Portuguese Conference on Artificial Intelligence 

About: Portuguese Conference on Artificial Intelligence is an academic conference. The conference publishes majorly in the area(s): Computer science & Context (language use). Over the lifetime, 1046 publications have been published by the conference receiving 8234 citations.

Feeling and reasoning: a computational model for emotional characters

Abstract: Interactive virtual environments (IVEs) are now seen as an engaging new way by which children learn experimental sciences and other disciplines. These environments are populated by synthetic characters that guide and stimulate the children activities. In order to build such environments, one needs to address the problem of how achieve believable and empathic characters that act autonomously. Inspired by the work of traditional character animators, this paper proposes an architectural model to build autonomous characters where the agent’s reasoning and behaviour is influenced by its emotional state and personality. We performed a small case evaluation in order to determine if the characters evoked empathic reactions in the users with positive results.

A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News

Abstract: Due to the Web expansion, the prediction of online news popularity is becoming a trendy research topic. In this paper, we propose a novel and proactive Intelligent Decision Support System (IDSS) that analyzes articles prior to their publication. Using a broad set of extracted features (e.g., keywords, digital media content, earlier popularity of news referenced in the article) the IDSS first predicts if an article will become popular. Then, it optimizes a subset of the articles features that can more easily be changed by authors, searching for an enhancement of the predicted popularity probability. Using a large and recently collected dataset, with 39,000 articles from the Mashable website, we performed a robust rolling windows evaluation of five state of the art models. The best result was provided by a Random Forest with a discrimination power of 73%. Moreover, several stochastic hill climbing local searches were explored. When optimizing 1000 articles, the best optimization method obtained a mean gain improvement of 15 percentage points in terms of the estimated popularity probability. These results attest the proposed IDSS as a valuable tool for online news authors.

SMOTE for Regression

Abstract: Several real world prediction problems involve forecasting rare values of a target variable. When this variable is nominal we have a problem of class imbalance that was already studied thoroughly within machine learning. For regression tasks, where the target variable is continuous, few works exist addressing this type of problem. Still, important application areas involve forecasting rare extreme values of a continuous target variable. This paper describes a contribution to this type of tasks. Namely, we propose to address such tasks by sampling approaches. These approaches change the distribution of the given training data set to decrease the problem of imbalance between the rare target cases and the most frequent ones. We present a modification of the well-known Smote algorithm that allows its use on these regression tasks. In an extensive set of experiments we provide empirical evidence for the superiority of our proposals for these particular regression tasks. The proposed SmoteR method can be used with any existing regression algorithm turning it into a general tool for addressing problems of forecasting rare extreme values of a continuous target variable.

The SWRC ontology – semantic web for research communities

Abstract: Representing knowledge about researchers and research communities is a prime use case for distributed, locally maintained, interlinked and highly structured information in the spirit of the Semantic Web. In this paper we describe the publicly available ‘Semantic Web for Research Communities’ (SWRC) ontology, in which research communities and relevant related concepts are modelled. We describe the design decisions that underlie the ontology and report on both experiences with and known usages of the SWRC Ontology. We believe that for making the Semantic Web reality the re-usage of ontologies and their continuous improvement by user communities is crucial. Our contribution aims to provide a description and usage guidelines to make the value of the SWRC explicit and to facilitate its re-use.

The Impact of Branching Heuristics in Propositional Satisfiability Algorithms

Abstract: This paper studies the practical impact of the branching heuristics used in Propositional Satisfiability (SAT) algorithms, when applied to solving real-world instances of SAT. In addition, different SAT algorithms are experimentally evaluated. The main conclusion of this study is that even though branching heuristics are crucial for solving SAT, other aspects of the organization of SAT algorithms are also essential. Moreover, we provide empirical evidence that for practical instances of SAT, the search pruning techniques included in the most competitive SAT algorithms may be of more fundamental significance than branching heuristics.