Igor Wojnicki

Advanced street lighting control

Design and control of outdoor lighting systems is a complex task, which is made even more difficult by introducing features like dynamic, sensor-based operation, multiple lighting levels and sophisticated, adjustable luminaires. This paper proposes an integrated approach, based on formal graph-based models and methods, to handle both of these tasks. The introduced formalisms help handle the state-space explosion related to the aforementioned characteristics. Control is performed by means of AI techniques (including rule-based systems and pattern matching), which is applied to the system using graph transformations. An illustrative, simple example is carried out throughout the paper, but the presented methods are highly scalable, which made them applicable to several practical projects of varying scale and characteristics.

Tab-Trees: A CASE Tool for the Design of Extended Tabular Systems

Tabular Systems constitute a particular form of rule-based systems. They follow the pattern of Relational Databases and Attributive Decision Tables. In Extended Tabular Systems non-atomic values of attributes are allowed. In order to assure safe, reliable and efficient performance of such systems, analysis and verification of selected qualitative properties such as completeness, consistency and determinism should be carried out. However, verification of them after the design of a system is both costly and late. In this paper another solution is proposed. A graphical CASE-like tool supporting the design of Extended Tabular Systems and providing verification possibilities is presented. The tool uses a new rule specification paradigm: the so-called tab-trees, covering the advantages of Attributive Decision Tables and Decision Trees. In the system the verification stage is moved into the design process. The background theory and the idea of tab-trees are outlined and presentation of a practical tool, the Osiris System, is carried out.

State-Space Reduction through Preference Modeling ⋆

Automated planning for numerous co-existing agents, with uncertainty caused by various levels of their predictability, observability and autonomy, is a complex task. One of the most significant issues is related to explosion of the state space. This paper presents a formal framework which can be used to model such systems and proposes the use of formally-modeled agents’ preferences as a way of reducing the number of states. A detailed description of preference modeling is provided, and the approach is evaluated by examples.

Ontology Oriented Storage, Retrieval and Interpretation for a Dynamic Map System

This paper presents the Dynamic Map system, one of the key products of the INSIGMA Project. The main focus is on the map and dynamic data storage subsystem, which utilizes a spatial database and is based on ontologies. First, the data models used are described, including the OpenStreetMap-based structure for the static map and the ontology-driven structure for dynamic parameters and events. The approach to generation of database structures from OWL is described in detail, followed by descriptions of the OSM import process, the GPS tracker module, the sensor state analyzer and the event interpreter. Finally, the planned future enhancements are outlined and discussed.

VARDA Rule Design and Visualization Tool-Chain

A prototype design tool-chain (VARDA) for the ARD hierarchical rule design method is presented in the paper. It is implemented in the Unix environment using Prolog and Graphviz for design visualization.

Application of distributed graph transformations to automated generation of control patterns for intelligent lighting systems

Abstract Designing a large infrastructure, such as a street lighting system, is a complex task itself especially in the context of Smart City and Smart Grid approaches. The problem is made even harder if it needs to be designed with control in mind. To facilitate a complex design process without losing fidelity, a graph-based formalism, namely General Environment Model (GEM), is proposed to be applied to model such an environment. Moreover, another graph-based model, namely Control Availability Graph or shortly CAG, is proposed to enable definition of routines for dynamic control of large-scale systems. Both of these models have been verified in practice, but the transition from GEM to CAG had been performed manually. In this paper, we propose a coherent, formal method of generating a control system from the graph-based environment description while taking into account the designers decisions. An application of the generated CAG as a control system yields up to 34% of energy consumption reduction in a pilot deployment of over 3500 light points for the city of Krakow, Poland.

Hierarchical rule design with HaDEs the HeKatE toolchain

A hierarchical approach to decision rule design in the HeKatE project is introduced in the paper. Within this approach a rule prototyping method ARD+ is presented. It offers a high-level hierarchical prototyping of decision rules. The method allows to identify all properties of the system being designed, and using the algorithm presented in this paper, it allows to automatically build rule prototypes. A practical implementation of the prototyping algorithm is discussed and a design example is also presented. Using the rule prototypes actual rules can be designed in the logical design stage. The design process itself is supported by HaDEs, a set of design tools developed within the project.

Street Lighting Control, Energy Consumption Optimization

Using a graph formalism to model outdoor lighting infrastructure has proven to be an efficient method for both design and control. It has been tested not only on laboratory scale, but also in a city-scale deployment. The paper proposes further energy usage optimization if the streetlights are dynamically controlled. It is to alter the design process taking into account influence of the control schemas. As a result substantial energy consumption savings can be achieved. The introduced optimization is also modeled with graphs and graph transformations.

A Robust Planning Algorithm for Groups of Entities in Discrete Spaces

Automated planning is a well-established field of artificial intelligence (AI), with applications in route finding, robotics and operational research, among others. The task of developing a plan is often solved by finding a path in a graph representing the search domain; a robust plan consists of numerous paths that can be chosen if the execution of the best (optimal) one fails. While robust planning for a single entity is rather simple, development of a robust plan for multiple entities in a common environment can lead to combinatorial explosion. This paper proposes a novel hybrid approach, joining heuristic search and the wavefront algorithm to provide a plan featuring robustness in areas where it is needed, while maintaining a low level of computational complexity.

Application of New ATAM Tools to Evaluation of the Dynamic Map Architecture

The paper reports an application of Architecture-based Tradeoff Analysis Method (ATAM) for early evaluation of the Dynamic Map architecture. The Dynamic Map is a complex information system, composed of spatial databases, storing static and dynamic data relevant for urban traffic, as well as a set of software modules responsible for data collection, interpretation and provision. Due to the complexity of the system, its size and key importance of its services to other subsystems, we decided to perform architecture evaluation using the ATAM method. To facilitate the task new tools supporting ATAM based assessment are proposed: Scenario Influence Matrix and Architectural Decision Matrix. Taking as example an excerpt from the system architecture, we present how they were used during the architecture evaluation. The gathered experience confirm usefulness of the tools, enabling ATAM to help detecting real flaws in a design and identify potential risks.