Szymon Bobek

HalVA - rule analysis framework for XTT2 rules

Quality and reliability issues are important in development and exploration of rule-based systems. In the paper a formalized knowledge representation for rules called XTT2 is considered. It is a rule language based on an expressive attribute logic called ALSV(FD). A custom runtime and verification framework for XTT2 called HalVA is proposed. It allows for verification of certain formal properties of rules, including determinism, subsumption or completeness.

Algorithms for rule inference in modularized rule bases

In the paper an extended knowledge representation for rules is considered. It is called Extended Tabular Trees (XTT2) and it provides a network of decision units grouping rules working in the same context. The units are linked into an inference network, where a number of inference options are considered. The original contribution of the paper is the proposal and formalization of several different inference algorithms working on the same rule base. Such an approach allows for a more flexible rule design and deployment, since the same knowledge base may be used in different ways, depending on the application.

How to Reason by HeaRT in a Semantic Knowledge-Based Wiki

Semantic wikis constitute an increasingly popular class of systems for collaborative knowledge engineering. We developed Loki, a semantic wiki that uses a logic-based knowledge representation. It is compatible with semantic annotations mechanism as well as Semantic Web languages. We integrated the system with a rule engine called Heart that supports inference with production rules. Several modes for modularized rule bases, suitable for the distributed rule bases present in a wiki, are considered. Embedding the rule engine enables strong reasoning and allows to run production rules over semantic knowledge bases. In the paper, we demonstrate the system concepts and functionality using an illustrative example.

Mobile context-based framework for threat monitoring in urban environment with social threat monitor

Engaging users in threat reporting is important in order to improve threat monitoring in urban environments. Today, mobile applications are mostly used to provide basic reporting interfaces. With a rapid evolution of mobile devices, the idea of context awareness has gained a remarkable popularity in recent years. Modern smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows for real-time processing of data gathered by their sensors. Universal access to the Internet via WiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Although there exist numerous frameworks for context-aware systems, they are usually dedicated to static, centralized, client-server architectures. There is still space for research in the field of context modeling and reasoning for mobile devices. In this paper, we propose a lightweight context-aware framework for mobile devices that uses data gathered by mobile device sensors and performs on-line reasoning about possible threats based on the information provided by the Social Threat Monitor system developed in the INDECT project.

Capturing Dynamics of Mobile Context-Aware Systems with Rules and Statistical Analysis of Historical Data

Mobile context-aware systems gained huge popularity in recent years due to the rapid evolution of personal mobile devices. Nowadays smartphones are equipped with a variety of sensors that allow for on-line monitoring of user context and reasoning upon it. Contextual information in such systems is very dynamic. It changes rapidly and these changes may have impact on system behaviour. Although there are many machine learning methods like Markov models that allow to handle such dynamics, they do not provide intelligibility features that rule-based systems do. In this paper we propose an extension to XTT2 rule representation that allows for modelling dynamics of the mobile context-aware systems using rules and statistical analysis of historical data. This was achieved by introducing time-based operators to rule conditions and statistical operators to right hand side of the rules.

Incomplete and Uncertain Data Handling in Context-Aware Rule-Based Systems with Modified Certainty Factors Algebra

Context-aware systems make use of contextual information to adapt their functionality to current environment state, or user needs and habits. One of the major problems concerning them is the fact, that there is no warranty that the contextual information will be available, nor certain at the time when the reasoning should be performed. This may be due to measurement errors, sensor inaccuracy, or semantic ambiguities of modeled concepts. Several approaches were developed to solve uncertainty in context knowledge bases, including probabilistic reasoning, fuzzy logic, or certainty factors. However, handling uncertainties in highly dynamic, mobile environments still requires more consideration. In this paper we perform comparison of application of different uncertainty modeling approaches to mobile context-aware environments. We also present an exemplary solution based on modified certainty factors algebra and logic-based knowledge representation for solving uncertainties caused by the imprecision of context-providers.

Challenges for Migration of Rule-Based Reasoning Engine to a Mobile Platform

Research in the area of context-awareness has recently been revolutionized by the rapid development of mobile devices like smart phones and tablets, which became omnipresent in daily human life. Such devices are valuable sources of information about their user location, physical and social activity, profiles and habits, etc. However, the information that can be obtained is not limited to the hardware sensors that the device is equipped with, but can be extended to every sensor that is available in a communication range of a device. Although the concept of multiple sensors and devices, exchanging information and working together as one big pervasive system is not new, there is still a lot of research that has to be done to allow building such systems efficiently. In this paper the prototype of a rule-based inference engine for mobile devices is described and evaluated. The most important challenges connected with migration from desktop to mobile environment were defined, and a comparison of Prolog-based platforms, as a portable environments for mobile context-aware systems were presented. We consider implementation using a portable Prolog compiler on Android platform.

Mobile Context-Based Framework for Monitoring Threats in Urban Environment

With a rapid evolution of mobile devices, the idea of context awareness has gained a remarkable popularity in recent years. Modern smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows real-time processing of data gathered by their sensors. Universal access to the Internet via WiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Although there exist numerous frameworks for context-aware systems, they are usually dedicated to static, centralized, client-server architectures. There is still space for research in a field of context modeling and reasoning for mobile devices. In this paper, we propose a lightweight context-aware framework for mobile devices that uses data gathered by mobile device sensors and perform on-line reasoning about possible threats, based on the information provided by the Social Threat Monitor system developed in the INDECT project.

Compact Representation of Conditional Probability for Rule-Based Mobile Context-Aware Systems

Context-aware systems gained huge popularity in recent years due to rapid evolution of personal mobile devices. Equipped with variety of sensors, such devices are sources of a lot of valuable information that allows the system to act in an intelligent way. However, the certainty and presence of this information may depend on many factors like measurement accuracy or sensor availability. Such a dynamic nature of information may cause the system not to work properly or not to work at all. To allow for robustness of the context-aware system an uncertainty handling mechanism should be provided with it. Several approaches were developed to solve uncertainty in context knowledge bases, including probabilistic reasoning, fuzzy logic, or certainty factors. In this paper, we present a representation method that combines strengths of rules based on the attributive logic and Bayesian networks. Such a combination allows efficiently encode conditional probability distribution of random variables into a reasoning structure called XTT2. This provides a method for building hybrid context-aware systems that allows for robust inference in uncertain knowledge bases.

Indoor microlocation with BLE beacons and incremental rule learning

Location is one of the most valuable and extensively used information in mobile context-aware systems. Its understanding may vary from geolocation that uses GPS infrastructure to locate objects on Earth, up to microlocation, which aims at locating users and objects inside closed areas. Although geolocation can be considered as a mature field, there is an ongoing research in the area of microlocation. Despite that, microlocation techniques do not offer satisfactory level of accuracy and implementation flexibility to be practically incorporated into commercial solutions. This is mainly because of high workload that needs to be done in terms of maps preparation and algorithms tuning. In this paper we present a method that can overcome this issue by providing incremental rule learning algorithm for automated discovery of user location on a room-level accuracy. We also show a method of augmenting semantic annotations on physical objects with a use of Bluetooth Low Energy beacons.