1,481 research outputs found
Offer Strategy Model of Integrative Negotiation for Automated Negotiation Agent: Multiple Equivalent Simultaneous Offers and Argumentation-based Negotiation
Automated negotiation has attracted increasing interest and received phenomenal attention in the area of electronic market (e-market). Most of the studies on the automated negotiation focused on the distributive (zero-sum) negotiation, and their effectiveness is only illustrated in a single-issue negotiation between software agent-to-software agent interaction. In this study, we propose an offer strategy model of integrative negotiation for an automated negotiation agent and focus on software agent-to-human interaction. Our offer strategy model is based on the integrative approach and negotiation theory, which emphasize the importance of exchanging information among negotiators and multi-issue negotiation including package offers helping to achieve an integrative (win-win) outcome. In developing this model, we are incorporating negotiation strategy of argumentation-based negotiation and negotiation tactic of multiple equivalent simultaneous offers as an offer strategy to achieve an integrative (win-win) negotiation outcome. We expect that the result from applying the offer strategy model becomes more attractive and persuasive, thus may increase negotiation outcome satisfaction for both economic measure and social-psychological measure
On the integration of trust with negotiation, argumentation and semantics
Agreement Technologies are needed for autonomous agents to come to mutually acceptable agreements, typically on behalf of humans. These technologies include trust computing, negotiation, argumentation and semantic alignment. In this paper, we identify a number of open questions regarding the integration of computational models and tools for trust computing with negotiation, argumentation and semantic alignment. We consider these questions in general and in the context of applications in open, distributed settings such as the grid and cloud computing. © 2013 Cambridge University Press.This work was partially supported by the Agreement Technology COST action (IC0801). The authors would like to thank for helpful discussions and comments all participants in the panel on >Trust, Argumentation and Semantics> on 16 December 2009, Agia Napa, CyprusPeer Reviewe
Ontology alignment through argumentation
Currently, the majority of matchers are able to establish
simple correspondences between entities, but are
not able to provide complex alignments. Furthermore,
the resulting alignments do not contain additional information
on how they were extracted and formed. Not
only it becomes hard to debug the alignment results,
but it is also difficult to justify correspondences. We
propose a method to generate complex ontology alignments
that captures the semantics of matching algorithms
and human-oriented ontology alignment definition
processes. Through these semantics, arguments that
provide an abstraction over the specificities of the alignment
process are generated and used by agents to share,
negotiate and combine correspondences. After the negotiation
process, the resulting arguments and their relations
can be visualized by humans in order to debug
and understand the given correspondences.(undefined
A recommendation framework based on automated ranking for selecting negotiation agents. Application to a water market
This thesis presents an approach which relies on automatic learning and
data mining techniques in order to search the best group of items from a
set, according to the behaviour observed in previous groups.
The approach is applied to a framework of a water market system, which
aims to develop negotiation processes, where trading tables are built in
order to trade water rights from users. Our task will focus on predicting
which agents will show the most appropriate behaviour when are invited
to participate in a trading table, with the purpose of achieving the most
bene cial agreement.
This way, a model is developed and learns from past transactions occurred
in the market. Then, when a new trading table is opened in order to
trade a water right, the model predicts, taking into account the individual
features of the trading table, the behaviour of all the agents that can be
invited to join the negotiation, and thus, becoming potential buyers of the
water right.
Once the model has made the predictions for a trading table, the agents
are ranked according to their probability (which has been assigned by the
model) of becoming a buyer in that negotiation. Two di erent methods are
proposed in the thesis for dealing with the ranked participants. Depending
on the method used, from this ranking we can select the desired number of
participants for making the group, or choose only the top user of the list
and rebuild the model adding some aggregate information in order to throw
a more detailed prediction.Dura Garcia, EM. (2011). A recommendation framework based on automated ranking for selecting negotiation agents. Application to a water market. http://hdl.handle.net/10251/15875Archivo delegad
BNAIC 2008:Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference
Automated Algorithmic Machine-to-Machine Negotiation for Lane Changes Performed by Driverless Vehicles at the Edge of the Internet of Things
This dissertation creates and examines algorithmic models for automated machine-to-machine negotiation in localized multi-agent systems at the edge of the Internet of Things. It provides an implementation of two such models for unsupervised resource allocation for the application domain of autonomous vehicle traffic as it pertains to lane changing and speed setting selection. The first part concerns negotiation via abstract argumentation. A general model for the arbitration of conflict based on abstract argumentation is outlined and then applied to a scenario where autonomous vehicles on a multi-lane highway use expert systems in consultation with private objectives to form arguments and use them to compete for lane positions. The conflict resolution component of the resulting argumentation framework is augmented with social voting to achieve a community supported conflict-free outcome. The presented model heralds a step toward independent negotiation through automated argumentation in distributed multi-agent systems. Many other cyber-physical environments embody stages for opposing positions that may benefit from this type of tool for collaboration. The second part deals with game-theoretic negotiation through mechanism design. It outlines a mechanism providing resource allocation for a fee and applies it to autonomous vehicle traffic. Vehicular agents apply for speed and lane assignments with sealed bids containing their private feasible action valuations determined within the context of their governing objective. A truth-inducing mechanism implementing an incentive-compatible strategyproof social choice functions achieves a socially optimal outcome. The model can be adapted to many application fields through the definition of a domain-appropriate operation to be used by the allocation function of the mechanism. Both presented prototypes conduct operations at the edge of the Internet of Things. They can be applied to agent networks in just about any domain where the sharing of resources is required. The social voting argumentation approach is a minimal but powerful tool facilitating the democratic process when a community makes decisions on the sharing or rationing of common-pool assets. The mechanism design model can create social welfare maximizing allocations for multiple or multidimensional resources
Complex negotiations in multi-agent systems
Los sistemas multi-agente (SMA) son sistemas distribuidos donde entidades autónomas llamadas
agentes, ya sean humanos o software, persiguen sus propios objetivos. El paradigma de SMA ha
sido propuesto como la aproximación de modelo apropiada para aplicaciones como el comercio
electrónico, los sistemas multi-robot, aplicaciones de seguridad, etc. En la comunidad de SMA, la
visión de sistemas multi-agente abiertos, donde agentes heterogéneos pueden entrar y salir del
sistema dinámicamente, ha cobrado fuerza como paradigma de modelado debido a su relación
conceptual con tecnologías como la Web, la computación grid, y las organizaciones virtuales.
Debido a la heterogeneidad de los agentes, y al hecho de dirigirse por sus propios objetivos, el
conflicto es un fenómeno candidato a aparecer en los sistemas multi-agente.
En los últimos años, el término tecnologías del acuerdo ha sido usado para referirse a todos aquellos
mecanismos que, directa o indirectamente, promueven la resolución de conflictos en sistemas
computacionales como los sistemas multi-agente. Entre las tecnologías del acuerdo, la negociación
automática ha sido propuesta como uno de los mecanismos clave en la resolución de conflictos
debido a su uso análogo en la resolución de conflictos entre humanos. La negociación automática
consiste en el intercambio automático de propuestas llevado a cabo por agentes software en nombre
de sus usuarios. El objetivo final es conseguir un acuerdo con todas las partes involucradas.
Pese a haber sido estudiada por la Inteligencia Artificial durante años, distintos problemas todavía
no han sido resueltos por la comunidad científica todavía. El principal objetivo de esta tesis es
proponer modelos de negociación para escenarios complejos donde la complejidad deriva de (1) las
limitaciones computacionales o (ii) la necesidad de representar las preferencias de múltiples
individuos. En la primera parte de esta tesis proponemos un modelo de negociación bilateral para el
problema deSánchez Anguix, V. (2013). Complex negotiations in multi-agent systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/21570Palanci
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