In decision-making methods, it is common to assume that the experts are honest and professional. However, this is not the case when one or more experts in the pairwise-based group decision-making framework, such as the group analytic hierarchy process, try to manipulate results in their favor. This paper aims to introduce two heuristics enabling detection of manipulators and minimizing their effect on the group consensus by diminishing their weights. The first heuristic is based on the assumption that manipulators will provide judgments that can be considered outliers with respect to those of the other experts in the group. The second heuristic assumes that dishonest judgments are less consistent than the average consistency of the group. Both approaches are illustrated with numerical examples and simulations.

Abstract The role of an expert in the decision-making process is crucial. If we ask an expert to help us to make a decision we assume their honesty. But what if the expert is dishonest? Then, the answer on how difficult it is for an expert to provide manipulated data in a given case of decision-making process becomes essential. In the presented work, we consider manipulation of a ranking obtained by the Geometric Mean Method applied to a pairwise comparisons matrix. More specifically, we propose an algorithm for finding an almost optimal way to swap the positions of two selected alternatives in a ranking. We also define a new index which measures how difficult such manipulation is in a given case.

This paper examines the differences in ordinal rankings obtained from a pairwise comparison matrix using the eigenvalue method and the geometric mean method. First, we introduce several propositions on the (dis)similarity of both rankings concerning the matrix size and its inconsistency expressed by the Koczkodaj's inconsistency index. Further on, we examine the relationship between differences in both rankings and Kendall's rank correlation coefficient τ and Spearman's rank coefficient ρ. Apart from theoretical results, intuitive numerical examples and Monte Carlo simulations are also provided.

To satiate the comprehensive information need of users, retrieval systems surpassing the boundaries of language are inevitable in the present digital space in the wake of an ever-rising multilingualism. This work presents the first-of-its-kind Bilingual Text Retrieval Explanations (BiTe-REx) aimed at users performing competitor or wage analysis in the automotive domain. BiTe-REx supports users to gather a more comprehensive picture of their query by retrieving results regardless of the query language and enables them to make a more informed decision by exposing how the underlying model judges the relevance of documents. With a user study, we demonstrate statistically significant results on the understandability and helpfulness of the explanations provided by the system.

In this paper, the problem of short-term prediction of traffic flow in a city traffic network is considered.This prediction is performed in order to provide input data to a dynamic control system for street lighting.The forecasting is done by a multi-layer using artificial neural network.Because of the limited number of sensors, the data is insufficient to describe the relation between the traffic intensity at a given point and the points in which the flow intensity is measured.The proposed approach is tested by using data from the centre of Kraków.The prediction error turned to be low.

Intelligent Transportation Systems (ITS) aim to improve safety, mobility and environmental performance of road transport. The INSIGMA project provides a fresh look at the possible innovations in this field, by enhancing the functionality and accuracy of ITS in urban environments. This paper describes the architecture, sensors, processing algorithms, output modules and advantages of the developed system. A comparison of existing ITS systems has been provided as background. Special attention has been given to performance and privacy issues, as the system includes social aspects such as location monitoring.

This paper proposes a scalable, multi-agent architecture for control of modern outdoor lighting systems. Most contemporary lighting systems utilize a static control structure, which is based on simple criteria (e.g. date, time of day, weather forecast) and operate on few (two or three) lighting modes of luminaries. Thus, centralized management is sufficient for such systems. Modern lighting control systems take dynamic and fine-grained (often local) conditions into account and operate on more sophisticated equipment, characterized by flexible lighting levels and geometries. These factors cause scalability problems, which may render the system unable to react to incoming events in time. The proposed solution introduces a hierarchy of agents, which allow for distributed control and supervision. Moreover a graph-based model is introduced as a formal representation of the agent's knowledge, which allows it to be processed in parallel by distributed agents.