Abstract One of the greatest challenges of computational argumentation research consists of creating persuasive strategies that can effectively influence the behaviour of a human user. From the human perspective, argumentation represents one of the most effective ways to reason and to persuade other parties. Furthermore, it is very common that humans adapt their discourse depending on the audience in order to be more persuasive. Thus, it is of utmost importance to take into account user modelling features for personalising the interactions with human users. Through computational argumentation, we can not only devise the optimal solution, but also provide the rationale for it. However, synergies between computational argumentative reasoning and computational persuasion have not been researched in depth. In this paper, we propose a new formal framework aimed at improving the persuasiveness of arguments resulting from the computational argumentative reasoning process. For that purpose, our approach relies on an underlying abstract argumentation framework to implement this reasoning and extends it with persuasive features. Thus, we combine a set of user modelling and linguistic features through the use of a persuasive function in order to instantiate abstract arguments following a user-specific persuasive policy. From the results observed in our experiments, we can conclude that the framework proposed in this work improves the persuasiveness of argument-based computational systems. Furthermore, we have also been able to determine that human users place a high level of trust in decision support systems when they are persuaded using arguments and when the reasons behind the suggestion to modify their behaviour are provided.

The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks.Such is the case of the automatic evaluation of complete professional argumentative debates.In this paper, we propose an original hybrid method to automatically predict the winning stance in this kind of debates.For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks.Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.

Argumentation schemes are generalised patterns that provide a way to (partially) dissociate the content from the reasoning structure of the argument. On the other hand, Cialdini’s principles of persuasion provide a generic model to analyse the persuasive properties of human interaction (e.g., natural language). Establishing the relationship between principles of persuasion and argumentation schemes can contribute to the improvement of the argument-based human-computer interaction paradigm. In this work, we perform a qualitative analysis of the persuasive properties of argumentation schemes. For that purpose, we present a new study conducted on a population of over one hundred participants, where twelve different argumentation schemes are instanced into four different topics of discussion considering both stances (i.e., in favour and against). Participants are asked to relate these argumentation schemes with the perceived Cialdini’s principles of persuasion. From the results of our study, it is possible to conclude that some of the most commonly used patterns of reasoning in human communication have an underlying persuasive focus, regardless of how they are instanced in natural language argumentation (i.e., their stance, the domain, or their content).

Social networking services (SNSs) such as Facebook and Twitter have experienced explosive growth during the few past years. Millions of users have created their profiles on these services because they experience great benefits in terms of friendship. SNSs can help people to maintain their friendships, organize their social lives, start new friendships, and meet others who share their hobbies and interests. However, all these benefits can be eclipsed by the privacy hazards that affect people in SNSs. People expose intimate information about their lives on SNSs, and this information affects the way others think about them. It is crucial that users be able to control how their information is distributed through the SNSs and decide who can access it. This article presents a list of privacy threats that can affect SNS users and what requirements privacy mechanisms should fulfill to prevent this threats. Then the article reviews current approaches and analyzes to what extent they cover the requirements.

The next generation of smart grid technologies demands intelligent capabilities for communication, interaction, monitoring, storage, and energy transmission. Multi-agent systems are envisioned to provide autonomic and adaptability features to these systems in order to gain advantage in their current environments. In this article we present a mechanism for providing distributed energy storage systems (DESSs) with intelligent capabilities. In more detail, we propose a self-configurable mechanism that allows a DESS to adapt itself according to the future environmental requirements. This mechanism is aimed at reducing the costs at which energy is purchased from the market.

Organization adaptation requires determining the consequences of applying changes not only in terms of the benefits provided but also measuring the adaptation costs as well as the impact that these changes have on all of the components of the organization. In this paper, we provide an approach for adaptation in multiagent systems based on a multidimensional transition deliberation mechanism (MTDM). This approach considers transitions in multiple dimensions and is aimed at obtaining the adaptation with the highest potential for improvement in utility based on the costs of adaptation. The approach provides an accurate measurement of the impact of the adaptation since it determines the organization that is to be transitioned to as well as the changes required to carry out this transition. We show an example of adaptation in a service provider network environment in order to demonstrate that the measurement of the adaptation consequences taken by the MTDM improves the organization performance more than the other approaches.

In hard real-time systems, gain time is defined as the difference between the Worst Case Execution Time (WCET) of a hard task and its actual processor consumption at runtime. This paper presents the results of an empirical study about how the presence of a significant amount of gain time in a hard real-time system questions the advantages of using the most representative scheduling algorithms or policies for aperiodic or soft tasks in fixed-priority preemptive systems. The work presented here refines and complements many other studies in this research area in which such policies have been introduced and compared. This work has been performed by using the authors' testing framework for soft scheduling policies, which produces actual, synthetic, randomly generated applications, executes them in an instrumented Real-Time Operating System (RTOS), and finally processes this information to obtain several statistical outcomes. The results show that, in general, the presence of a significant amount of gain time reduces the performance benefit of the scheduling policies under study when compared to serving the soft tasks in background, which is considered the theoretical worst case. In some cases, this performance benefit is so small that the use of a specific scheduling policy for soft tasks is questionable.

In this article, an agent-based negotiation model for negotiation teams that negotiate a deal with an opponent is presented. Agent-based negotiation teams are groups of agents that join together as a single negotiation party because they share an interest that is related to the negotiation process. The model relies on a trusted mediator that coordinates and helps team members in the decisions that they have to take during the negotiation process: which offer is sent to the opponent, and whether the offers received from the opponent are accepted. The main strength of the proposed negotiation model is the fact that it guarantees unanimity within team decisions since decisions report a utility to team members that is greater than or equal to their aspiration levels at each negotiation round. This work analyzes how unanimous decisions are taken within the team and the robustness of the model against different types of manipulations. An empirical evaluation is also performed to study the impact of the different parameters of the model.

Agent-based negotiation teams are negotiation parties formed by more than a single individual. Individuals unite as a single negotiation party because they share a common goal that is related to a negotiation with one or several opponents. My research goal is providing agent-based computational models for negotiation teams in multi-agent systems.

Abstract Security is becoming a major concern in multiagent systems, since an agent's incorrect or inappropriate behaviour may cause non‐desired effects, such as money and data loss. Some multiagent platforms (MAP) are now providing baseline security features, such as authentication, authorization, integrity and confidentiality. However, they fail to support other features related to the sociability skills of agents such as agent groups. What is more, none of the listed MAPs provide a mechanism for preserving the privacy of the users (regarding their identities) that run their agents on such MAPs. In this paper, we present the security infrastructure (SI) of the Magentix MAP, which supports agent groups and preserves user identity privacy. The SI is based on identities that are assigned to all the different entities found in Magentix (users, agents and agent groups). We also provide an evaluation of the SI describing an example application built on top of Magentix and a performance evaluation of it. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract In embedded systems, the typical host/target development scheme normally requires a tracing mechanism to be available in the target system in order to collect run‐time information of the application. This information is then used in the host system to debug, monitor, fine tune, etc., the application. Related to this, POSIX has recently incorporated the definition of standard tracing services into the complete POSIX 1003.1‐2001 standard. It is obvious that standard interfaces present several advantages for applications, specially regarding portability. However, the observed trend in real‐time operating systems (RTOSs) of progressively incorporating the POSIX interfaces have not reached the POSIX tracing services. As far as we know, this tracing interface has not been incorporated yet into any commercial RTOS, probably because it is quite recent and its tracing model is too complex and demanding for small, embedded real‐time systems. After carefully analyzing and then implementing and using the POSIX Trace standard in both RT‐Linux and MarteOS, this paper presents the redefinition of the POSIX tracing standard from the perspective of small real‐time systems and, in particular, systems conforming to the POSIX Minimal Realtime System Profile (MRSP). The new tracing model is calledLightweight POSIX tracing. The model has been carefully designed to maintain all the syntax, and almost all the semantics, of the original standard, but it allows for the definition of useful subsets of the original standard that can be implemented very efficiently. The experimental results show that a minimal tracing system which is appropriate for MRSP systems exhibits a significantly smaller memory footprint and lower overhead, compared to the implementation of the complete standard. Copyright © 2007 John Wiley & Sons, Ltd.

As real-time applications become more complex, the availability of event tracing mechanisms becomes more important in order to perform run-time monitoring. The recent approved standard POSIX 1003.Iq defines a common application interface for trace management. In this paper we present our experience with the implementation of a subset of the approved tracing standard that conforms to the POSIX Minimal Real-Time System Profile. The implementation has been done in the RT-Linux operating system, in order to obtain a real-time kernel with standard tracing services that conforms to the Minimal ProJile.

The paper addresses the problem of jointly scheduling tasks with both hard and soft real time constraints. We present a new analysis applicable to systems scheduled using a priority preemptive dispatcher, with priorities assigned dynamically according to the EDF policy. Further, we present a new efficient online algorithm (the acceptor algorithm) for servicing aperiodic work load. The acceptor transforms a soft aperiodic task into a hard one by assigning a deadline. Once transformed, aperiodic tasks are handled in exactly the same way as periodic tasks with hard deadlines. The proposed algorithm is shown to be optimal in terms of providing the shortest aperiodic response time among fixed and dynamic priority schedulers. It always guarantees the proper execution of periodic hard tasks. The approach is composed of two parts: an offline analysis and a run time scheduler. The offline algorithm runs in pseudopolynomial time O(mn), where n is the number of hard periodic tasks and m is the hyperperiod/min deadline.