Testing Carlo Cipolla's Laws of Human Stupidity with Agent-Based Modeling

International audienceWe set up an agent-based simulation to test Carlo M. Cipolla's theory of human stupidity. In particular, we investigate under which hypotheses his theory is compatible with a well-corroborated theory like natural evolution, which we build into the model. We discover that there exist parameter settings which determine the emergence of stylized facts in line with Cipolla's theory. The assumptions corresponding to those parameter settings are intuitive and justified by common sense

Hybrid Possibilistic Conditioning for Revision under Weighted Inputs

International audienceWe propose and investigate new operators in the possi-bilistic belief revision setting, obtained as different combinations of the conditioning operators on models and countermodels, as well as of how weighted inputs are interpreted. We obtain a family of eight operators that essentially obey the basic postulates of revision, with a few slight differences. These operators show an interesting variety of behaviors, making them suitable to representing changes in the beliefs of an agent in different contexts

A Conceptual Representation of Documents and Queries for Information Retrieval Systems by Using Light Ontologies

International audienceThis article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-hoc collections demonstrate the effectiveness of the proposed approach

Syntactic Computation of Hybrid Possibilistic Conditioning under Uncertain Inputs

International audienceWe extend hybrid possibilistic conditioning to deal with inputs consisting of a set of triplets composed of propositional formulas, the level at which the formulas should be accepted, and the way in which their models should be revised. We characterize such conditioning using elementary operations on possibility distributions. We then solve a difficult issue that concerns the syntactic computation of the revision of possibilistic knowledge bases, made of weighted formulas, using hybrid conditioning. An important result is that there is no extra computational cost in using hybrid possibilistic conditioning and in particular the size of the revised possibilistic base is polynomial with respect to the size of the initial base and the input

A Neuro-Evolutionary Corpus-Based Method for Word Sense Disambiguation

International audienceWe propose a supervised approach to Word Sense Disambiguation based on Neural Networks combined with Evolutionary Algorithms. An established method to automatically design the structure and learn the connection weights of Neural Networks by means of an Evolutionary Algorithm is used to evolve a neural-network disambiguator for each polysemous word, against a dataset extracted from an annotated corpus. Two distributed encoding schemes, based on the orthography of words and characterized by different degrees of information compression, have been used to represent the context in which a word occurs. The performance of such encoding schemes has been compared. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words. Comparison with the best entry of the Semeval-2007 competition has shown that the proposed approach is almost competitive with state-of-the-art WSD approaches

Trusting the messenger because of the message: feedback dynamics from information quality to source evaluation

Published Online: 28 August 2013International audienceInformation provided by a source should be assessed by an intelligent agent on the basis of several criteria: most notably, its content and the trust one has in its source. In turn, the observed quality of information should feed back on the assessment of its source, and such feedback should intelligently distribute among different features of the source--e.g., competence and sincerity. We propose a formal framework in which trust is treated as a multi-dimensional concept relativized to the sincerity of the source and its competence with respect to specific domains: both these aspects influence the assessment of the information, and also determine a feedback on the trustworthiness degree of its source. We provide a framework to describe the combined effects of competence and sincerity on the perceived quality of information. We focus on the feedback dynamics from information quality to source evaluation, highlighting the role that uncertainty reduction and social comparison play in determining the amount and the distribution of feedback

Syntactic Possibilistic Goal Generation

International audienceWe propose syntactic deliberation and goal election al-gorithms for possibilistic agents which are able to deal with incom-plete and imprecise information in a dynamic world. We show that the proposed algorithms are equivalent to their semantic counterparts already presented in the literature. We show that they lead to an ef-ficient implementation of a possibilistic BDI model of agency which integrates goal generation

A Syntactic Possibilistic Belief Change Operator: Theory and empirical study

International audienceWe propose a syntactic possibilistic belief-change operator, which operates on a belief base of necessity-valued formulas. Such a base may be regarded as a finite and compact encoding of a possibility distribution over a possibly infinite set of interpretations. The proposed operator is designed so that it behaves like a semantic possibilistic belief-change operator for BDI agents recently proposed in the literature. The equivalence of the semantic and syntactic operators is then proved. Experimental results are presented. The aim of these experiments is to demonstrate that the cost of belief revision (expressed in terms of the number of entailment checks required) as well as the size of the belief base do not explode as the number of new pieces of information (formulas) supplied increases

Extending a Fuzzy Polarity Propagation Method for Multi-Domain Sentiment Analysis with Word Embedding and POS Tagging

International audienceWithin multi-domain sentiment analysis, we study how different domain-dependent polarities can be learned for the same concepts. To this aim, we extend an existing approach based on the propagation of fuzzy polarities over a semantic graph capturing background linguistic knowledge to learn concept polarities with respect to various domains and their uncertainty from labeled datasets. In particular, we use POS tagging to refine the association between terms and concepts and word embedding to enhance the construction of the semantic graph. The proposed approach is then evaluated on a standard benchmark, showing that the combined use of POS tagging and word embedding improves its performance. One particularly strong point of the proposed approach is its recall, which is always very close to 100%. In addition, we observe that it exhibits good cross-domain generalization capabilities

Learning to Classify Logical Formulas Based on Their Semantic Similarity

International audienceAn important task in logic, given a formula and a knowledge base which represents what an agent knows of the current state of the world, is to be able to guess the truth value of the formula. Logic reasoners are designed to perform inferences, that is, to decide whether a formula is a logical consequence of the knowledge base, which is stronger than that and can be intractable in some cases. In addition, under the open-world assumption, it may turn out impossible to infer a formula or its negation. In many practical situations, however, when an agent has to make a decision, it is acceptable to resort to heuristic methods to determine the probable veracity or falsehood of a formula, even in the absence of a guarantee of correctness, to avoid blocking the decisionmaking process and move forward. This is why we propose a method to train a classification model based on available knowledge in order to be able of accurately guessing whether an arbitrary, unseen formula is true or false. Our method exploits a kernel representation of logical formulas based on a model-theoretic measure of semantic similarity. The results of experiments show that the proposed method is highly effective and accurate