Jasper van de Ven

A Survey of Qualitative Spatial and Temporal Calculi: Algebraic and Computational Properties

Qualitative spatial and temporal reasoning (QSTR) is concerned with symbolic knowledge representation, typically over infinite domains. The motivations for employing QSTR techniques include exploiting computational properties that allow efficient reasoning to capture human cognitive concepts in a computational framework. The notion of a qualitative calculus is one of the most prominent QSTR formalisms. This article presents the first overview of all qualitative calculi developed to date and their computational properties, together with generalized definitions of the fundamental concepts and methods that now encompass all existing calculi. Moreover, we provide a classification of calculi according to their algebraic properties.

Privacy Classification for Ambient Intelligence

In the field of ambient intelligence (AmI) privacy is recognized as one of the key factors regarding the acceptance of systems. However, this topic is mostly neglected or only addressed in a simplified form. Our approach is to understand privacy as a service, i.e., the AmI restricts its own knowledge rather than only hiding it. Through providing related vocabulary and a basic theory of privacy, we introduce a categorization of privacy related functionality and a classification of privacy affordances an AmI can provide. By investigating and evaluating small prototypical systems we propose a measure to compare different systems with respect to the balance between support provided and privacy.

Qualitative Privacy Description Language

Privacy is a major concern regarding acceptance of technology. Although, general concepts, privacy languages, and technology to implement privacy exist, these aspects are considered rather independently yet. We propose a logic based qualitative privacy description language (QPDL), which allows for an integrated view of these three perspectives and system analysis based on policy formalizations, e.g., system conformance or policy conflicts.

A distributed online learning tracking algorithm

In this paper we introduce a way of tracking people in an indoor environment across multiple cameras with overlapping as well as non-overlapping fields of view. To do so, we use our distribution model called SpARTA and an extended Tracking-Learning-Detection algorithm. A big advantage in comparison to other systems is that each camera node learns the tracked person and builds a database of positive and negative examples in real time. With these datasets we are able to distinguish different people across different nodes. The learned data is shared across nodes, so that they improve each other while tracking. In the main part we present an experimental validation of the system. Finally, we will show that distribution of tracking data improves tracking across multiple nodes considerably with regard to partial occlusion of the tracked object.

Spatial Problem Solving in Spatial Structures

The ability to solve spatial tasks is crucial for everyday life and therefore of great importance for cognitive agents. In artificial intelligence (AI) we model this ability by representing spatial configurations and spatial tasks in the form of knowledge about space and time. Augmented by appropriate algorithms, such representations enable the generation of knowledge-based solutions to spatial problems. In comparison, natural embodied and situated cognitive agents often solve spatial tasks without detailed knowledge about underlying geometric and mechanical laws and relationships. They directly relate actions and their effects through physical affordances inherent in their bodies and their environments. Examples are found in everyday reasoning and also in descriptive geometry. In an ongoing research effort we investigate how spatial and temporal structures in the body and the environment can support or even replace reasoning effort in computational processes. We call the direct use of spatial structure Strong Spatial Cognition. Our contribution describes cognitive principles of an extended paradigm of cognitive processing. The work aims (i) to understand the effectiveness and efficiency of natural problem solving approaches; (ii) to overcome the need for detailed representations required in the knowledge-based approach; and (iii) to build computational cognitive systems that make use of these principles.

Formal representation of qualitative direction

ABSTRACT This paper reviews formal approaches to representing spatial knowledge about qualitative direction. Unlike geometric direction information, qualitative information does not employ numerical values but relies on comparison. The qualitative approach is often regarded as suitable for capturing commonsense concepts and thus is relevant to human-centered interfaces for spatial information systems. To establish a context for the work on qualitative direction, we preset a brief history of the development of qualitative temporal and spatial representations from different scientific perspectives. We identify main focal areas of these representations of spatial direction and propose a taxonomy. In the light of more than three decades of fruitful research, we obtain a map of formal representations that reveal interrelationships between different research strands in the field.

Analyzing Strong Spatial Cognition: A Modeling Approach

Natural cognitive agents such as humans and animals may frequently solve spatial problems in their environment by manipulating their environment instead of doing all the computation in their head (e.g., untangling a power cable by inspection and direct interaction: pull here, push there). We call this replacement of computational effort from the central processor by direct manipulation strong spatial cognition. Artificial cognitive agents are currently lacking a comparable ability to exploit their spatio-physical environment for efficient problem solving. One main issue with equipping artificial cognitive agents with strong spatial cognition is that the constraints and properties of this type of problem solving are still insufficiently understood. Being tightly embedded in the spatio-physical and temporal surrounding renders strong spatial cognition difficult to assess by traditional methods. This makes it hard to gain an explicit understanding of its nature and to compare it to existing computational approaches. In this paper, we propose to employ models of strong spatial cognition to gain a deeper understanding of this phenomenon and its nature. We created models of an example application of strong spatial cognition to solve the shortest path problem. By considering different approaches for a computational simulation model, our modeling work revealed that (instantaneous) information propagation constitutes a core characteristic of strong spatial cognition. Moreover, modeling facilitated identifying those questions, which seem of major importance for further deepening our understanding of strong spatial cognition.

The Spatial Interaction Laboratory

In this paper we present the spatial interaction laboratory (SIL), an ambient intelligence (AmI) at the Bremen Spatial Cognition Center, University of Bremen. We describe three scenarios that guided design of hardware and software components. We sketch the environment and the middleware, which provides the basis for further functionality development. Furthermore, we give examples of existing components and stress the importance of privacy as a key acceptance factor for ambient intelligence.

The SOCIAL Project

The aim of the project SOCIAL is to explore possibilities to facilitate spontaneous and informal communication in spatially distributed groups by exploiting ambient intelligence and smart environments. Spontaneous and informal communication has a strong impact on the productivity, social identity, and wellbeing of work groups. The spatial distance between peers plays a key role in successfully establishing and maintaining such communication. In co-located teams, spontaneous communication occurs daily: People occasionally meet on office floors, at the coffee corner, or have lunch together. Today, due to globalization we often encounter distributed work settings that impede spontaneous communication between co-workers, as teams are distributed over branch offices located in different cities and countries. We propose to approach this problem by (1) detecting situations with the potential for spontaneous informal communication, (2) representing and raising awareness for these situations appropriately, and (3) enabling users to engage seamlessly in spontaneous communication spanning spatially separated locations. In this paper we focus on the second aspect. A pilot study is described with results on combining various interaction modalities in order to raise awareness for communication. In addition, we describe a formal representation for ambient intelligence incorporating situational context and the system itself.

Knowledge vs. Support

Privacy is recognized as one of the key factors regarding the acceptance of ambient intelligence (AmI). However, privacy is neglected in many projects. We address a formal representation of AmI allowing to model systems including privacy expectations and assumptions. In order to be able to compare systems, either existing or theoretically defined, we develop a benchmark framework that is based on this formal representation. We demonstrate the applicability of our approach with a system implementing two different privacy settings.