The significance of bidding, accepting and opponent modeling in automated negotiation

Given the growing interest in automated negotiation, the search for effective strategies has produced a variety of different negotiation agents. Despite their diversity, there is a common structure to their design. A negotiation agent comprises three key components: the bidding strategy, the opponent model and the acceptance criteria. We show that this three-component view of a negotiating architecture not only provides a useful basis for developing such agents but also provides a useful analytical tool. By combining these components in varying ways, we are able to demonstrate the contribution of each component to the overall negotiation result, and thus determine the key contributing components. Moreover, we are able to study the interaction between components and present detailed interaction effects. Furthermore, we find that the bidding strategy in particular is of critical importance to the negotiator's success and far exceeds the importance of opponent preference modeling techniques. Our results contribute to the shaping of a research agenda for negotiating agent design by providing guidelines on how agent developers can spend their time most effectively

Automated combination of bilateral energy contracts negotiation tactics

[EN] This paper addresses the theme automated bilateral negotiation of energy contracts. In this work, the automatic combination between different negotiation tactics is proposed. This combination is done dynamically throughout the negotiation process, as result from the online assessment that is performed after each proposal and counter-proposal. The proposed method is integrated in a decision support system for bilateral negotiations, called Decision Support for Energy Contracts Negotiations (DECON), which in turn is integrated with the Multi-Agent Simulator of Competitive Electricity Markets (MASCEM). This integration enables testing and validating the proposed methodology in a realistic market negotiation environment. A case study is presented, demonstrating the advantages of the proposed approach

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

Generic Methods for Adaptive Management of Service Level Agreements in Cloud Computing

The adoption of cloud computing to build and deliver application services has been nothing less than phenomenal. Service oriented systems are being built using disparate sources composed of web services, replicable datastores, messaging, monitoring and analytics functions and more. Clouds augment these systems with advanced features such as high availability, customer affinity and autoscaling on a fair pay-per-use cost model. The challenge lies in using the utility paradigm of cloud beyond its current exploit. Major trends show that multi-domain synergies are creating added-value service propositions. This raises two questions on autonomic behaviors, which are specifically ad- dressed by this thesis. The first question deals with mechanism design that brings the customer and provider(s) together in the procurement process. The purpose is that considering customer requirements for quality of service and other non functional properties, service dependencies need to be efficiently resolved and legally stipulated. The second question deals with effective management of cloud infrastructures such that commitments to customers are fulfilled and the infrastructure is optimally operated in accordance with provider policies. This thesis finds motivation in Service Level Agreements (SLAs) to answer these questions. The role of SLAs is explored as instruments to build and maintain trust in an economy where services are increasingly interdependent. The thesis takes a wholesome approach and develops generic methods to automate SLA lifecycle management, by identifying and solving relevant research problems. The methods afford adaptiveness in changing business landscape and can be localized through policy based controls. A thematic vision that emerges from this work is that business models, services and the delivery technology are in- dependent concepts that can be finely knitted together by SLAs. Experimental evaluations support the message of this thesis, that exploiting SLAs as foundations for market innovation and infrastructure governance indeed holds win-win opportunities for both cloud customers and cloud providers

What to bid and when to stop

Negotiation is an important activity in human society, and is studied by various disciplines, ranging from economics and game theory, to electronic commerce, social psychology, and artificial intelligence. Traditionally, negotiation is a necessary, but also time-consuming and expensive activity. Therefore, in the last decades there has been a large interest in the automation of negotiation, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents eventually being able to negotiate on behalf of human negotiators.Every year, automated negotiation agents are improving in various ways, and there is now a large body of negotiation strategies available, all with their unique strengths and weaknesses. For example, some agents are able to predict the opponent's preferences very well, while others focus more on having a sophisticated bidding strategy. The problem however, is that there is little incremental improvement in agent design, as the agents are tested in varying negotiation settings, using a diverse set of performance measures. This makes it very difficult to meaningfully compare the agents, let alone their underlying techniques. As a result, we lack a reliable way to pinpoint the most effective components in a negotiating agent.There are two major advantages of distinguishing between the different components of a negotiating agent's strategy: first, it allows the study of the behavior and performance of the components in isolation. For example, it becomes possible to compare the preference learning component of all agents, and to identify the best among them. Second, we can proceed to mix and match different components to create new negotiation strategies., e.g.: replacing the preference learning technique of an agent and then examining whether this makes a difference. Such a procedure enables us to combine the individual components to systematically explore the space of possible negotiation strategies.To develop a compositional approach to evaluate and combine the components, we identify structure in most agent designs by introducing the BOA architecture, in which we can develop and integrate the different components of a negotiating agent. We identify three main components of a general negotiation strategy; namely a bidding strategy (B), possibly an opponent model (O), and an acceptance strategy (A). The bidding strategy considers what concessions it deems appropriate given its own preferences, and takes the opponent into account by using an opponent model. The acceptance strategy decides whether offers proposed by the opponent should be accepted.The BOA architecture is integrated into a generic negotiation environment called Genius, which is a software environment for designing and evaluating negotiation strategies. To explore the negotiation strategy space of the negotiation research community, we amend the Genius repository with various existing agents and scenarios from literature. Additionally, we organize a yearly international negotiation competition (ANAC) to harvest even more strategies and scenarios. ANAC also acts as an evaluation tool for negotiation strategies, and encourages the design of negotiation strategies and scenarios.We re-implement agents from literature and ANAC and decouple them to fit into the BOA architecture without introducing any changes in their behavior. For each of the three components, we manage to find and analyze the best ones for specific cases, as described below. We show that the BOA framework leads to significant improvements in agent design by wining ANAC 2013, which had 19 participating teams from 8 international institutions, with an agent that is designed using the BOA framework and is informed by a preliminary analysis of the different components.In every negotiation, one of the negotiating parties must accept an offer to reach an agreement. Therefore, it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When contemplating whether to accept an offer, the agent is faced with the acceptance dilemma: accepting the offer may be suboptimal, as better offers may still be presented before time runs out. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. We classify and compare state-of-the-art generic acceptance conditions. We propose new acceptance strategies and we demonstrate that they outperform the other conditions. We also provide insight into why some conditions work better than others and investigate correlations between the properties of the negotiation scenario and the efficacy of acceptance conditions.Later, we adopt a more principled approach by applying optimal stopping theory to calculate the optimal decision on the acceptance of an offer. We approach the decision of whether to accept as a sequential decision problem, by modeling the bids received as a stochastic process. We determine the optimal acceptance policies for particular opponent classes and we present an approach to estimate the expected range of offers when the type of opponent is unknown. We show that the proposed approach is able to find the optimal time to accept, and improves upon all existing acceptance strategies.Another principal component of a negotiating agent's strategy is its ability to take the opponent's preferences into account. The quality of an opponent model can be measured in two different ways. One is to use the agent's performance as a benchmark for the model's quality. We evaluate and compare the performance of a selection of state-of-the-art opponent modeling techniques in negotiation. We provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. We identify a class of simple and surprisingly effective opponent modeling techniques that did not receive much previous attention in literature.The other way to measure the quality of an opponent model is to directly evaluate its accuracy by using similarity measures. We review all methods to measure the accuracy of an opponent model and we then analyze how changes in accuracy translate into performance differences. Moreover, we pinpoint the best predictors for good performance. This leads to new insights concerning how to construct an opponent model, and what we need to measure when optimizing performance.Finally, we take two different approaches to gain more insight into effective bidding strategies. We present a new classification method for negotiation strategies, based on their pattern of concession making against different kinds of opponents. We apply this technique to classify some well-known negotiating strategies, and we formulate guidelines on how agents should bid in order to be successful, which gives insight into the bidding strategy space of negotiating agents. Furthermore, we apply optimal stopping theory again, this time to find the concessions that maximize utility for the bidder against particular opponents. We show there is an interesting connection between optimal bidding and optimal acceptance strategies, in the sense that they are mirrored versions of each other.Lastly, after analyzing all components separately, we put the pieces back together again. We take all BOA components accumulated so far, including the best ones, and combine them all together to explore the space of negotiation strategies.We compute the contribution of each component to the overall negotiation result, and we study the interaction between components. We find that combining the best agent components indeed makes the strongest agents. This shows that the component-based view of the BOA architecture not only provides a useful basis for developing negotiating agents but also provides a useful analytical tool. By varying the BOA components we are able to demonstrate the contribution of each component to the negotiation result, and thus analyze the significance of each. The bidding strategy is by far the most important to consider, followed by the acceptance conditions and finally followed by the opponent model.Our results validate the analytical approach of the BOA framework to first optimize the individual components, and then to recombine them into a negotiating agent

Algorithm Selection in Multimodal Medical Image Registration

Medical image acquisition technology has improved significantly throughout the last several decades, and clinicians now rely on medical images to diagnose illnesses, and to determine treatment protocols, and surgical planning. Medical images have been divided by researchers into two types of structures: functional and anatomical. Anatomical imaging, such as magnetic resonance imaging (MRI), computed tomography imaging (C.T.), ultrasound, and other systems, enables medical personnel to examine a body internally with great accuracy, thereby avoiding the risks associated with exploratory surgery. Functional (or physiological) imaging systems contain single-photon emission computed tomography (SPECT), positron emission tomography (PET), and other methods, which refer to a medical imaging system for discovering or evaluating variations in absorption, blood flow, metabolism, and regional chemical composition. Notably, one of these medical imaging models alone cannot usually supply doctors with adequate information. Additionally, data obtained from several images of the same subject generally provide complementary information via a process called medical image registration. Image registration may be defined as the process of geometrically mapping one -image’s coordinate system to the coordinate system of another image acquired from a different perspective and with a different sensor. Registration performs a crucial role in medical image assessment because it helps clinicians observe the developing trend of the disease and make proper measures accordingly. Medical image registration (MIR) has several applications: radiation therapy, tumour diagnosis and recognition, template atlas application, and surgical guidance system. There are two types of registration: manual registration and registration-based computer system. Manual registration is when the radiologist /physician completes all registration tasks interactively with visual feedback provided by the computer system, which can result in serious problems. For instance, investigations conducted by two experts are not identical, and registration correctness is determined by the user's assessment of the relationship between anatomical features. Furthermore, it may take a long time for the user to achieve proper alignment, and the outcomes vary according to the user. As a result, the outcomes of manual alignment are doubtful and unreliable. The second registration approach is computer-based multimodal medical image registration that targets various medical images, and an arraof application types. . Additionally, automatic registration in medical pictures matches the standard recognized characteristics or voxels in pre- and intra-operative imaging without user input. Registration of multimodal pictures is the initial step in integrating data from several images. Automatic image processing has emerged to mitigate (Husein, do you mean “mitigate” or “improve”?) the manual image registration reliability, robustness, accuracy, and processing time. While such registration algorithms offer advantages when applied to some medical images, their use with others is accompanied by disadvantages. No registration technique can outperform all input datasets due to the changeability of medical imaging and the diverse demands of applications. However, no algorithm is preferable under all possible conditions; given many available algorithms, choosing the one that adapts the best to the task is vital. The essential factor is to choose which method is most appropriate for the situation. The Algorithm Selection Problem has emerged in numerous research disciplines, including medical diagnosis, machine learning, optimization, and computations. The choice of the most powerful strategy for a particular issue seeks to minimize these issues. This study delivers a universal and practical framework for multimodal registration algorithm choice. The primary goal of this study is to introduce a generic structure for constructing a medical image registration system capable of selecting the best registration process from a range of registration algorithms for various used datasets. Three strategies were constructed to examine the framework that was created. The first strategy is based on transforming the problem of algorithm selection into a classification problem. The second strategy investigates the effect of various parameters, such as optimization control points, on the optimal selection. The third strategy establishes a framework for choosing the optimal registration algorithm for a delivered dataset based on two primary criteria: registration algorithm applicability, and performance measures. The approach mentioned in this section has relied on machine learning methods and artificial neural networks to determine which candidate is most promising. Several experiments and scenarios have been conducted, and the results reveal that the novel Framework strategy leads to achieving the best performance, such as high accuracy, reliability, robustness, efficiency, and low processing time

Contributions to Service Level Agreement (SLA), Negotiation and Monitoring in Cloud Computing

Cloud computing is a dynamic field of research, as the latest advances in the cloud computing applications have led to development of a plethora of cloud services in the areas of software, hardware, storage, internet of things connected to the cloud, and 5G supported by the cloud networks. Due to ever increasing developments and the subsequent emergence of a wide range of cloud services, a cloud market was created with cloud providers and customers seeking to buy the cloud services. With the expansion of the cloud market and the presence of a virtual environment in which cloud services are provided and managed, the face to-face meetings between customers and cloud providers is almost impossible, and the negotiation over the cloud services using the state-of-the-art autonomous negotiation agents has been theorized and researched by several researchers in the field of cloud computing, however, the solutions offered by literature are less applicable in the real-time cloud market with the evolving nature of services and customers’ requirements. Therefore, this study aimed to develop the solutions addressing issues in relation to negotiation of cloud services leading to the development of a service-level agreement (SLA), and monitoring of the terms and conditions specified in the SLA. We proposed the autonomous service-level framework supported by the autonomous agents for negotiating over the cloud services on behalf of the cloud providers and customers. The proposed framework contained gathering, filtering, negotiation and SLA monitoring functions, which enhanced its applicability in the real-time cloud market environment. Gathering and filtering stages facilitated the effectiveness of the negotiation phase based on the requirements of customers and cloud services available in the cloud market. The negotiation phase was executed by the selection of autonomous agents, leading to the creation of an SLA with metrics agreed upon between the cloud provider and the customer. Autonomous agents improved the efficiency of negotiation over multiple issues by creating the SLA within a short time and benefiting both parties involved in the negation phase. Rubinstein’s Alternating Offers Protocol was found to be effective in drafting the automated SLA solutions in the challenging environment of the cloud market. We also aimed to apply various autonomous agents to build the new algorithms which can be used to create novel negotiation strategies for addressing the issues in SLAs in cloud computing. The monitoring approach based on the CloudSim tool was found to be an effective strategy for detecting violations against the SLA, which can be an important contribution to building effective monitoring solutions for improving the quality of services in the cloud market