Recommendation system; Food allergy; Multi-agent systemThe automatic recipe recommendation which take into account the dietary restrictions of users (such as allergies or intolerances) is a complex and open problem. Some of the limitations of the problem is the lack of food databases correctly labeled with its potential allergens and non-unification of this information by companies in the food sector. In the absence of an appropriate solution, people affected by food restrictions cannot use recommender systems, because this recommend them inappropriate recipes. In order to resolve this situation, in this article we propose a solution based on a collaborative multi-agent system, using negotiation and machine learning techniques, is able to detect and label potential allergens in recipes. The proposed system is being employed in receteame.com, a recipe recommendation system which includes persuasive technologies, which are interactive technologies aimed at changing users' attitudes or behaviors through persuasion and social influence, and social information to improve the recommendations. MotivationWhen the doctor diagnoses a patient with an allergy, the set of substances that produce extreme sensitivity in the patient's immune system should be avoided. In the case of food allergies, the difficulty is greater because avoiding a food is not an easy task when many of them are composed by others (e.g. mayonnaise, is composed of oil, egg, vinegar, etc.). This is an awkward situation of high impact because it involves nutrition, a necessary and daily task in the lives of people activity. This increasingly affects to more individuals in our society (up to 8% in children and 2% in adults) 1 . Moreover, the problem of food allergies is not resolved by simply avoiding certain foods, because the lack of nutrients they provide must be compensated with other foods.People affected by food allergies are forced to become expert nutritionists to maintain a healthy life, free from allergens that they cannot tolerate. Currently, Internet is the most popular way of obtaining information about allergies. On the Internet, for instance, the World Allergy Organization (WAO) regulates and offers the terminology used to characterize allergies information. However, the information is difficult to understand because of its complexity and quantity. Thus, traditional search and navigation activities are being combined or even replaced by direct interactions between users in the form of recommendations, advice and warnings; 2 out of 3 take into account the recommendations of other users to make decisions (about products, treatments, entertainment, etc.); and of these, 69% gives a lot or some credibility to what their friends or acquaintances say on social networks. 1 WAO World Allergy Organization, Food allergy statistics: http://www.worldallergy.org/public/allergic_diseases_center/foodallergy/

Abstract-Open societies are situated in dynamic environments and are formed by heterogeneous autonomous agents. In order to ensure social order, norms have been employed as coordination mechanisms. However, the dynamical features of open systems may cause that norms loose their validity and need to be adapted. Therefore, this paper proposes a new dialogue game protocol for modelling the interactions produced between agents that must reach an agreement on the use of norms. An application example has been presented for showing both the performance of the protocol and its usefulness as a mechanism for managing the solving process of a coordination problem through norms.

Abstract-In real-time Multi-Agent Systems, Real-Time Agents merge intelligent deliberative techniques with real-time reactive actions in a distributed environment. CBR has been successfully applied in Multi-Agent Systems as deliberative mechanism for agents. However, in the case of Real-Time Multi-Agent Systems the temporal restrictions of their Real-Time Agents make their deliberation process to be temporally bounded. Therefore, this paper presents a guide to temporally bound the CBR to adapt it to be used as deliberative mechanism for Real-Time Agents.

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.