Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces

Negotiating contracts with multiple interdependent issues may yield non- monotonic, highly uncorrelated preference spaces for the participating agents. These scenarios are specially challenging because the complexity of the agents’ utility functions makes traditional negotiation mechanisms not applicable. There is a number of recent research lines addressing complex negotiations in uncorrelated utility spaces. However, most of them focus on overcoming the problems imposed by the complexity of the scenario, without analyzing the potential consequences of the strategic behavior of the negotiating agents in the models they propose. Analyzing the dynamics of the negotiation process when agents with different strategies interact is necessary to apply these models to real, competitive environments. Specially problematic are high price of anarchy situations, which imply that individual rationality drives the agents towards strategies which yield low individual and social welfares. In scenarios involving highly uncorrelated utility spaces, “low social welfare” usually means that the negotiations fail, and therefore high price of anarchy situations should be avoided in the negotiation mechanisms. In our previous work, we proposed an auction-based negotiation model designed for negotiations about complex contracts when highly uncorrelated, constraint-based utility spaces are involved. This paper performs a strategy analysis of this model, revealing that the approach raises stability concerns, leading to situations with a high (or even infinite) price of anarchy. In addition, a set of techniques to solve this problem are proposed, and an experimental evaluation is performed to validate the adequacy of the proposed approaches to improve the strategic stability of the negotiation process. Finally, incentive-compatibility of the model is studied.Spain. Ministerio de Educación y Ciencia (grant TIN2008-06739-C04-04

Intelligent Traffic Light Management using Multi-Behavioral Agents

[EN] One of the biggest challenges in modern societies is to solve vehicular traffic problems. In this scenario, our proposal is to use a Multi-Agent Systems (MAS) composed of three types of agent: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. This third type of agent is able to change its behaviour between what we have called a selfish mode (the agent will try to influence the other neighbour agents of its type to achieve its goal) or an altruistic mode (the agent will take into consideration the other neighbour selfish agents indications). To validate our solution, we have developed a MAS emulator which communicates with the Simulation of Urban MObility (SUMO) traffic simulator using the Traci tool to realize the experiments in a realistic environment. The obtained results show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration.This work has been supported by the Spanish Ministry of Economy and Competitiveness grants TIN2016-80622-P, TIN2014-61627-EXP and TEC2013-45183-R, and by the University of Alcala through CCG2016/EXP-048.Cruz-Piris, L.; Rivera, D.; Marsa-Maestre, I.; De La Hoz, E.; Fernandez, S. (2018). Intelligent Traffic Light Management using Multi-Behavioral Agents. En XIII Jornadas de Ingeniería telemática (JITEL 2017). Libro de actas. Editorial Universitat Politècnica de València. 110-117. https://doi.org/10.4995/JITEL2017.2017.6494OCS11011

Ontology Alignment Architecture for Semantic Sensor Web Integration

Abstract: Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall

Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment

At the present time, Wi-Fi networks are everywhere. They operate in unlicensed radio-frequency spectrum bands (divided in channels), which are highly congested. The purpose of this paper is to tackle the problem of channel assignment in Wi-Fi networks. To this end, we have modeled the networks as multilayer graphs, in a way that frequency channel assignment becomes a graph coloring problem. For a high number and variety of scenarios, we have solved the problem with two different automated negotiation techniques: a hill-climber and a simulated annealer. As an upper bound reference for the performance of these two techniques, we have also solved the problem using a particle swarm optimizer. Results show that the annealer negotiator behaves as the best choice because it is able to obtain even better results than the particle swarm optimizer in the most complex scenarios under study, with running times one order of magnitude below. Finally, we study how different properties of the network layout affect to the performance gain that the annealer is able to obtain with respect to the particle swarm optimizer.Comment: This is a pre-print of an article published in Group Decision and Negotiation. The final version is available online at https://doi.org/10.1007/s10726-018-9600-

Scalable Complex Contract Negotiation with Structured Search and Agenda Management

A large number of interdependent issues in complex contract negotiation poses a significant challenge for current approaches, which becomes even more apparent when negotiation problems scale up. To address this challenge, we present a structured anytime search process with an agenda management mechanism using a hierarchical negotiation model, where agents search at various levels during the negotiation with the guidance of a mediator. This structured negotiation process increases computational efficiency, making negotiations scalable for large number of interdependent issues. To validate the contributions of our approach, 1) we developed our proposed negotiation model using a hierarchical problem structure and a constraint-based preference model for real-world applications; 2) we defined a scenario matrix to capture various characteristics of negotiation scenarios and developed a scenario generator that produces test cases according to this matrix; and 3) we performed an extensive set of experiments to study the performance of this structured negotiation protocol and the influence of different scenario parameters, and investigated the Pareto efficiency and social welfare optimality of the negotiation outcomes. The experimental result supports the hypothesis that this hierarchical negotiation approach greatly improves scalability with the complexity of the negotiation scenarios

A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks

Channel assignment has become a critical configuration task in Wi-Fi networks due to the increasing number and density of devices which use the same frequency band in the radioelectric spectrum. There have been a number of research efforts that propose how to assign channels to the access points of Wi-Fi networks. However, most of them ignore the effect of clients (also called stations or STAs) in channel assignment, instead focusing only on access points (APs). In this paper, we claim that considering STAs in the channel assignment procedure yields better solutions in comparison with those obtained when STAs are ignored. To evaluate this hypothesis we have proposed a heuristic technique that includes the effect of interferences produced by STAs. Results show that taking STAs into account clearly improves the performance of the solutions both in terms of the achieved utility and in terms of the variability of results. We believe that these results will be useful to the design of future channel assignment techniques which consider the effect of STAs

Fuzzy Ontology-Based System for Driver Behavior Classification

Intelligent transportation systems encompass a series of technologies and applications that exchange information to improve road traffic and avoid accidents. According to statistics, some studies argue that human mistakes cause most road accidents worldwide. For this reason, it is essential to model driver behavior to improve road safety. This paper presents a Fuzzy Rule-Based System for driver classification into different profiles considering their behavior. The system’s knowledge base includes an ontology and a set of driving rules. The ontology models the main entities related to driver behavior and their relationships with the traffic environment. The driving rules help the inference system to make decisions in different situations according to traffic regulations. The classification system has been integrated on an intelligent transportation architecture. Considering the user’s driving style, the driving assistance system sends them recommendations, such as adjusting speed or choosing alternative routes, allowing them to prevent or mitigate negative transportation events, such as road crashes or traffic jams. We carry out a set of experiments in order to test the expressiveness of the ontology along with the effectiveness of the overall classification system in different simulated traffic situations. The results of the experiments show that the ontology is expressive enough to model the knowledge of the proposed traffic scenarios, with an F1 score of 0.9. In addition, the system allows proper classification of the drivers’ behavior, with an F1 score of 0.84, outperforming Random Forest and Naive Bayes classifiers. In the simulation experiments, we observe that most of the drivers who are recommended an alternative route experience an average time gain of 66.4%, showing the utility of the proposal