does not consider self-organization aspects, with some of the identified fragments for self-organization.

Service Oriented Computing (SOC) and Agent Oriented Programming (AOP) are two promising paradigms exploited in the development of distributed systems. In fact, even if starting from different conceptual abstractions, both paradigms try to face unpred

Todaypsilas complex systems can be addressed by exploiting software agents, and in particular sets of interacting agents. Agent societies represent a powerful means to design organization-oriented agent-based systems, in particular those where agents belonging to different users meet and interact. To effectively exploit them, developers must be supported by appropriate methodologies. In this paper we present a survey of agent-based methodologies, evaluated under some criteria that, in our opinion, are useful for developing societies. Our aim is to help developers in understanding the society-related features and choosing the most appropriate to their needs.

Agents are an emerging technology that grants programmers a new way to exploit distributed resources. One of the hardest difficult in the development of this kind of application is the managing of agent interactions, since agents must interact in a collaborative and/or competitive way to achieve their task. Roles are a powerful paradigm that can be used to model agent interactions, both between two (or more) agents and between an agent and the platform/node on which it is running. In this paper we propose a guideline to role developing and use, with regard to the agent technology. In particular we focus on the main problems that a role system and a role developer must take into account.

One of the main issues in mobile services' research (M-service) is supporting M-service availability, regardless of the user's context (physical location, device employed, etc.). However, most scenarios also require the enforcement of context-awareness, to dynamically adapt M-services depending on the context in which they are requested. In this paper, we focus on the problem of adapting M-services depending on the users' location, whether physical (in space) or logical (within a specific distributed group/application). To this end, we propose a framework to model users' location via a multiplicity of local and active service contexts. First, service contexts represent the mean to access to M-services available within a physical locality. This leads to an intrinsic dependency of M-service on the users' physical location. Second, the execution of service contexts can be tuned depending on who is requesting what M-service. This enables adapting M-services to the logical location of users (e.g., a request can lead to different executions for users belonging to different groups/applications). The paper firstly describes the framework in general terms, showing how it can facilitate the design of distributed applications involving mobile users as well as mobile agents. Then, it shows how the MARS coordination middleware, implementing service contexts in terms of programmable tuple spaces, can be used to develop and deploy applications and M-services coherently with the above framework. A case study is introduced and discussed through the paper to clarify our approach and to show its effectiveness.

Information overloading introduced by the large amount of data that is spread over the Internet must be faced in an appropriate way. The dynamism and the uncertainty of the Internet, along with the heterogeneity of the sources of information are the two main challenges for today's technologies related to information management. In the area of information integration, this paper proposes an approach based on mobile software agents integrated in the MOMIS (Mediator envirOnment for Multiple Information Sources) infrastructure, which enables semi-automatic information integration to deal with the integration and query of multiple, heterogeneous information sources (relational, object, XML and semi-structured sources). The exploitation of mobile agents in MOMIS can significantly increase the flexibility of the system. In fact, their characteristics of autonomy and adaptability well suit the distributed and open environments, such as the Internet. The aim of this paper is to show the advantages of the introduction in the MOMIS infrastructure of intelligent and mobile software agents for the autonomous management and coordination of integration and query processing over heterogeneous data sources.

The design and development of Internet applications requiring dynamic and possibly mobile access to Internet resources can take advantage of an approach based on autonomous mobile agents. However, mobility introduces peculiar issues related to the modeling and management of the agents' coordination activities. This paper introduces context-dependent coordination as a framework for the design and development of Internet applications based on mobile agents, and shows how it can be supported by a proper coordination infrastructure. Context-dependent coordination is centered on the notion of programmable coordination media, as the software abstraction via which an agent in an Internet site can access to local resources and coordinate with local agents. Programmability stems from the fact that the behavior of the media can be fully configured to influence agents' coordination activities. This enables local administrators to configure coordination media so as to enact site-dependent coordination policies, and mobile agents to configure the accessed coordination media to obtain an application-dependent behavior of the media themselves. Several application examples shows that exploiting context-dependent coordination promotes a clear separation of concerns in design and development, and can make applications more modular and easier to be maintained. The MARS system is assumed as an exemplar coordination infrastructure to clarify the concepts expressed and to show their actual implementation.

Mobility introduces peculiar coordination problems in agent-based Internet applications. First, it suggests the exploitation of an infrastructure based on a multiplicity of local interaction spaces. Second, it may require coordination activities to be adapted both to the characteristics of the execution environment where they occur and to the needs of the application to which the coordinating agents belong. This paper introduces the concept of context-dependent coordination based on programmable interaction spaces. On the one hand, interaction spaces associated to different execution environments may be independently programmed so as to lead to differentiated, environment-dependent, behaviors. On the other hand, agents can program the interaction spaces of the visited execution environments to obtain an application-dependent behavior of the interaction spaces themselves. Several examples show how an infrastructure for context-dependent coordination can be exploited to simplify the design of Internet applications based on mobile agents. In addition, the MARS coordination infrastructure is presented as an example of a system in which the concept of context-dependent coordination has found a clean and efficient implementation.

Owing to its widespread diffusion and ease of use, the Web is an attractive platform to support distributed cooperative work. The paper presents an object-oriented proxy-based framework to support synchronous cooperation on the Web. The framework implements the general-purpose mechanisms of a proxy server. Specific application-dependent functionalities have to be implemented in a module to be installed in the proxy-framework. This leads to the implementation of an extensible proxy-framework, which facilitates the development of specific cooperative services. The paper presents and evaluates several applications enabling different cooperative Web activities, to confirm the effectiveness of our proposal in terms of reusability, usability and performance. Copyright © 1999 John Wiley & Sons, Ltd.

The paper introduces a taxonomy of the possible coordination models for mobile agent applications and evaluates their impact in the design of a widely distributed application in the area of information retrieval. The analysis outlines the advantages of uncoupled coordination models and points out the need for coordination models based on reactive blackboards.