Games for the Strategic Influence of Expectations

We introduce a new class of games where each player's aim is to randomise her strategic choices in order to affect the other players' expectations aside from her own. The way each player intends to exert this influence is expressed through a Boolean combination of polynomial equalities and inequalities with rational coefficients. We offer a logical representation of these games as well as a computational study of the existence of equilibria.Comment: In Proceedings SR 2014, arXiv:1404.041

Expanding FLew with a Boolean connective

We expand FLew with a unary connective whose algebraic counterpart is the operation that gives the greatest complemented element below a given argument. We prove that the expanded logic is conservative and has the Finite Model Property. We also prove that the corresponding expansion of the class of residuated lattices is an equational class.Comment: 15 pages, 4 figures in Soft Computing, published online 23 July 201

A comprehensive study of implicator-conjunctor based and noise-tolerant fuzzy rough sets: definitions, properties and robustness analysis

© 2014 Elsevier B.V. Both rough and fuzzy set theories offer interesting tools for dealing with imperfect data: while the former allows us to work with uncertain and incomplete information, the latter provides a formal setting for vague concepts. The two theories are highly compatible, and since the late 1980s many researchers have studied their hybridization. In this paper, we critically evaluate most relevant fuzzy rough set models proposed in the literature. To this end, we establish a formally correct and unified mathematical framework for them. Both implicator-conjunctor-based definitions and noise-tolerant models are studied. We evaluate these models on two different fronts: firstly, we discuss which properties of the original rough set model can be maintained and secondly, we examine how robust they are against both class and attribute noise. By highlighting the benefits and drawbacks of the different fuzzy rough set models, this study appears a necessary first step to propose and develop new models in future research.Lynn D’eer has been supported by the Ghent University Special Research Fund, Chris Cornelis was partially supported by the Spanish Ministry of Science and Technology under the project TIN2011-28488 and the Andalusian Research Plans P11-TIC-7765 and P10-TIC-6858, and by project PYR-2014-8 of the Genil Program of CEI BioTic GRANADA and Lluis Godo has been partially supported by the Spanish MINECO project EdeTRI TIN2012-39348-C02-01Peer Reviewe

A methodology to engineer graded BDI agents

In this work we present a methodological framework to engineer graded BDI agent-based systems. The graded BDI agent model allows to specify an agent’s architecture able to deal with the environment uncertainty and with graded mental attitudes. We work up previous approaches on software engineering process, adapting and extending them, in order to develop agents with a more complex internal architecture.VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Modeling travel assistant agents: a graded BDI approach

In this paper, we use a graded BDI agent model based on multi-context systems to specify an architecture for a Travel Assistant Agent that helps a tourist to choose holiday packages. We outline the theories of the different contexts and the bridge rules and illustrate the overall reasoning process of our model.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 2Red de Universidades con Carreras en Informática (RedUNCI

Two formalisms of extended possibilistic logic programming with context-dependent fuzzy unification A comparative description

AbstractPossibilistic logic is a logic of uncertainty where a certainty degree between 0 and 1, interpreted as a lower bound of a necessity measure, is attached to each classical formula. In this paper we present a comparative description of two models extending first order possibilistic logic so as to allow for fuzzy unification. The first formalism, called PLFC, is a general extension that allows clauses with fuzzy constants and fuzzily restricted quantifiers. The second formalism is an implication-based extension defined on top of Gödel infinitely-valued logic, capable of dealing with fuzzy constants. In this paper we compare these approaches, mainly their Horn-clause fragments, discussing their basic differences, specially in what regards their unification and automated deduction mechanisms