An approach to automated agent negotiation using belief revision

Feature selection is a useful machine learning technique aimed at reducing the dimensionality of the input space, discarding useless or redundant variables, in order to increase the performance and interpretability of models. The well-known Recursive Feature Elimination (RFE) algorithm provides good performance with moderate computational efforts, in particular for wide datasets. When using Support Vector Machines (SVM) for multiclass classification problems, the most typical strategy is to apply a simple One–Vs–One (OVO) strategy to produce a multiclass classifier starting from binary ones. In this work we introduce improved methods to produce the final ranking of features on multiclass problems with OVO–SVM, based on different combinations of the set of rankings produced by the diverse binary problems. We evaluated our new strategies using wide datasets from mass–spectrometry analysis and standard datasets from the UCI repository. In particular, we compared the new methods with the traditional average strategy. Our results suggest that one of our new methods outperforms the traditional scheme in most situations.Sociedad Argentina de Informática e Investigación Operativ

AGM 25 years: twenty-five years of research in belief change

The 1985 paper by Carlos Alchourrón (1931–1996), Peter Gärdenfors, and David Makinson (AGM), “On the Logic of Theory Change: Partial Meet Contraction and Revision Functions” was the starting-point of a large and rapidly growing literature that employs formal models in the investigation of changes in belief states and databases. In this review, the first twenty five years of this development are summarized. The topics covered include equivalent characterizations of AGM operations, extended representations of the belief states, change operators not included in the original framework, iterated change, applications of the model, its connections with other formal frameworks, computatibility of AGM operations, and criticism of the model.info:eu-repo/semantics/publishedVersio

A multi-demand negotiation model based on fuzzy rules elicited via psychological experiments

This paper proposes a multi-demand negotiation model that takes the effect of human users’ psychological characteristics into consideration. Specifically, in our model each negotiating agent's preference over its demands can be changed, according to human users’ attitudes to risk, patience and regret, during the course of a negotiation. And the change of preference structures is determined by fuzzy logic rules, which are elicited through our psychological experiments. The applicability of our model is illustrated by using our model to solve a problem of political negotiation between two countries. Moreover, we do lots of theoretical and empirical analyses to reveal some insights into our model. In addition, to compare our model with existing ones, we make a survey on fuzzy logic based negotiation, and discuss the similarities and differences between our negotiation model and various consensus models

A logic-based axiomatic model of bargaining

AbstractThis paper introduces an axiomatic model for bargaining analysis. We describe a bargaining situation in propositional logic and represent bargainers' preferences in total pre-orders. Based on the concept of minimal simultaneous concessions, we propose a solution to n-person bargaining problems and prove that the solution is uniquely characterized by five logical axioms: Consistency, Comprehensiveness, Collective rationality, Disagreement, and Contraction independence. This framework provides a naive solution to multi-person, multi-issue bargaining problems in discrete domains. Although the solution is purely qualitative, it can also be applied to continuous bargaining problems through a procedure of discretization, in which case the solution coincides with the Kalai–Smorodinsky solution