HEARTDROID—Rule engine for mobile and context‐aware expert systems

Abstract

Building mobile context‐aware systems is inherently complex and non‐trivial task. It consists of several phases starting from acquisition of context, through modeling to execution of contextual models. Today, such systems are mostly implemented on mobile platforms, that introduce specific requirements, such as intelligibility, robustness, privacy, and efficiency. Over the last decade, along with the rapid development of mobile industry, many approaches were developed that unevenly support these requirements. This is mainly caused by the fact that current modelling and reasoning methods are not crafted to operate in mobile environments. We argue that the use of rule‐based reasoning tailored to mobile environments is an optimal solution. Rules are based on symbolic knowledge representation, as such they meet the general tendency to enforce understandability, intelligibility, and controllability of artificial intelligence software, as stated in the recent European Union General Data Protection Regulation. To this goal, we introduce a lightweight rule engine dedicated for Android platform called HEARTDROID. It executes models in the HMR+ rule language that are capable of expressing uncertainty of knowledge, capturing dynamics of mobile environment and provide high level of intelligibility. We present a qualitative and quantitative comparison of HEARTDROID with the most popular rule engines available.

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

“…However, the development of large-scale rule-based systems may face numerous challenges. For instance, the reasoning process can be very complex, and designing of such systems becomes hard to manage [133]. There is still a lack of lightweight rule-based inference engines that will allow for reasoning on mobile devices [133].…”

“…For instance, the reasoning process can be very complex, and designing of such systems becomes hard to manage [133]. There is still a lack of lightweight rule-based inference engines that will allow for reasoning on mobile devices [133]. Thus a set of concise and effective rules will be beneficial in terms of outcome and simplicity for such a rule-based expert system for mobile devices.…”

Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis

“…It uses the SPARQL Event Processing Architecture (SEPA) to perform SPARQL updates and queries on the underlying RDF graph, enabling to monitor the daily behaviour of people that face problems in home settings due to ageing or illnesses. HEARTDROID [ 44 ] uses a rule inference engine for Android mobile devices. It allows the definition of semantic annotations of its components in order to increase intelligence and transparency of the model and the reasoning services.…”

Analysis and Use of the Emotional Context with Wearable Devices for Games and Intelligent Assistants

“…We have demonstrated that it is straightforward to include emotional contexts in AmI systems built on top of HMR+ and HeaRTDroid [65], mainly due to the modularized nature of HMR+ models, which can be easily extended with additional knowledge. Figure 12 presents a simple HMR+ model for adapting a mobile phone ringtone to a temporal context; this example came from the tutorial for HeaRTDroid, and originally did not include the emotional context table.…”