Some Observations on Multi-Agent Based Negotiation in B2C E-Commerce

Multi-agent based negotiation is the emergent functionality of E-Commerce. There are several approach deployed by various researcher in the B2C, E-Commerce model. In this research paper we provide some observations on various negotiation mechanism which are deployed in various E-Commerce mode

Efficient Communication in Agent-based Autonomous Logistic Processes

Transportation of goods plays a vital role for the success of a logistics network. The ability to transport goods quickly and cost effectively is one of the major requirements of the customers. Dynamics involved in the logistics process like change or cancellation of orders or uncertain information about the orders add to the complexity of the logistic network and can even reduce the efficiency of the entire logistics process. This brings about a need of integrating technology and making the system more autonomous to handle these dynamics and to reduce the complexity. Therefore, the distributed logistics routing protocol (DLRP) was developed at the University of Bremen. In this thesis, DLRP is extended with the concept of clustering of transport goods, two novel routing decision schemes and a negotiation process between the cluster of goods and the vehicle. DLRP provides the individual logistic entities the ability to perform routing tasks autonomously e.g., discovering the best route to the destination at the given time. Even though DLRP seems to solve the routing problem in real-time, the amount of message flooding involved in the route discovery process is enormous. This motivated the author to introduce a cluster-based routing approach using software agents. The DLRP along with the clustering algorithm is termed as the cluster-based DLRP. In the latter, the goods are first clustered into groups based on criteria such as the common destination. The routing is now handled by the cluster head rather than the individual transport goods which results in a reduced communication volume in the route discovery. The latter is proven by evaluating the performance of the cluster-based DLRP approach compared to the legacy DLRP. After the routing process is completed by the cluster heads, the next step is to improve the transport performance in the logistics network by identifying the best means to transport the clustered goods. For example, to have better utilization of the transport capacity, clusters can be transported together on a stretch of overlapping route. In order to make optimal transport decisions, the vehicle calculates the correlation metric of the routes selected by the various clusters. The correlation metric aids in identifying the clusters which can be transported together and thereby can result in better utilization of the transport resources. In turn, the transportation cost that has to be paid to the vehicle can be shared between the different clusters. The transportation cost for a stretch of route is calculated by the vehicle and offered to the cluster. The latter can decide based upon the transportation cost or the selected route whether to accept the transport offer from the vehicle or not. In this regard, different strategies are developed and investigated. Thereby a performance evaluation of the capacity utilization of the vehicle and the transportation cost incurred by the cluster is presented. Finally, the thesis introduces the concept of negotiation in the cluster based routing methods. The negotiation process enhances the transport decisions by giving the clusters and the vehicles the flexibility to negotiate the transportation cost. Thus, the focus of this part of the thesis is to analyse the negotiation strategies used by the logistics entities and their role in saving negotiation time while achieving a favorable transportation cost. In this regard, a performance evaluation of the different proposed strategies is presented, which in turn gives the logistics practitioners an overview of the best strategy to be deployed in various scenarios. Clustering of goods aid in the negotiation process as on the one hand, a group of transport goods have a stronger basis for negotiation to achieve a favorable transportation price from the vehicle. On the other hand it makes it easier for the vehicle to select the packages for transport and helps the vehicle to operate close to its capacity. In addition, clustering enables the negotiation process to be less complex and voluminous. From the analytical considerations and obtained results in the three parts of this thesis, it can be concluded that efficient transport decisions, though very complex in a logistics network, can be simplified to a certain extent utilizing the available information of the goods and vehicles in the network

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.Os mercados de electricidade sofreram um processo de reestruturação que originou um aumento considerável da competitividade neste sector e, consequentemente, criou novos desafios na operação das entidades nele envolvidas. De forma a ultrapassar estes desafios é essencial para os profissionais uma compreensão detalhada dos princípios destes mercados e de como gerir os seus investimentos num ambiente tão dinâmico e competitivo. A crescente necessidade de entender estes mecanismos e a forma como a interacção das entidades envolvidas afecta os resultados destes mercados levou a uma grande procura de ferramentas de software, nomeadamente simulação, para analisar possíveis resultados de cada contexto de mercado para as várias entidades participantes. Os sistemas multi-agente são adequados à análise de sistemas dinâmicos e adaptativos com interacções complexas entre os seus constituintes, e portanto, várias ferramentas de modelação dirigidas para o estudo dos mercados reestruturados de electricidade usam este tipo de técnicas. Tirando partido destes simuladores, é possível estudar vários tipos de mercados e a interacção entre as entidades neles envolvidas. No entanto, todos estes simuladores apresentam lacunas no que diz respeito ao apoio à decisão a essas entidades, nomeadamente na gestão dos seus investimentos. Um aspecto tão relevante como é a utilização de todo este suporte de simulação para permitir aos agentes de mercado realmente aprenderem com a experiência de mercado e desenvolveram capacidades para analisar contextos de negociação e adaptar automaticamente os seus comportamentos estratégicos de acordo com as circunstâncias, não é considerado na amplitude que é requerida. É neste âmbito que esta dissertação contribui, utilizando técnicas de inteligência artificial para oferecer um apoio relevante e eficaz às decisões estratégicas das empresas envolvidas nestes tipos de negociação. O principal objectivo deste trabalho é dotar essas entidades de capacidades que lhes permitam apresentar comportamentos inteligentes e adaptativos na sua actuação nos mercados de electricidade de forma a serem capazes de atingir os seus objectivos da melhor forma possível, sendo capazes de reconhecer e actuar em conformidade com os contextos em que estão inseridas. De forma a atingir este objectivo, foi desenvolvido o sistema ALBidS – Adaptive Learning strategic Bidding System (sistema de aprendizagem adaptativa para licitações estratégias). Este sistema está implementado como um sistema multi-agente independente, em que cada agente é responsável pela execução de uma abordagem estratégica diferente. Este sistema está integrado com o simulador MASCEM, para que seja possível testar e validar as contribuições dadas num contexto de simulação de mercados já implementado e consolidado. Sendo este simulador uma ferramenta que simula mercados de electricidade permitindo a utilização de informação obtida a partir de mercados de electricidade reais, garante-se, assim, também que as conclusões retiradas deste trabalho são apoiadas por experimentação baseada em casos reais ou quase reais. A definição das estratégias de oferta dos agentes de mercado é baseada na aprendizagem adaptativa por parte das entidades, considerando o histórico do sistema, através da informação disponível, incluindo informação recolhida durante a utilização do próprio sistema multi-agente. Para isso são propostos e testados vários algoritmos e metodologias de aprendizagem e análise de dados, para que conjuntamente contribuam para que os agentes possam tomar as melhores decisões em cada momento de acordo com o contexto identificado. Um contributo importante do trabalho está na proposta destes algoritmos, na sua combinação e na obtenção de conhecimento relativo à utilização criteriosa dos algoritmos considerados em função do contexto, utilizando o conceito de context awareness. A análise destes contextos é efectuada por um mecanismo desenvolvido para esse efeito, analisando as características específicas de cada dia e período de negociação. São estudados e analisados vários algoritmos baseados em abordagens diversas, para que seja possível contemplar formas distintas de resolver problemas, dependendo de circunstâncias concretas. Entre estas abordagens, podem referir-se: redes neuronais artificiais dinâmicas; teoria de jogos; médias/regressões lineares; abordagens económicas, tendo em conta a análise macroeconómica e sectorial, e também a análise interna das empresas no que diz respeito aos seus investimentos e perspectivas de crescimento; algoritmos de Inteligência Artificial (IA), como os algoritmos Roth-Erev e o Q-Learning de aprendizagem por reforço; uma abordagem baseada na teoria do determinismo, em que são analisadas todas as variáveis intervenientes na obtenção dos resultados pelo simulador; e outras propostas de algoritmos de aprendizagem e análise de dados específicos para determinadas situações, bem como a combinação de algoritmos de tipos diversos. Numa camada superior aos algoritmos mencionados foi implementado um mecanismo de aprendizagem por reforço, baseado em estatísticas e em probabilidades, que é responsável por escolher em cada altura a proposta de licitação que dá mais garantias de sucesso. Com o passar do tempo, vão sendo actualizadas as estatísticas, através da análise dos resultados de cada proposta. Este mecanismo permite que em cada momento sejam escolhidos os algoritmos que estão a ter os melhores resultados para cada situação e contexto. Ao serem considerados vários algoritmos, de naturezas completamente distintas, consegue-se uma maior probabilidade de haver sempre algum a oferecer bons resultados. Existe também a possibilidade de se definir as preferências e parametrizações relativas a cada algoritmo individualmente, e também de se definirem preferências relativas ao desempenho dos algoritmos no que diz respeito à eficiência computacional, permitindo que o utilizador escolha a relação eficiência/probabilidade de sucesso, de acordo com as suas preferências. O sistema excluirá então, automaticamente, os algoritmos que usualmente requerem um maior tempo de processamento, quando esse tempo não corresponde a soluções proporcionalmente melhores. Desta forma, garante-se que o sistema estará a utilizar o seu tempo de processamento em abordagens que oferecem melhores respostas no menor tempo possível. Como apoio ao funcionamento adequado das estratégias implementadas foi criado um mecanismo de definição de perfis dos agentes competidores. Desta forma é possível obter previsões acerca das acções esperadas dos outros agentes participantes no mercado, tendo em conta as suas acções passadas e as reacções verificadas quando confrontados com situações específicas, como o sucesso ou o falhanço

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

A Hybrid Simulation Framework of Consumer-to-Consumer Ecommerce Space

In the past decade, ecommerce transformed the business models of many organizations. Information Technology leveled the playing field for new participants, who were capable of causing disruptive changes in every industry. Web 2.0 or Social Web further redefined ways users enlist for services. It is now easy to be influenced to make choices of services based on recommendations of friends and popularity amongst peers. This research proposes a simulation framework to investigate how actions of stakeholders at this level of complexity affect system performance as well as the dynamics that exist between different models using concepts from the fields of operations engineering, engineering management, and multi-model simulation. Viewing this complex model from a systems perspective calls for the integration of different levels of behaviors. Complex interactions exist among stakeholders, the environment and available technology. The presence of continuous and discrete behaviors coupled with stochastic and deterministic behaviors present challenges for using standalone simulation tools to simulate the business model. We propose a framework that takes into account dynamic system complexity and risk from a hybrid paradigm. The SCOR model is employed to map the business processes and it is implemented using agent based simulation and system dynamics. By combining system dynamics at the strategy level with agent based models of consumer behaviors, an accurate yet efficient representation of the business model that makes for sound basis of decision making can be achieved to maximize stakeholders\u27 utility

Sustainable Development of Real Estate

Research, theoretical and practical tasks of sustainable real estate development process are revised in detail in this monograph; particular examples are presented as well. The concept of modern real estate development model and a developer is discussed, peculiarities of the development of built environment and real estate objects are analyzed, as well as assessment methods, models and management of real estate and investments in order to increase the object value. Theoretical and practical analyses, presented in the monograph, prove that intelligent and augmented reality technologies allow business managers to reach higher results in work quality, organize a creative team of developers, which shall present more qualitative products for the society. The edition presents knowledge on economic, legal, technological, technical, organizational, social, cultural, ethical, psychological and environmental, as well as its management aspects, which are important for the development of real estate: publicly admitted sustainable development principles, urban development and aesthetic values, territory planning, participation of society and heritage protection. It is admitted that economical crises are inevitable, and the provided methods shall help to decrease possible loss. References to the most modern world scientific literature sources are presented in the monograph. The monograph is prepared for the researchers, MSc and PhD students of construction economics and real estate development. The book may be useful for other researchers, MSc and PhD students of economics, management and other specialities, as well as business specialist of real estate business. The publication of monograph was funded by European Social Fund according to project No. VP1-2.2-ŠMM-07-K-02-060 Development and Implementation of Joint Master’s Study Programme “Sustainable Development of the Built Environment”