Ivan Marsa-Maestre

AAMAS Conference 2017 Conference Paper

A Distributed, Multi-Agent Approach to Reactive Network Resilience

• Enrique de la Hoz
• Jose Manuel Gimenez-Guzman
• Ivan Marsa-Maestre
• Luis Cruz-Piris
• David Orden

Critical network infrastructures are communication networks whose disruption can create a severe impact on other systems. Multi-agent systems have been successfully used to protect critical network infrastructures, with approaches ranging from reasoning about secure design and policies to multi-agent intrusion detection systems (IDS). However, there is little research on the possibilities for multiagent systems to react to known intrusions. In this paper we propose a multi-agent framework for reactive network resilience, that is, to allow a network to reconfigure itself in the event of a security incident so that the risk of further damage is mitigated. The proposed framework takes advantage of a risk model based on multilayer networks and of distributed belief propagation techniques to agree on a new, more resilient configuration of the network in the event of an attack. We compare our proposal with a number of centralized optimization and multi-agent negotiation techniques. Experiments show that our proposal outperforms the reference approaches both in terms of risk mitigation and performance

AAMAS Conference 2017 Conference Paper

Multi-Agent Nonlinear Negotiation for Wi-Fi Channel Assignment

• Enrique de la Hoz
• Ivan Marsa-Maestre
• Jose Manuel Gimenez-Guzman
• David Orden
• Mark Klein

Optimizing resource use in complex networks with self-interested participants (e. g. transportation networks, electric grids, Internet systems) is a challenging and increasingly critical real-world problem. We propose an approach for solving this problem based on multi-agent nonlinear negotiation, and demonstrate it in the context of Wi-Fi channel assignment. We compare the performance of our proposed approaches with a complete information optimizer based on particle swarms, together with the de facto heuristic technique based on using the least congested channel. We have evaluated all these techniques in a wide range of settings, including randomly generated scenarios and real-world ones. Our experiments show that our approach outperforms the rest of techniques in terms of social welfare. The particle swarm optimizer is the only technique whose performance is close to ours, but its computation cost is much higher. Finally, we also study the effect of some graphs metrics on the gain that our approach can achieve.

AAAI Conference 2014 Conference Paper

Scalable Complex Contract Negotiation with Structured Search and Agenda Management

• Xiaoqin Zhang
• Mark Klein
• Ivan Marsa-Maestre

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.

AAMAS Conference 2012 Conference Paper

Hierarchical Clustering and Linguistic Mediation Rules for Multiagent Negotiation

• Enrique de la Hoz
• Miguel Angel Lopez Carmona
• Mark Klein
• Ivan Marsa-Maestre

We propose a framework based on Hierarchical Clustering (HC) to perform multiagent negotiations where we can specify the type of agreements needed in terms of utility sharing among the agents. The proposed multi-round mediation process is based on the analysis of the agents’ offers at each negotiation round and the generation of a social contract at each round as a feedback to the agents, which explore the negotiation space to generate new offers. This mechanism efficiently manages negotiations following predefined consensus policies avoiding zones of no agreement.

AAMAS Conference 2010 Conference Paper

A Multi-issue Negotiation Framework for Non-monotonic Preference Spaces

• Miguel A. Lopez-Carmona
• Ivan Marsa-Maestre
• Juan R. Velasco
• Enrique de la Hoz

We present a framework for non-mediated bilateral multi-issue negotiation under non-monotonic preference spaces. The framework is based on a region-based recursive bargaining mechanism. Preliminary experimental evaluationshows that our approach may obtain approximate Pareto-optimal results in acceptable negotiation time with a lowfailure rate.

JAAMAS Journal 2010 Journal Article

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

• Miguel A. Lopez-Carmona
• Ivan Marsa-Maestre
• Takayuki Ito

Abstract 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.

AAMAS Conference 2010 Conference Paper

Avoiding the Prisoner's Dilemma in Auction-based Negotiations for Highly Rugged Utility Spaces

• Ivan Marsa-Maestre
• Miguel A. Lopez-Carmona
• Juan R. Velasco
• Enrique de la Hoz

There is a number of recent research lines addressing complex negotiations in highly rugged utility spaces. However, most of them focus on overcoming the problems imposedby the complexity of the scenario, without analyzing thestrategic behavior of the agents in the models they propose. Analyzing the dynamics of the negotiation processwhen agents with different strategies interact is necessaryto apply these models to real, competitive environments, where agents cannot be supposed to behave in the same way. Specially problematic are situations like the well-known prisoner's dilemma, or more generally, situations of high price ofanarchy. These situations imply that individual rationalitydrives the agents towards strategies which yield low individual and social welfares. In highly rugged scenarios, suchsituations usually make agents fail to reach an agreement, and therefore negotiation mechanisms should be designed toavoid them. This paper performs a strategy analysis of anauction-based negotiation model designed for highly ruggedscenarios, revealing that the approach is prone to the prisoner's dilemma. In addition, a set of techniques to solvethis problem are proposed, and an experimental evaluationis performed to validate the adequacy of the proposed approaches to improve the strategic stability of the negotiationprocess.

IJCAI Conference 2009 Conference Paper

• Ivan Marsa-Maestre
• Miguel A. Lopez-Carmona
• Juan R. Velasco
• Takayuki Ito
• Mark Klein
• Katsuhide Fujita

Negotiation scenarios involving nonlinear utility functions are specially challenging, because traditional negotiation mechanisms cannot be applied. Even mechanisms designed and proven useful for nonlinear utility spaces may fail if the utility space is highly nonlinear. For example, although both contract sampling and constraint sampling have been successfully used in auction based negotiations with constraint-based utility spaces, they tend to fail in highly nonlinear utility scenarios. In this paper, we will show that the performance of these approaches decrease drastically in highly nonlinear utility scenarios, and propose a mechanism which balances utility and deal probability for the bidding and deal identification processes. The experiments show that the proposed mechanisms yield better results than the previous approaches in highly nonlinear negotiation scenarios.

AAMAS Conference 2009 Conference Paper

Effective Bidding and Deal Identification for Negotiations in Highly Nonlinear Scenarios

• Ivan Marsa-Maestre
• Miguel A. Lopez-Carmona
• Juan R. Velasco
• Enrique de la Hoz

Most real-world negotiation scenarios involve multiple, interdependent issues. These scenarios are specially challenging because the agents’ utility functions are nonlinear, which makes traditional negotiation mechanisms not applicable. Even mechanisms designed and proven useful for nonlinear utility spaces may fail if the utility space is highly nonlinear. For example, simulated annealing has been used successfully in bidding based negotiations with constraint-based utility spaces to identify high utility regions in the contract space, and to send these regions as bids to a mediator. In this paper, we will show that the performance of this approach decreases drastically in highly nonlinear scenarios, and propose alternative mechanisms for the bidding process which take advantage of the constraint-based preference model. Also, we propose a probabilistic search method for the mediator to improve the scalability of the deal identification process, and an iterative, expressive negotiation protocol to give feedback to the agents in case no deals have been found with the initial bids. The experiments show that the proposed mechanisms yield better results than the previous approach in highly nonlinear negotiation scenarios.