Prototyping Futures gives you a glimpse of what collaborating with academia might look like. Medea and its co-partners share their stories about activities happening at the research centre – projects, methods, tools, and approaches – what challenges lie ahead, and how these can be tackled. Examples of highlighted topics include: What is a living lab and how does it work? What are the visions behind the Connectivity Lab at Medea? And, how can prototyping-methods be used when sketching scenarios for sustainable futures? Other topics are: What is the role of the body when designing technology? What is collaborative media and how can this concept help us understand contemporary media practices? Prototyping Futures also discusses the open-hardware platform Arduino, and the concepts of open data and the Internet of Things, raising questions on how digital media and connected devices can contribute to more sustainable lifestyles, and a better world.

District heating systems (DHS) is in many countries an important part of the heating infrastructure, especially in and around urban areas. Combined heat and power (CHP) production makes it possible to producer heat while simultaneously producing power. This combination help maximize the energy efficiency in production, often reaching an 80-90% utilization level of the primary fuel, compared to around 30-50% in a traditional power plant. The heat produced in the CHP plant is used to heat the adjacent DHS, while the power is transferred and sold on the power market. The work presented in this paper relates to the Nord Pool Spot power market, which is the leading power market in Europe and one of the largest in the world. On Nord Pool Spot power is bought and sold based on hourly spot prices, facilitated by the primary day-ahead market and the supplementary balancing intraday market. Since it isn’t possible to separate the physical process of producing heat and power in a CHP production facility, the energy company will want to synchronize high heat load production with high spot prices for power whenever possible. This can be done by using large storage tanks where heat is buffered during hours with high spot prices, while then distributed to the DHS as the heat load demand increases. However, such storage tanks are expensive to build and maintain, and they have limited operational dynamics. An alternative is to use the actual buildings connected to the DHS, in order to utilize their thermal inertia by the use of active load control. This paper presents a multi-agent system (MAS) designed to bridge the information gap between energy companies and building owners in order to enable the use of system-wide active load control in order to synchronization heat load and spot prices. The presented scheme provides a self-regulating market analogy in which agents act to allocate load control resources. Each participating building is assigned a consumer agent, while each production unit is represented by a production agent. These agents interact on the market analogy which is in turn supervised by a market agent. The work in this paper is focused on the intraday market although the underpinning synchronization scheme is suitable for the day-ahead market as well as the intraday market. The results show considerable gains for participating entities when applying the presented strategy to the often volatile intraday spot price market.

We present the Transportation And Production Agent-based Simulator (TAPAS), which is an agent-based model for simulation of transport chains that can be used, e.g., for analysis of transport-related policy and infrastructure measures. TAPAS is more powerful than traditional approaches to freight transport analysis, as it explicitly models production and customer demand, and it captures the interaction between individual transport chain actors, their heterogeneity and decision making processes, as well as time aspects. Whereas traditional approaches rely on assumed statistical correlation, TAPAS relies on causality, i.e., the focus is on the decisions and negotiations that lead to activities. TAPAS is composed of two connected layers, one that simulates the physical activities, e.g., production and transportation, and one that simulates the decision making and interaction between actors. We illustrate TAPAS with a scenario in which the consequences of three transport policy and infrastructure measures are studied.

The purpose of this paper is to present a framework for intelligent goods and illustrate how it can be applied when modeling intelligent goods as agents. It includes a specification of different levels of capability connected to the goods, which is based on a number of capability dimensions. Additionally, three specific intelligent goods services related to transport are presented. We show how these services can be modeled as agents and how they relate to the intelligent goods framework. A discussion of different physical locations of service information and processing is also included.

Purpose of this paper: This paper presents a method for identifying possible architectural solutions for potential intelligent goods services. The solutions range from implementing all service intelligence at the goods level to requiring no goods intelligence at all. The paper also shows how different solutions, identified by the method, can be compared and evaluated through quality analysis in order to determine when intelligent goods are beneficial. Design/methodology/approach: Literature and case studies, and theoretical analyses form the foundations of the paper. The method is based on a general framework for describing intelligent goods systems, which involves several levels of intelligence related to both the goods and the local entities surrounding the goods. Three specific services are used to illustrate the method. The quality analysis is based on the Analytic Hierarchy Process (AHP) and a number of proposed quality attributes. Findings: The paper illustrates how the framework can be used to identify relevant architectural solutions for a particular service, with different levels of intelligence on the goods. It also shows how to qualitatively analyze individual solutions and suggests an extension to the general framework for describing intelligent goods systems. Research limitations/implications: The paper does not contain measured data to base the quality analysis on. Testing the method and quality analysis on implemented cases is recommended for future work. What is original/value of paper: The novel method and approach for identifying and evaluating architectural solutions for potential intelligent goods services, are useful in general management when investigating the value of implementing intelligent goods. The paper also contributes to the description of intelligent goods systems.

The amount of software that hosts spyware has increased dramatically. To avoid legal repercussions, the vendors need to inform users about inclusion of spyware via end user license agreements (EULAs) during the installation of an application. However, this information is intentionally written in a way that is hard for users to comprehend. We investigate how to automatically discriminate between legitimate software and spyware associated software by mining EULAs. For this purpose, we compile a data set consisting of 996 EULAs out of which 9.6% are associated to spyware. We compare the performance of 17 learning algorithms with that of a baseline algorithm on two data sets based on a bag-of-words and a meta data model. The majority of learning algorithms significantly outperform the baseline regardless of which data representation is used. However, a non-parametric test indicates that bag-of-words is more suitable than the meta model. Our conclusion is that automatic EULA classification can be applied to assist users in making informed decisions about whether to install an application without having read the EULA. We therefore outline the design of a spyware prevention tool and suggest how to select suitable learning algorithms for the tool by using a multi-criteria evaluation approach.

A novel approach to efficiently plan and execute effective transport solutions is presented. It provides agent-based support for key tasks, such as, finding the best sequence of transport services for a particular goods transport, monitoring the execution of the transport, as well as the interaction between the involved actors. The approach is based on the FREIGHTWISE framework in which a minimal set of information packages is defined. The purpose is to capture all the information that needs to be communicated between the actors involved in a transport, such as, transport users, transport providers, and infrastructure managers, during the complete process from planning to termination. The approach is inspired by the concepts of virtual enterprises and breeding environments. We analyse the requirements of such an approach and describe a multi-agent system architecture meeting these requirements.

This article uses analytical methods to assess reductions in total costs of telematic systems that can result from common infrastructure utilization. Analytical methods based on clustering and K-minimum spanning tree can be adopted for finding clusters or sets which maximize reductions in total system costs due to infrastructure sharing between telematic systems. Efficient integration of telematic systems through infrastructure sharing can positively influence telematic service interoperability while reducing costs. Results show the measure of synergy for each K-value, as well as total cost savings of up to 2%.

To predict which supply chain effects will appear when applying governmental control policies, infrastructure investments, and business strategies, multi-agent-based simulation (MABS) can be used. In this paper, we identify abstract supply chain responsibilities, roles and interactions that are argued to be sufficient for representing all types of organizations involved in the processes of buying and selling products and transport services. The identified responsibilities, roles and interactions are organized into a framework together with a set of modeling guidelines, which we relate to the GAIA methodology to simplify the process of developing multi-agent-based supply chain simulation models. To illustrate the usage of the framework, we provide two case studies where we apply it to two different MABS models.

We present one of the first actual applications of Multi Agent-Based Simulation (MABS) to the field of software process simulation modelling (SPSM). Although there are some recent attempts to do this, we argue that these fail to take full advantage of the agency paradigm. Our model of the software development process integrates individual-level performance, cognition and artefact quality models in a common simulation framework. In addition, this framework allows the implementation of both MABS and System Dynamics (SD) simulators using the same basic models. As SD is the dominating approach within SPSM, we are able to make relevant and unique comparisons between it and MABS. This enabled us to uncover quite interesting properties of these approaches, e.g., that MABS reflects the problem domain more realistically than SD.

A novel application of agent-mediated electronic commerce is presented. It concerns developing and maintaining efficient and effective transport solutions. The suggested approach is inspired by the concepts of virtual enterprises and breeding environments, as well as peer-to-peer technology. We discuss the requirements of such an approach and outline a software architecture meeting these requirements.