ACoPla: a Multiagent Simulator to Study Individual Strategies in Dynamic Situations

One important issue in multi-agent systems is how to define agents’ interaction strategies in dynamic open environments. Generally, agents’ behaviors, such as being cooperative/altruistic or competitive/adversarial, are defined a priori by their creators. However, this is a weak premise when considering interaction among anonymous self-interested agents. Whenever agents meet, there is always a decision to be made: what is the best group interaction strategy? We argue that the answer depends on the amount of information required to make a decision and on the deadline proximity for accomplishing the task in hand. In certain situations, it is to the agents’ advantage to exchange information with others, while in other situations there are no incentives for them to spend time doing so. Understanding effective behaviors according to the decision- making scenario is still an open issue in multi-agent systems. In this paper, we present a multi-agent simulator (ACoPla) to understand the correlations between agents’ interaction strategy, decision-making context and successful task accomplishment rate. Additionally, we develop a case study in the domain of site evacuation to exemplify our findings. Through this study, we detect the types of conditions under which cooperation becomes the preferred strategy, as the environment changes

Applying collaborative filtering to reputation domain: a model for more precise reputation estimates in case of changing behavior by rated participants

Automated Collaborative Filtering (CF) techniques have been successfully applied on Recommendation domains. Dellarocas [1] proposes their use on reputation domains to provide more reliable and personalized reputation estimates. Despite being solved by recommendation field researches (e.g. significance weighting [2]), the problem of selecting low-trusted neighborhoods finds new roots in the reputation domain, mostly related to different behavior by the evaluated participants. It can turn evaluators with similar tastes into distant ones, contributing to poor reputation rates. A Reputation Model is proposed to minimize those problems. It uses CF techniques adjusted with the following improvements: 1) information of evaluators taste profiles is added to the user evaluation history; 2) transformations are applied on user evaluation history based on the similarities between the taste profiles of the active user and of the other evaluators to identify more reliable neighborhoods. An experiment is implemented through a simulated electronic marketplace where buyers choose sellers based on reputation estimates generated by the proposed reputation model and by a model that uses traditional CF. The goal is to compare the proposed model performance with the traditional one through comparative analysis of the data that is created. The results are explained at the end of the paper.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1Red de Universidades con Carreras en Informática (RedUNCI

An Analysis of Constructed Categories for Textual Classification Using Fuzzy Similarity and Agglomerative Hierarchical Methods

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A Roadmap for AI in Latin America

International audienceIf we want ensure that AI in the upcoming years is a positive factor of the development of Latin America we need to start acting now and stop doing the same thing over and over again. The recent past and the current context in the region clearly indicates that it is unlikely that we see any improvements in the resources and support that AI has, instead, it will probably be aggravated by the impact of the COVID-19 pandemic. Consequently, it is our role as researchers to visit this issue and attempt to propose a road map towards a solution.The driving motivation for this paper is to plant the seeds of a discussion on how to create a bottom-up and inclusive positive momentum for AI in the region, given the existing conditions, while, at the same time, reducing the potential negative impacts that it might have. We present this in the form of a roadmap or workflow that identifies the main obstacles that should be addressed and how they can be overcome by a combination focusing the work AI practitioners on particular research topics and that of decision markers and concern citizens

Should I stay or should I go? Managing Brazilian WhatsApp groups

Instant messaging (IM) technology enables individuals to connect and maintain relationships with friends, family and colleagues, keeping participants updated on subjects of interest. It has rapidly become widespread in many countries and even been used for political activism. IM enables rapid, informal interaction between participants, but can generate message overload and notification fatigue, which leads to the adoption of different strategies to handle this problem. In this paper we report on an empirical study focused on the management of IM groups: reasons for joining or leaving, and the strategies adopted to manage the information flow. We distributed a survey that was answered by 442 WhatsApp users in Brazil. Answers help us understand the ways in which participants cope with message overload

Building a Model for Augmented Design Documentation

. Project-specific knowledge is the rationale behind the project data and specifications, including the design decisions that link elements of basic data, design data, project-specifications, domain knowledge, and general knowledge to explain the design. This information should be available in design documentation, but usually it is missing. The paper describes an approach for improving design documentation in which the computer acts as an apprentice to the designer to capture the rationale during the design process. The initial focus of the work is on HVAC (Heating, Ventilation, and Air Conditioning) design. We are using videotape analysis of design sessions along with structured interviews to develop a model of design rationale in this domain. INTRODUCTION The life cycle of the civil engineering facilities can be measured in decades, a long period during which the facility may undergo substantial changes. Moreover, most of the facilities are highly complex and require substantial t..

High-speed idea filtering with the bag of lemons

Open innovation platforms (web sites where crowds post ideas in a shared space) enable us to elicit huge volumes of potentially valuable solutions for problems we care about, but identifying the best ideas in these collections can be prohibitively expensive and time-consuming. This paper presents an approach, called the “bag of lemons”, which enables crowd to filter ideas with accuracy superior to conventional (Likert scale) rating approaches, but in only a fraction of the time. The key insight behind this approach is that crowds are much better at eliminating bad ideas than at identifying good ones. Keywords: Collective intelligence; Open innovation; Social computing; Idea filterin