6 research outputs found
A genetic approach for long term virtual organization distribution
Electronic versíon of an article published as International Journal on Artificial Intelligent Tools, Volume 20, issue 2, 2011. 10.1142/S0218213011000152. © World Scientific Publishing Company[EN] An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios. © 2011 World Scientific Publishing Company.This work is supported by TIN2008-04446, TIN2009-13839-C03-01, CSD2007-00022 and FPU grant AP2008-00600 of the Spanish government, and PROMETEO 2008/051 of the Generalitat Valenciana.Sánchez Anguix, V.; Valero Cubas, S.; García Fornes, AM. (2011). A genetic approach for long term virtual organization distribution. International Journal on Artificial Intelligence Tools. 20(2):271-295. https://doi.org/10.1142/S0218213011000152S27129520
Can We Reach Pareto Optimal Outcomes Using Bottom-Up Approaches?
Traditionally, researchers in decision making have focused on attempting to
reach Pareto Optimality using horizontal approaches, where optimality is
calculated taking into account every participant at the same time. Sometimes,
this may prove to be a difficult task (e.g., conflict, mistrust, no information
sharing, etc.). In this paper, we explore the possibility of achieving Pareto
Optimal outcomes in a group by using a bottom-up approach: discovering Pareto
optimal outcomes by interacting in subgroups. We analytically show that Pareto
optimal outcomes in a subgroup are also Pareto optimal in a supergroup of those
agents in the case of strict, transitive, and complete preferences. Then, we
empirically analyze the prospective usability and practicality of bottom-up
approaches in a variety of decision making domains.Comment: 2nd Workshop on Conflict Resolution in Decision Making
(COREDEMA@ECAI2016
ANAC 2017: Repeated Multilateral Negotiation League
The Automated Negotiating Agents Competition (ANAC) is annually organized competition to facilitate the research on automated negotiation. This paper presents the ANAC 2017 Repeated Multilateral Negotiation League. As human negotiators do, agents are supposed to learn from their previous negotiations and improve their negotiation skills over time. Especially, when they negotiate with the same opponent on the same domain, they can adopt their negotiation strategy according to their past experiences. They can adjust their acceptance threshold or bidding strategy accordingly. In ANAC 2017, participants aimed to develop such a negotiating agent. Accordingly, this paper describes the competition settings and results with a brief description of the winner negotiation strategies
Intra-Team Strategies for Teams Negotiating Against Competitor, Matchers, and Conceders
Under some circumstances, a group of individuals may need to negotiate
together as a negotiation team against another party. Unlike bilateral
negotiation between two individuals, this type of negotiations entails to adopt
an intra-team strategy for negotiation teams in order to make team decisions
and accordingly negotiate with the opponent. It is crucial to be able to
negotiate successfully with heterogeneous opponents since opponents'
negotiation strategy and behavior may vary in an open environment. While one
opponent might collaborate and concede over time, another may not be inclined
to concede. This paper analyzes the performance of recently proposed intra-team
strategies for negotiation teams against different categories of opponents:
competitors, matchers, and conceders. Furthermore, it provides an extension of
the negotiation tool Genius for negotiation teams in bilateral settings.
Consequently, this work facilitates research in negotiation teams.Comment: Novel Insights in Agent-based Complex Automated Negotiation, 201
Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques
A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy