Hans W. Guesgen

Imprecise reasoning in geographic information systems

Abstract The concept of space underlying geographic information systems is basically Euclidean, requiring all subjects to adhere to the same view of space. This makes most attempts to deal with imprecise or uncertain geographic information difficult or sometimes even impossible. In this paper, we describe a way of incorporating imprecise qualitative spatial reasoning with quantitative reasoning in geographic information systems that is not restricted to Euclidean geometry. The ideal is to use fuzzy sets to model qualitative spatial relations among objects, like The downtown shopping mall is close to the harbor. The membership function of such a fuzzy set defines a fuzzy distance operator, for which a new algorithm is introduced in this paper.

Qualitative Spatial and Temporal Reasoning: Emerging Applications, Trends, and Directions

The field of Qualitative Spatial and Temporal Representation and Reasoning (QSTR) has evolved as a specialised discipline within Artificial Intelligence (Allen, 1983; Freksa, 1991; van Beek, 1992; Ladkin & Maddux, 1994; Cohn & Renz, 2007; Renz & Nebel, 2007). Recent years have witnessed remarkable advances in some of the long-standing problems of the field, primarily pertaining to spatial calculi and model construction issues emanating from the founding premises and early work in the community (Ligozat, 1990; Guesgen & Hertzberg, 1993, 1988). Subsequently, major developments have accrued with new results about tractability of spatial calculi and characterisation of important subclasses of relations (e.g., Nebel & Bürckert, 1994; Bessière et al., 1996; Renz, 1999, 2007; Li et al., 2009) and explicit construction of models of one or more aspects of space (e.g., Freksa, 1992; Randell et al., 1992; Cohn et al., 1997; Bennett, 2001; de Weghe et al., 2005; Moratz, 2006). Similar to these works, which are situated within an Artificial Intelligence/Knowledge Representation (KR) context, many crucial advances have accrued from other communities concerned with the development of formalisms and algorithms for modelling and reasoning about spatial information, a prime example here being the domain of spatial information theory for Geography (and Geographic Information Systems (GIS)) (Egenhofer & Franzosa, 1991; Egenhofer & Mark, 1995).

Reasoning About Distance Based on Fuzzy Sets

Most computer systems that deal with issues of space reason about distance by using a metric. For example, most geographic information systems apply the euclidean metric, requiring all subjects to adhere to the same view of space. As a result, dealing with imprecise or uncertain geographic information becomes difficult or sometimes even impossible. In this paper, we describe a way of reasoning about distance that is not restricted to euclidean geometry. The idea is to use fuzzy sets to describe how close objects are to each other.

Wireless Sensors Networks based monitoring: Review, challenges and implementation issues

Due to the advances in the field of communications networks there evolved, a very interesting and challenging area of wireless sensors networks, which is rapidly coming of age. These wireless sensors in the network are capable of performing various tasks and sense many types of information for various applications. These wireless sensor network technology has an ample potential to change the way we live and work, as these are applicable in various fields like entertainment, travel, retail, industry, medicine, care of the very young and very old people, emergency management thus enrich lives and make processes easier. In this paper a review of wireless sensors especially designed for health monitoring has been discussed. The characteristics of the sensors for this type of application have been studied. The requirement of the sensor for making a smart sensor network has been investigated. A typical in-house developed system for home monitoring for eldercare application has been presented.

A constraint-based approach to spatiotemporal reasoning

We introduce a form of spatiotemporal reasoning that uses homogeneous representations of time and the three dimensions of space. The basis of our approach is Allens temporal logic on the one hand and general constraint satisfaction algorithms on the other, where we present a new view of constraint reasoning to cope with the affordances of spatiotemporal reasoning as introduced here. As a realization for constraint reasoning, we suggest a massively parallel implementation in form of Boltzmann machines.

Behaviour Recognition from Sensory Streams in Smart Environments

One application of smart homes is to take sensor activations from a variety of sensors around the house and use them to recognise the particular behaviours of the inhabitants. This can be useful for monitoring of the elderly or cognitively impaired, amongst other applications. Since the behaviours themselves are not directly observed, only the observations by sensors, it is common to build a probabilistic model of how behaviours arise from these observations, for example in the form of a Hidden Markov Model (HMM). In this paper we present a method of selecting which of a set of trained HMMs best matches the current observations, together with experiments showing that it can reliably detect and segment the sensor stream into behaviours. We demonstrate our algorithm on real sensor data obtained from the MIT PlaceLab. The results show a significant improvement in the recognition accuracy over other approaches.

Spatio-Temporal Reasoning and Context Awareness

Smart homes provide many research challenges, but some of the most interesting ones are in dealing with data that monitors human behaviour and that is inherently both spatial and temporal in nature. This means that context becomes all important: a person lying down in front of the fireplace could be perfectly normal behaviour if it was cold and the fire was on, but otherwise it is unusual. In this example, the context can include temporal resolution on various scales (it is winter and therefore probably cold, it is not nighttime when the person would be expected to be in bed rather than the living room) as well as spatial (the person is lying in front of the fireplace) and extra information such as whether or not the fire is lit. It could also include information about how they reached their current situation: if they went from standing to lying very suddenly there would be rather more cause for concern than if they first knelt down and then lowered themselves onto the floor. Representing all of these different temporal and spatial aspects together is a major challenge for smart home research. In this chapter we will provide an overview of some of the methodologies that can be used to deal with these problems. We will also outline our own research agenda in the Massey University Smart Environments (MUSE) group.

Use cases for abnormal behaviour detection in smart homes

While people have many ideas about how a smart home should react to particular behaviours from their inhabitant, there seems to have been relatively little attempt to organise this systematically. In this paper, we attempt to rectify this in consideration of context awareness and novelty detection for a smart home that monitors its inhabitant for illness and unexpected behaviour. We do this through the concept of the Use Case, which is used in software engineering to specify the behaviour of a system. We describe a set of scenarios and the possible outputs that the smart home could give and introduce the SHMUC Repository of Smart Home Use Cases. Based on this, we can consider how probabilistic and logic-based reasoning systems would produce different capabilities.

Exploring the responsibilities of single-inhabitant Smart Homes with Use Cases

This paper makes a number of contributions to the field of requirements analysis for Smart Homes. It introduces Use Cases as a tool for exploring the responsibilities of Smart Homes and it proposes a modification of the conventional Use Case structure to suit the particular requirements of Smart Homes. It presents a taxonomy of Smart-Home-related Use Cases with seven categories. It draws on those Use Cases as raw material for developing questions and conclusions about the design of Smart Homes for single elderly inhabitants, and it introduces the SHMUC repository, a web-based repository of Use Cases related to Smart Homes that anyone can exploit and to which anyone may contribute.

An empirical study of the component dependency resolution search space

Dependency Resolution (DR) uses a components explicitly declared requirements and capabilities to calculate systems where all requirements are met. DR can lead to large amounts of possible solutions because multiple versions of the same component can be available and different vendors can offer the same functionality. From this set of potential solutions DR should identify and return the optimal solution. Determining the feasibility of many optimisation techniques largely depends on the size and complexity of the DR solution search space. Using two sets of OSGi components collected from the Eclipse project and Spring Enterprise Bundle Repository, we measure the size and examine the complexity of the DR search space. By adding simple constraints based on desirable properties, we show the potentially large search space can be significantly restricted. This restriction could be used to make more complex optimisation algorithms feasible for DR.