[[alternative]]The Strategies and Framework of Automated Negotiation

計畫編號：NSC90-2213-E032-005研究期間：200108~200207研究經費：320,000[[sponsorship]]行政院國家科學委員

[[alternative]]On the Prediction of the Opponents Preferences in Negotiation

計畫編號：NSC91-2213-E032-017研究期間：200208~200307研究經費：408,000[[sponsorship]]行政院國家科學委員

Building a Semantic Tendering System

In the new B2B e-commerce arena, applications such as auctions and data exchange are growing rapidly. However, Web content is currently designed for human consumption rather than computer manipulation. This limits the possibility of Web automation. Fortunately, the new development of the Semantic Web that allows Web pages to provide information not only in terms of their content, but also in terms of the properties of that content, can be used for automation. Electronic tendering systems are among the successfully commercial systems that can tremendously benefit from the availability of Semantic Web. This study proposes an e-tendering system that uses the Semantic Web to investigate the automatic negotiation process. The system is built in a P2P environment to simulate a two-player negotiation. It is found that the ontology of semantic information can be used to locate qualified suppliers and precede negotiation. The bargaining power of each party is then determined by the relative magnitude of the negotiators’ respective costs of haggling and the utility that varies with the degree of risk preference. Our experiments showed that applying automatic negotiation strategies to e-tendering system in semantic web can reflect the risk preference of the participants

An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications

Collaborative and adaptive supply chain planning

Dans le contexte industriel d'aujourd'hui, la compétitivité est fortement liée à la performance de la chaîne d'approvisionnement. En d'autres termes, il est essentiel que les unités d'affaires de la chaîne collaborent pour coordonner efficacement leurs activités de production, de façon a produire et livrer les produits à temps, à un coût raisonnable. Pour atteindre cet objectif, nous croyons qu'il est nécessaire que les entreprises adaptent leurs stratégies de planification, que nous appelons comportements, aux différentes situations auxquelles elles font face. En ayant une connaissance de l'impact de leurs comportements de planification sur la performance de la chaîne d'approvisionnement, les entreprises peuvent alors adapter leur comportement plutôt que d'utiliser toujours le même. Cette thèse de doctorat porte sur l'adaptation des comportements de planification des membres d'une même chaîne d'approvisionnement. Chaque membre pouvant choisir un comportement différent et toutes les combinaisons de ces comportements ayant potentiellement un impact sur la performance globale, il est difficile de connaître à l'avance l'ensemble des comportements à adopter pour améliorer cette performance. Il devient alors intéressant de simuler les différentes combinaisons de comportements dans différentes situations et d'évaluer les performances de chacun. Pour permettre l'utilisation de plusieurs comportements dans différentes situations, en utilisant la technologie à base d'agents, nous avons conçu un modèle d'agent à comportements multiples qui a la capacité d'adapter son comportement de planification selon la situation. Les agents planificateurs ont alors la possibilité de se coordonner de façon collaborative pour améliorer leur performance collective. En modélisant les unités d'affaires par des agents, nous avons simulé avec la plateforme de planification à base d'agents de FORAC des agents utilisant différents comportements de planification dits de réaction et de négociation. Cette plateforme, développée par le consortium de recherche FORAC de l'Université Laval, permet de simuler des décisions de planification et de planifier les opérations de la chaîne d'approvisionnement. Ces comportements de planification sont des métaheurisciques organisationnelles qui permettent aux agents de générer des plans de production différents. La simulation est basée sur un cas illustrant la chaîne d'approvisionnement de l'industrie du bois d'œuvre. Les résultats obtenus par l'utilisation de multiples comportements de réaction et de négociation montrent que les systèmes de planification avancée peuvent tirer avantage de disposer de plusieurs comportements de planification, en raIson du contexte dynamique des chaînes d'approvisionnement. La pertinence des résultats de cette thèse dépend de la prémisse que les entreprises qui adapteront leurs comportements de planification aux autres et à leur environnement auront un avantage concurrentiel important sur leurs adversaires

R&D for computational cognitive and social models : foundations for model evaluation through verification and validation (final LDRD report).

Sandia National Laboratories is investing in projects that aim to develop computational modeling and simulation applications that explore human cognitive and social phenomena. While some of these modeling and simulation projects are explicitly research oriented, others are intended to support or provide insight for people involved in high consequence decision-making. This raises the issue of how to evaluate computational modeling and simulation applications in both research and applied settings where human behavior is the focus of the model: when is a simulation 'good enough' for the goals its designers want to achieve? In this report, we discuss two years' worth of review and assessment of the ASC program's approach to computational model verification and validation, uncertainty quantification, and decision making. We present a framework that extends the principles of the ASC approach into the area of computational social and cognitive modeling and simulation. In doing so, we argue that the potential for evaluation is a function of how the modeling and simulation software will be used in a particular setting. In making this argument, we move from strict, engineering and physics oriented approaches to V&V to a broader project of model evaluation, which asserts that the systematic, rigorous, and transparent accumulation of evidence about a model's performance under conditions of uncertainty is a reasonable and necessary goal for model evaluation, regardless of discipline. How to achieve the accumulation of evidence in areas outside physics and engineering is a significant research challenge, but one that requires addressing as modeling and simulation tools move out of research laboratories and into the hands of decision makers. This report provides an assessment of our thinking on ASC Verification and Validation, and argues for further extending V&V research in the physical and engineering sciences toward a broader program of model evaluation in situations of high consequence decision-making

Mobile-agent based multi-constraint one-to-many bilateral e-Negotiation framework

The thesis proposes a multi-constraint one-to-many bilateral e-Trade negotiation framework. It deploys mobile agents in negotiation, considers trading competition between vendors and search space, efficiently manages the risk of losing top utility offers that expire before the negotiation deadline, accurately evaluates offers, and truly maintains the security of negotiation data

Simulation of automated negotiation

Durch die Automatisierung von Verhandlungen sollen bessere Verhandlungsergebnisse erzielt werden können als bei Verhandlungen zwischen Menschen und neue Koordinationsformen für autonome Agentensysteme ermöglicht werden. Diese Arbeit beschäftigt sich mit der Simulation solcher Systeme für automatisierte Verhandlungen, da operative Systeme zur Zeit noch nicht verfügbar sind. Die Arbeit basiert auf einer Erhebung und Diskussion der aktuellen Literatur im Bereich der Simulation automatisierter Verhandlungen. Existierende Ansätze weisen einige Unzulänglichkeiten bezüglich deren praktischer Umsetzbarkeit in einer offenen Umgebung wie dem Internet auf, wo automatisierte Verhandlungen nicht nur sehr schnell durchgeführt werden sondern sich auch Software-Agenten und Verhandlungsprobleme ändern können. Diese Defizite thematisierend werden Verhandlungssysteme für automatisierte Verhandlungen vorgeschlagen. Diese bestehen zum einen aus Software-Agenten, die generische Angebots- und Konzessionsstratgien verfolgen, zum anderen aus Interaktionsprotokollen, die es Agenten erlauben ihre Strategien vorübergehend oder permanent auszusetzen. Ergebnisse der Simulation dieser Systeme, mit Verhandlungsproblemen aus Verhandlungsexperimenten mit menschlichen Probanden als Input, werden für unterschiedliche Ergebnisdimensionen -- Übereinkunftshäufigkeit, Fairness, individuelle und kollektive Effizienz -- zwischen Systemen und auch mit den Ergebnissen der Experimente verglichen. Trotz fundamentaler Zielkonflikte zwischen den einzelnen Ergebnisdimensionen erzielen einige Systeme konsistent bessere Ergebnisse sowohl im Systemvergleich als auch verglichen mit den Ergebnissen der Experimente. Diese Systeme bestehen aus Software-Agenten die systematisch Angebote mit monoton abnehmendem Nutzen unterbreiten und erste Konzessionensschritte tätigen solange der Opponent bisherige Konzessionen erwidert hat. Das verwendete Interaktionsprotokoll zeichnet sich dadurch aus, dass es den Agenten erlaubt ungünstige Angebote zurückzuweisen und damit neue Angebote des Opponenten einzufordern, durch diese Unterbrechung der eigenen Angebotsstrategie können ungünstige Verhandlungsergebnisse vermieden werden.Automated negotiation is argued to improve negotiation outcomes by replacing humans and to enable coordination in autonomous systems. As operative systems do not yet exist scholars rely on simulations to evaluate potential systems for automated negotiation. This dissertation reviews the state of the art literature on simulation of automated negotiation along its main components - negotiation problem, interaction protocol, and software agents. Deficiencies of existing approaches concerning the practical application in an open environment as the Internet - where automated negotiation proceeds fast, with changing opponents, and for various negotiation problems - are identified. To address these deficiencies we develop and simulate automated negotiation systems, consisting of software agents that follow generic offer generation and concession strategies and protocols that allow these agents to interrupt their strategy to avoid exploitation and unfavorable agreements. Outcomes of simulation runs are compared across systems and to human negotiation along various outcome dimensions - proportion of agreements, dyadic and individual performance, and fairness - for various negotiation problems derived from negotiation experiments with human subjects. Though there exist trade-offs between the different outcome dimensions, systems consisting of software agents, that systematically propose offers of monotonically decreasing utility and make first concession steps if the opponent reciprocated previous concessions, and an interaction protocol that enables to reject unfavorable offers - without immediately aborting negotiations - in order to elicit new offers from the opponent, performed best. These systems performed very well in all outcome dimensions when compared with other systems and were the only that outperformed negotiation between humans in all dimensions