A baseline for non-linear bilateral negotiations: the full results of the agents competing in ANAC 2014

In the past few years, there is a growing interest in automated negotiation in which software agents facilitate negotiation on behalf of their users and try to reach joint agreements. The potential value of developing such mechanisms becomes enormous when negotiation domain is too complex for humans to find agreements (e.g. e-commerce) and when software components need to reach agreements to work together (e.g. web-service composition). Here, one of the major challenges is to design agents that are able to deal with incomplete information about their opponents in negotiation as well as to effectively negotiate on their users’ behalves. To facilitate the research in this field, an automated negotiating agent competition has been organized yearly. This paper introduces the research challenges in Automated Negotiating Agent Competition (ANAC) 2014 and explains the competition set up and results. Furthermore, a detailed analysis of the best performing five agents has been examined

Automated Negotiation for Complex Multi-Agent Resource Allocation

The problem of constructing and analyzing systems of intelligent, autonomous agents is becoming more and more important. These agents may include people, physical robots, virtual humans, software programs acting on behalf of human beings, or sensors. In a large class of multi-agent scenarios, agents may have different capabilities, preferences, objectives, and constraints. Therefore, efficient allocation of resources among multiple agents is often difficult to achieve. Automated negotiation (bargaining) is the most widely used approach for multi-agent resource allocation and it has received increasing attention in the recent years. However, information uncertainty, existence of multiple contracting partners and competitors, agents\u27 incentive to maximize individual utilities, and market dynamics make it difficult to calculate agents\u27 rational equilibrium negotiation strategies and develop successful negotiation agents behaving well in practice. To this end, this thesis is concerned with analyzing agents\u27 rational behavior and developing negotiation strategies for a range of complex negotiation contexts. First, we consider the problem of finding agents\u27 rational strategies in bargaining with incomplete information. We focus on the principal alternating-offers finite horizon bargaining protocol with one-sided uncertainty regarding agents\u27 reserve prices. We provide an algorithm based on the combination of game theoretic analysis and search techniques which finds agents\u27 equilibrium in pure strategies when they exist. Our approach is sound, complete and, in principle, can be applied to other uncertainty settings. Simulation results show that there is at least one pure strategy sequential equilibrium in 99.7% of various scenarios. In addition, agents with equilibrium strategies achieved higher utilities than agents with heuristic strategies. Next, we extend the alternating-offers protocol to handle concurrent negotiations in which each agent has multiple trading opportunities and faces market competition. We provide an algorithm based on backward induction to compute the subgame perfect equilibrium of concurrent negotiation. We observe that agents\u27 bargaining power are affected by the proposing ordering and market competition and for a large subset of the space of the parameters, agents\u27 equilibrium strategies depend on the values of a small number of parameters. We also extend our algorithm to find a pure strategy sequential equilibrium in concurrent negotiations where there is one-sided uncertainty regarding the reserve price of one agent. Third, we present the design and implementation of agents that concurrently negotiate with other entities for acquiring multiple resources. Negotiation agents are designed to adjust 1) the number of tentative agreements and 2) the amount of concession they are willing to make in response to changing market conditions and negotiation situations. In our approach, agents utilize a time-dependent negotiation strategy in which the reserve price of each resource is dynamically determined by 1) the likelihood that negotiation will not be successfully completed, 2) the expected agreement price of the resource, and 3) the expected number of final agreements. The negotiation deadline of each resource is determined by its relative scarcity. Since agents are permitted to decommit from agreements, a buyer may make more than one tentative agreement for each resource and the maximum number of tentative agreements is constrained by the market situation. Experimental results show that our negotiation strategy achieved significantly higher utilities than simpler strategies. Finally, we consider the problem of allocating networked resources in dynamic environment, such as cloud computing platforms, where providers strategically price resources to maximize their utility. While numerous auction-based approaches have been proposed in the literature, our work explores an alternative approach where providers and consumers negotiate resource leasing contracts. We propose a distributed negotiation mechanism where agents negotiate over both a contract price and a decommitment penalty, which allows agents to decommit from contracts at a cost. We compare our approach experimentally, using representative scenarios and workloads, to both combinatorial auctions and the fixed-price model, and show that the negotiation model achieves a higher social welfare

A hybrid model of electronic negotiation : integration of negotiation support and automated negotiation models

Electronic business negotiations are enabled by different electronic negotiation models: automated negotiation models for software agents, negotiation support models for human negotiators, and auction models for both. To date, there is no electronic negotiation model that enables bilateral multi-issue negotiations between a human negotiator and a negotiation agent?an important task in electronic negotiation research. In this thesis, a model is presented that integrates the automated negotiation model and the negotiation support model. The resulting hybrid negotiation model paves the way for human-agent business negotiations. The integration of two models is realised at the levels of negotiation process, communication support and decision making. To this end, the negotiation design, negotiation process, negotiation decision making, and negotiation communication in negotiation support systems (NSSs) and agent negotiation systems (ANSs) are studied and analysed. The analyses on these points help in strengthening the motivation behind hybrid negotiation model and setting aims for the integration of an NSS and an ANS in hybrid negotiation model. We mainly propose a human-agent negotiation design, negotiation process protocols to support the design, a hybrid communication model for human-agent interaction, an agent decision-making model for negotiation with human, and a component for interoperability between NSS and ANS. The agent decision-making model is composed of heuristic and argumentation-based negotiation techniques. It is proposed after analysing different automated negotiation models for different human negotiation strategies. The proposed communication model supports human negotiator and negotiation agent to understand and process negotiation messages from each other. This communication model consists of negotiation ontology, a wrapper agent, and a proper selection of an agent communication language (ACL) and a content language. The wrapper agent plays a role for interoperability between agent system and NSS by providing a communication interface along with the negotiation ontology. The negotiation ontology, ACL and agent content language make the communication model of negotiation agent in ANS. The proposed hybrid model is realised by integrating an ANS into NSS Negoisst. The research aim is to show that a hybrid negotiation system, composed of two heterogeneous negotiation models, can enable human-agent multi-issue integrative negotiations.Elektronische ökonomische Verhandlungen werden durch verschiedene Verhandlungsmodelle ermöglicht: Automatisierte Verhandlungsmodelle für Softwareagenten, Verhandlungsunterstützung für menschliche Verhandelnde und Auktionsmodelle für Beide. Bis heute existiert kein elektronisches Verhandlungsmodell, das bilaterale multi-attributive Verhandlungen zwischen einem menschlichen Verhandelnden und einem Verhandlungsagenten ? eine wichtige Aufgabe in der Forschung im Bereich elektronischer Verhandlungen. In dieser Arbeit wird ein Modell präsentiert, welches das automatisierte Verhandlungsmodell und das Verhandlungsunterstützungsmodell integriert. Das resultierende hybride Verhandlungsmodell ebnet den Weg für ökonomische Mensch-Agent-Verhandlungen. Die Integration der zwei Modelle ist realisiert auf der Ebene von Verhandlungsprozess, Kommunikationsunterstützung und Entscheidungsunterstützung. Dazu werden Verhandlungsdesign, Verhandlungsprozess, verhandlungsbezogene Entscheidungsfindung und Verhandlungskommunikation in Verhandlungsunterstützungssystemen (NSS) und Agentenverhandlungssystemen (ANS) studiert und analysiert. Die Analysen zu diesen Punkten verstärken die Motivation hinter dem hybriden Verhandlungsmodell und bestimmen die Ziele für die Integration von NSS und ANS. Es werden hauptsächlich ein Mensch-Agent-Verhandlungsdesign, Verhandlungsprozessprotokolle zur Unterstützung des Designs, ein hybrides Kommunikationsmodell für Mensch-Agent-Kommunikation, ein Agenten-Entscheidungsmodell für die Verhandlung mit menschlichem Gegenpart und eine Komponente für die Interoperabilität zwischen NSS und ANS. Das Entscheidungsmodell für Agenten besteht aus heuristischen und argumentativen Verhandlungstechniken. Es wird aufgestellt nachdem verschiedene automatisierte Verhandlungsmodelle für verschiedene menschliche Verhandlungsstrategien analysiert worden sind. Die vorgeschlagenen Kommunikationsmodelle unterstützen menschliche Verhandler und Verhandlungsagenten dabei Verhandlungsnachrichten voneinander zu verstehen und zu verarbeiten. Dieses Kommunikationsmodell besteht aus einer Verhandlungsontologie, einem Wrapper-Agenten und einer angemessenen Auswahl der Agentenkommunikationssprache (ACL) und der Inhaltssprache. Der Wrapper-Agent spielt eine Rolle bei der Interoperabilität zwischen dem Agentensystem und dem NSS durch eine Kommunikationsschnittstelle zusammen mit der Verhandlungsontologie. Die Verhandlungsontologie, die ACL und die Inhaltssprache der Agenten ergeben das Kommunikationsmodell der Verhandlungsagenten im ANS. Das vorgestellte hybride Modell ist realisiert als Integration eines ANS in das NSS Negoisst. Das Forschungsziel ist zu zeigen, dass ein hybrides Verhandlungssystem, basierend auf zwei heterogenen Verhandlungsmodellen, integrative multi-attributive Mensch-Agent-Verhandlungen ermöglicht

On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes