An Adaptive Interface for Customer Transaction Assistant in Electronic Commerce

Personalized service and adaptive interface play important factors in electronic commerce. This work proposes an adaptive interface to for helping the customer transaction in electronic commerce. The adaptive interface collects the consumer behaviors by monitoring the customer operations, excluding unnecessary operations, and recognizing the behavior patterns. The interface uses the Bayesian belief network and the RBF neural networks to achieve the above tasks. The interface then evaluates knowledge and skill proficiency according to the customer behavior patterns. Finally, the interface generates the adaptive interface to the consumers for helping the transaction process

An intelligent content prefix classification approach for quality of service optimization in information-centric networking

This research proposes an intelligent classification framework for quality of service (QoS) performance improvement in information-centric networking (ICN). The proposal works towards keyword classification techniques to obtain the most valuable information via suitable content prefixes in ICN. In this study, we have achieved the intelligent function using Artificial Intelligence (AI) implementation. Particularly, to find the most suitable and promising intelligent approach for maintaining QoS matrices, we have evaluated various AI algorithms, including evolutionary algorithms (EA), swarm intelligence (SI), and machine learning (ML) by using the cost function to assess their classification performances. With the goal of enabling a complete ICN prefix classification solution, we also propose a hybrid implementation to optimize classification performances by integration of relevant AI algorithms. This hybrid mechanism searches for a final minimum structure to prevent the local optima from happening. By simulation, the evaluation results show that the proposal outperforms EA and ML in terms of network resource utilization and response delay for QoS performance optimization

MARLUI: Multi-Agent Reinforcement Learning for Adaptive UIs

Adaptive user interfaces (UIs) automatically change an interface to better support users' tasks. Recently, machine learning techniques have enabled the transition to more powerful and complex adaptive UIs. However, a core challenge for adaptive user interfaces is the reliance on high-quality user data that has to be collected offline for each task. We formulate UI adaptation as a multi-agent reinforcement learning problem to overcome this challenge. In our formulation, a user agent mimics a real user and learns to interact with a UI. Simultaneously, an interface agent learns UI adaptations to maximize the user agent's performance. The interface agent learns the task structure from the user agent's behavior and, based on that, can support the user agent in completing its task. Our method produces adaptation policies that are learned in simulation only and, therefore, does not need real user data. Our experiments show that learned policies generalize to real users and achieve on par performance with data-driven supervised learning baselines

Generating Effective Recommendations Using Viewing-Time Weighted Preferences for Attributes

Recommender systems are an increasingly important technology and researchers have recently argued for incorporating different kinds of data to improve recommendation quality. This paper presents a novel approach to generating recommendations and evaluates its effectiveness. First, we review evidence that item viewing time can reveal user preferences for items. Second, we model item preference as a weighted function of preferences for item attributes. We then propose a method for generating recommendations based on these two propositions. The results of a laboratory evaluation show that the proposed approach generated estimated item ratings consistent with explicit item ratings and assigned high ratings to products that reflect revealed preferences of users. We conclude by discussing implications and identifying areas for future research

APPLICATION OF SWARM AND REINFORCEMENT LEARNING TECHNIQUES TO REQUIREMENTS TRACING

Today, software has become deeply woven into the fabric of our lives. The quality of the software we depend on needs to be ensured at every phase of the Software Development Life Cycle (SDLC). An analyst uses the requirements engineering process to gather and analyze system requirements in the early stages of the SDLC. An undetected problem at the beginning of the project can carry all the way through to the deployed product. The Requirements Traceability Matrix (RTM) serves as a tool to demonstrate how requirements are addressed by the design and implementation elements throughout the entire software development lifecycle. Creating an RTM matrix by hand is an arduous task. Manual generation of an RTM can be an error prone process as well. As the size of the requirements and design document collection grows, it becomes more challenging to ensure proper coverage of the requirements by the design elements, i.e., assure that every requirement is addressed by at least one design element. The techniques used by the existing requirements tracing tools take into account only the content of the documents to establish possible links. We expect that if we take into account the relative order of the text around the common terms within the inspected documents, we may discover candidate links with a higher accuracy. The aim of this research is to demonstrate how we can apply machine learning algorithms to software requirements engineering problems. This work addresses the problem of requirements tracing by viewing it in light of the Ant Colony Optimization (ACO) algorithm and a reinforcement learning algorithm. By treating the documents as the starting (nest) and ending points (sugar piles) of a path and the terms used in the documents as connecting nodes, a possible link can be established and strengthened by attracting more agents (ants) onto a path between the two documents by using pheromone deposits. The results of the work show that ACO and RL can successfully establish links between two sets of documents

Robot Learning from Human Demonstrations for Human-Robot Synergy

Human-robot synergy enables new developments in industrial and assistive robotics research. In recent years, collaborative robots can work together with humans to perform a task, while sharing the same workplace. However, the teachability of robots is a crucial factor, in order to establish the role of robots as human teammates. Robots require certain abilities, such as easily learning diversified tasks and adapting to unpredicted events. The most feasible method, which currently utilizes human teammate to teach robots how to perform a task, is the Robot Learning from Demonstrations (RLfD). The goal of this method is to allow non-expert users to a programa a robot by simply guiding the robot through a task. The focus of this thesis is on the development of a novel framework for Robot Learning from Demonstrations that enhances the robotsa abilities to learn and perform the sequences of actions for object manipulation tasks (high-level learning) and, simultaneously, learn and adapt the necessary trajectories for object manipulation (low-level learning). A method that automatically segments demonstrated tasks into sequences of actions is developed in this thesis. Subsequently, the generated sequences of actions are employed by a Reinforcement Learning (RL) from human demonstration approach to enable high-level robot learning. The low-level robot learning consists of a novel method that selects similar demonstrations (in case of multiple demonstrations of a task) and the Gaussian Mixture Model (GMM) method. The developed robot learning framework allows learning from single and multiple demonstrations. As soon as the robot has the knowledge of a demonstrated task, it can perform the task in cooperation with the human. However, the need for adaptation of the learned knowledge may arise during the human-robot synergy. Firstly, Interactive Reinforcement Learning (IRL) is employed as a decision support method to predict the sequence of actions in real-time, to keep the human in the loop and to enable learning the usera s preferences. Subsequently, a novel method that modifies the learned Gaussian Mixture Model (m-GMM) is developed in this thesis. This method allows the robot to cope with changes in the environment, such as objects placed in a different from the demonstrated pose or obstacles, which may be introduced by the human teammate. The modified Gaussian Mixture Model is further used by the Gaussian Mixture Regression (GMR) to generate a trajectory, which can efficiently control the robot. The developed framework for Robot Learning from Demonstrations was evaluated in two different robotic platforms: a dual-arm industrial robot and an assistive robotic manipulator. For both robotic platforms, small studies were performed for industrial and assistive manipulation tasks, respectively. Several Human-Robot Interaction (HRI) methods, such as kinesthetic teaching, gamepad or a hands-freea via head gestures, were used to provide the robot demonstrations. The a hands-freea HRI enables individuals with severe motor impairments to provide a demonstration of an assistive task. The experimental results demonstrate the potential of the developed robot learning framework to enable continuous humana robot synergy in industrial and assistive applications

Systèmes interactifs auto-adaptatifs par systèmes multi-agents auto-organisateurs : application à la personnalisation de l'accès à l'information

Les réseaux de systèmes d'information tendent à devenir de plus en plus complexes en raison de leur hétérogénéité, de leur dynamique et de leur croissance permanente. Afin de gérer cette complexité et ces problèmes de surcharge informationnelle, les moteurs de recherche actuels s'appuient sur la notion de profil usager qui représente les centres d'intérêts, les préférences et les besoins d'un individu. Or, ces techniques dérivées de la recherche d'information et de l'apprentissage artificiel ne proposent pas de solution réellement adaptative pour la prise en compte de l'aspect évolutif du profil et le respect de la vie privée de l'utilisateur. Nous proposons d'exploiter le paradigme des systèmes multi-agents, et plus spécifiquement l'approche par AMAS (Adaptive Multi-Agent System), pour apporter une solution distribuée à la personnalisation et à l'adaptation des services offerts aux utilisateurs. Nos contributions portent tout d'abord sur l'évaluation adaptative et personnalisée du feedback implicite de l'utilisateur, puis sur la construction adaptative de son profil à partir de documents textuels représentant ses intérêts. Elles proposent également une plateforme nommée SWAPP dédiée à la recherche d'information personnalisée sur le Web. Ce cadre applicatif a permis d'expérimenter nos deux premières contributions individuellement, puis conjointement. Cette évaluation simultanée a mis en évidence un nouveau problème théorique : le couplage de deux AMAS conçus de manière totalement indépendante. Ce travail propose ainsi une première approche pour la conception de systèmes de systèmes auto-adaptatifs.Networks of information systems are becoming more and more complex due to their heterogeneity, their dynamics and their continuous growing. In order to cope with this information overload and this complexity, nowadays search engines make use of the notion of user profile that aim to model main interests, preferences and user's needs. Nevertheless, these techniques, derived from information retrieval and artificial learning research field, does not represent truly adaptive solutions able to cope with user profiles evolutions and to ensure user's privacy. Faced to these challenges, we propose to use the multi-agent system paradigm, and more specifically the AMAS approach (Adaptive Multi-Agent System), in order to provide a distributed solution for the personalisation and the adaptation of services and information access. Our first contribution consists in the adaptive and personalised evaluation of user implicit feedback. The second contribution studies the adaptive modelling of user profile from textual documents that represents its interests. We also propose the SWAPP platform which is dedicated to the evaluation of our approach to the web personalised information retrieval. After the individual experimentation and validation of these two first contributions within this applicative framework, they have been evaluated together. This last evaluation underlined a new theoretical problem : the coupling of two AMAS that were independently designed. Thus, this study proposes a first approach for the design of systems of self-adaptive systems