RBUIS: simplifying enterprise application user interfaces through engineering role-based adaptive behavior

Enterprise applications such as customer relationship management (CRM) and enterprise resource planning (ERP) are very large scale, encompassing millions of lines-of-code and thousands of user interfaces (UI). These applications have to be sold as feature-bloated off-the-shelf products to be used by people with diverse needs in required feature-set and layout preferences based on aspects such as skills, culture, etc. Although several approaches have been proposed for adapting UIs to various contexts-of-use, little work has focused on simplifying enterprise application UIs through engineering adaptive behavior. We define UI simplification as a mechanism for increasing usability through adaptive behavior by providing users with a minimal feature-set and an optimal layout based on the context-of-use. In this paper we present Role-Based UI Simplification (RBUIS), a tool supported approach based on our CEDAR architecture for simplifying enterprise application UIs through engineering role-based adaptive behavior. RBUIS is integrated in our general-purpose platform for developing adaptive model-driven enterprise UIs. Our approach is validated from the technical and end-user perspectives by applying it to developing a prototype enterprise application and user-testing the outcome

Engineering adaptive user interfaces for enterprise applications

The user interface (UI) layer is considered an important component in software applications since it links the users to the software’s functionality. Enterprise applications such as enterprise resource planning and customer relationship management systems have very complex UIs that are used by users with diverse needs in terms of the required features and layout preferences. The inability to cater for the variety of user needs diminishes the usability of these applications. One way to cater for those needs is through adaptive UIs. Some enterprise software providers offer mechanisms for tailoring UIs based on the variable user needs, yet those are not generic enough to be used with other applications and require maintaining multiple UI copies manually. A generic platform based on a model-driven approach could be more reusable since operating on the model level makes it technology independent. The main objective of this research is devising a generic, scalable, and extensible platform for building adaptive enterprise application UIs based on a runtime model-driven approach. This platform primarily targets UI simplification, which we defined as a mechanism for increasing usability through adaptive behavior by providing users with a minimal feature-set and an optimal layout based on the context-of-use. This paper provides an overview of the research questions and methodology, the results that were achieved so far, and the remaining work

Reinforcement Learning through Supervision for Autonomous Agents

Abstract Reinforcement Learning (RL) is a class of model-free learning control methods that can solve Markov Decision Process (MDP) problems. However, one difficulty for the application of RL control is its slow convergence, especially in MDPs with continuous state space. In this paper, a modified structure of RL is proposed to accelerate reinforcement learning control. This approach combines supervision technique with the standard Qlearning algorithm of reinforcement learning. The a priori information is provided to the RL learning agent by a direct integration of a human operator commands (a.k.a. human advices) or by an optimal LQ-controller, indicating preferred actions in some particular situations. It is shown that the convergence speed of the supervised RL agent is greatly improved compared to the conventional Q-Learning algorithm. Simulation work and results on the cart-pole balancing problem and learning navigation tasks in unknown grid world with obstacles are given to illustrate the efficiency of the proposed method

Engineering Adaptive Model-Driven User Interfaces for Enterprise Applications

Enterprise applications such as enterprise resource planning systems have numerous complex user interfaces (UIs). Usability problems plague these UIs because they are offered as a generic off-the-shelf solution to end-users with diverse needs in terms of their required features and layout preferences. Adaptive UIs can help in improving usability by tailoring the features and layout based on the context-of-use. The model-driven UI development approach offers the possibility of applying different types of adaptations on the various UI levels of abstraction. This approach forms the basis for many works researching the development of adaptive UIs. Yet, several gaps were identified in the state-of-the-art adaptive model-driven UI development systems. To fill these gaps, this thesis presents an approach that offers the following novel contributions: - The Cedar Architecture serves as a reference for developing adaptive model-driven enterprise application user interfaces. - Role-Based User Interface Simplification (RBUIS) is a mechanism for improving usability through adaptive behavior, by providing end-users with a minimal feature-set and an optimal layout based on the context-of-use. - Cedar Studio is an integrated development environment, which provides tool support for building adaptive model-driven enterprise application UIs using RBUIS based on the Cedar Architecture. The contributions were evaluated from the technical and human perspectives. Several metrics were established and applied to measure the technical characteristics of the proposed approach after integrating it into an open-source enterprise application. Additional insights about the approach were obtained through the opinions of industry experts and data from real-life projects. Usability studies showed the approach’s ability to significantly improve usability in terms of end-user efficiency, effectiveness and satisfaction

Reply to Dario Floreano's 'Engineering Adaptive Behavior'

Special issue on Complete Agent Learning in Complex Environments, M.J. Mataric (Ed.)info:eu-repo/semantics/publishe

Reply to Dario Floreano's "Engineering Adaptive Behavior"

ociator which can only learn linearly separable functions, while ALECSYS is a more general system which does not have this limit: a possible lower speed of ALECSYS is counterbalanced by its greater generality. Second, ALECSYS learns by reinforcement learning, while Nehmzow and McGonigle's pattern associator learns by supervised learning. Again, ALECSYS is more general, and also more indicated for robotics applications, where labeled training pairs are difficult if not impossible to provide. In fact, although Nehmzow and McGonigle devised a clever way to automatically generate training pairs, their approach is feasible only if the number of possible actions for each input pattern is very small, as it is the case in their experiments (they have three possible actions, as opposed to the sixteen we use in most of our experiments; obviously, the greater the number of actions, the smoother the resulting movement of the robot).

Cedar: Engineering Role-Based Adaptive User Interfaces for Enterprise Applications

Feature-bloated enterprise applications such as customer relationship management (CRM) and enterprise resource planning (ERP) are very large scale, encompassing millions of lines-of-code and thousands of user interfaces (UI). Also, these applications are sold as generic off-the-shelf products to be used by people with diverse needs in required feature-set and backgrounds such as skills, culture, etc. Although several approaches have been proposed for adapting UIs to various user profiles, little work has focused on simplifying enterprise application UIs through engineering adaptive behavior. We define UI simplification as a mechanism for increasing usability through adaptive behavior by providing users with a minimal feature-set and an optimal layout based on their individual profile. In this paper we present Role-Based UI Simplification (RBUIS), a tool supported approach based on our CEDAR architecture for simplifying enterprise application UIs through engineering role-based adaptive behavior. RBUIS is integrated in our generic platform for developing adaptive model-driven enterprise UIs. Our approach is validated from the technical and end-user perspectives by applying it to developing a prototype enterprise application and user-testing the outcome