WOF: Towards behavior analysis and representation of emotions in adaptive systems

Abstract

With the increasing use of new technologies such as Communicating Objects (COT) and the Internet of Things (IoT) in our daily life (connected objects, mobile devices, etc.), designing Intelligent Adaptive Distributed software Systems (DIASs) has become an important research issue. Human face the problem of mastering the complexity and sophistication of such systems as those require an important cognitive load for end-users who usually are not expert. Starting from the principle that it is to technology-based systems to adapt to end-users and not the reverse, we address the issue of how to help developers design and produce such systems. We then propose WOF, an object oriented Framework founded on the concept of Wise Object (WO), a metaphor to refer to human introspection and learning capabilities. To make systems able to learn by themselves, we designed introspection, monitoring and analysis software mechanisms such that WOs can learn and construct their own knowledge. We then define a WO as a software-based entity able to learn by itself on itself (i.e. on services it is intended to provide) and also on the others (i.e. the way others use its services). A WO is seen as an avatar of either a physical or a logical object (e.g. device/software component). In this paper, we introduce the main requirements for DIASs as well as the design principles of WOF. We detail the WOF conceptual architecture and the Java implementation we built for it. To provide application developers with relevant support, we designed WOF with the minimum intrusion in the application source code. Adaptation and distribution related mechanisms defined in WOF can be inherited by application classes. In our Java implementation of WOF, object classes produced by a developer inherit the behavior of Wise Object (WO) class. An instantiated system is a Wise Object System (WOS) composed of WOs that interact through an event bus. In the first version of WOF, a WO was able to use introspection and monitoring built-in mechanisms to construct knowledge on: (a) services it is intended to render; (b) the usage done of its services. In the current version, we integrated an event-based WO simulator and a set of Analyzer classes to provide a WO with the possibility to use different analysis models and methods on its data. Our major goal is that a WO can be able to identify common usage of its services and to detect unusual usage. We use the metaphor of emotions to refer to unusual behavior (stress, surprise, etc.). We show in the paper a first experiment based on a statistical analysis method founded on stationary processes to identify usual/unusual behavior.