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

Competitive service market: modeling, storage and management

In order to capture the business dynamics underlying SOA-based service systems, we propose and formalize the concept of a competitive service market (CSM). A CSM is composed of a set of composite service providers, each managing a collection of atomic service providers. With the help of service composition protocol, composite service providers are able to invoke atomic services and aggregate them into value-added composite services for servicing various types of customers\u27 requests. Centering around the setting of a competitive service market, our research is separated into three parts: 1. Aiming to support the quantitative-based decision processes of different market players, we construct stochastic models to conduct performance analysis at various levels spanning vertically on the structural hierarchy of the service market. 2. In the context of requirements analysis, we classify the concept of service and service instance in terms of their respective functional and non-functional features. Hereafter, we identify the related storage issues and propose a counting Bloom filter-based hybrid storage architecture for the service registry design underlying the service market. A feature-based service discovery protocol is developed to demonstrate the usefulness of this design. 3. The business relationship between different market players are typically framed through the service level agreements (SLAs), which specify the attributes of QoS-based metrics and service costs for the realized service provisioning. SLAs constitute the backbone structure for managing the CSM. We identify several SLA design patterns in terms of different business scenarios that can occur in the life cycle of a service market. Against each pattern we study the corresponding SLA design scheme that can meet its unique requirements. In addition, we systematically investigate the application of Bayes estimator in these schemes, since the knowledge of their negotiation counterpart or market competitors is essential for reaching the goal of utility optimization. At the end, we cast the hybrid SLA design framework into a stochastic model that allows decision makers to obtain evaluations of performance of interest

Negotiation-Style Recommender Based on Computational Ecology in Open Negotiation Environments.

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies.Fil: De La Rosa, Josep Lluis. Universidad de Girona; EspañaFil: Hormazábal, Nicolás. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Lopardo, Gabriel Alejandro. Universidad de Girona; EspañaFil: Trias, Albert. Universidad de Girona; EspañaFil: Montaner, Miquel. No especifíca

Political Risk and Regulatory Risk: Issues in Emerging Markets Infrastructure Concessions

Political and regulatory risks, cause damage to countries and investors because of investment diminishing. When investments take place, those could increase services prices. Present work has as its objectives to characterize theoretically the problem, to study existent measures to face it, to know the available instruments to deal with it, and to draw some general conclusions on political and regulatory risks, and some specific conclusions referred to infrastructure concessions. The article is limited to the study of opportunistic behavior or governments.regulatory risks; Issues in Emerging Markets; Infrastructure Concessions

Computational intelligence based complex adaptive system-of-systems architecture evolution strategy

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta-architecture generation, architecture assessment and architecture implementation. Meta-architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. An SAR SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta-architectures based on the wave model. --Abstract, page iii

From wholesale energy markets to local flexibility markets: structure, models and operation

Most energy markets (EMs) across Europe are based on a design framework involving day-ahead, intraday, and bilateral markets, operating together with balancing markets. This framework was set out, however, when the vast majority of generation units were controllable and fuel-based. The increasing levels of renewable generation create unique challenges in the operation of EMs. In this context, flexibility markets are starting to be recognized as a promising and powerful tool to adequately valorize demand-side flexibility. This chapter describes the models underlying both centralized and bilateral markets, analyzes the operation of several European markets, introduces some energy management tools, analyzes the pressing issue of flexibility in system operation, and describes various pioneering flexibility platforms.info:eu-repo/semantics/publishedVersio

2021 Workers Compensation Institute

Meeting proceedings of a seminar by the same name, held November 9, 2021

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