RCC8 is a popular fragment of the region connection calculus, in which qualitative spatial relations between regions, such as adjacency, overlap and parthood, can be expressed. While RCC8 is essentially dimensionless, most current applications are confined to reasoning about two-dimensional or three-dimensional physical space. In this paper, however, we are mainly interested in conceptual spaces, which typically are high-dimensional Euclidean spaces in which the meaning of natural language concepts can be represented using convex regions. The aim of this paper is to analyze how the restriction to convex regions constrains the realizability of networks of RCC8 relations. First, we identify all ways in which the set of RCC8 base relations can be restricted to guarantee that consistent networks can be convexly realized in respectively 1D, 2D, 3D, and 4D. Most surprisingly, we find that if the relation 'partially overlaps' is disallowed, all consistent atomic RCC8 networks can be convexly realized in 4D. If instead refinements of the relation 'part of' are disallowed, all consistent atomic RCC8 relations can be convexly realized in 3D. We furthermore show, among others, that any consistent RCC8 network with 2n+1 variables can be realized using convex regions in the n-dimensional Euclidean space.

In this paper, we exploit minimal sensing information gathered from biologically inspired sensor networks to perform exploration and mapping in an unknown environment. A probabilistic motion model of mobile sensing nodes, inspired by motion characteristics of cockroaches, is utilized to extract weak encounter information in order to build a topological representation of the environment. Neighbor to neighbor interactions among the nodes are exploited to build point clouds representing spatial features of the manifold characterizing the environment based on the sampled data. To extract dominant features from sampled data, topological data analysis is used to produce persistence intervals for features, to be used for topological mapping. In order to improve robustness characteristics of the sampled data with respect to outliers, density based subsampling algorithms are employed. Moreover, a robust scale-invariant classification algorithm for persistence diagrams is proposed to provide a quantitative representation of desired features in the data. Furthermore, various strategies for defining encounter metrics with different degrees of information regarding agents' motion are suggested to enhance the precision of the estimation and classification performance of the topological method.

Map matching of GPS trajectories from a sequence of noisy observations serves the purpose of recovering the original routes in a road network. In this work in progress, we attempt to share our experience of feature construction in a spatial database by reporting our ongoing experiment of feature extrac-tion in Conditional Random Fields (CRFs) for map matching. Our preliminary results are obtained from real-world taxi GPS trajectories.

Mobile-manipulation robots performing service tasks in human-centric indoor environments has long been a dream for developers of autonomous agents. Tasks such as cooking and cleaning require interaction with the environment, hence robots need to know relevant aspects of their spatial surroundings. However, unlike the structured settings that industrial robots operate in, service robots typically have little prior information about their environment. Even if this information was given, due to the involvement of many other agents (e.g., humans moving objects), uncertainty in the complete state of the world is inevitable over time. Additionally, most information about the world is irrelevant to any particular task at hand. Mobile manipulation robots therefore need to continuously perform the task of state estimation, using perceptual information to maintain the state, and its uncertainty, of task-relevant aspects of the world. Because indoor tasks frequently require the use of objects, objects should be given critical emphasis in spatial representations for service robots. Compared to occupancy grids and feature-based maps often used in navigation and SLAM, object-based representations are arguably still in their infancy. In my thesis, I propose a representation framework based on objects, their 'semantic' attributes, and their geometric realizations in the physical world.

The problem of checking the consistency of spatial calculi that contain both unknown and known entities (constants, i.e., real geometries) has recently been studied. Until now, all the approaches are theoretical and no implementation has been proposed. In this paper we present the first reasoner that takes as input RCC-5 or RCC-8 networks with variables and constants and decides their consistency. We investigate the performance of the reasoner experimentally using real-world networks and show that we can achieve significantly better times by geometry simplification and parallelization.

The Region Connection Calculus (RCC) is a well-known calculus for representing part-whole and topological relations. It plays an important role in qualitative spatial reasoning, geographical information science, and ontology. The computational complexity of reasoning with RCC has been investigated in depth in the literature. Most of these works focus on the consistency of RCC constraint networks. In this paper, we consider the important problem of redundant RCC constraints. For a set Γ of RCC constraints, we say a constraint (x R y) in Γ is redundant if it can be entailed by the rest of Γ. A prime network of Γ is a subset of Γ which contains no redundant constraints but has the same solution set as Γ. It is natural to ask how to compute a prime network, and when it is unique. In this paper, we show that this problem is in general co-NP hard, but becomes tractable if Γ is over a tractable subclass of RCC. If S is a tractable subclass in which weak composition distributes over non-empty intersections, then we can show that Γ has a unique prime network, which is obtained by removing all redundant constraints from Γ. As a byproduct, we identify a sufficient condition for a path-consistent network being minimal.

We present a cognitive design assistance system equipped with analytical capabilities aimed at anticipating architectural building design performance with respect to people-centred functional design goals. The paper focuses on the system capability to generate "narratives of visuo-locomotive user experience" from digital computer-aided architecture design (CAAD) models. The system is based on an underlying declarative narrative representation and computation framework pertaining to conceptual, geometric, and qualitative spatial knowledge. The semantics of the declarative narrative model, i.e., the overall   representation and computation model, is founded on: (a). conceptual knowledge formalised in an OWL ontology; (b). a general spatial representation and reasoning engine implemented in constraint logic programming; and (c). a declaratively encoded (narrative) construction process (based on search over graph structures) implemented in answer-set programming. We emphasise and demonstrate: complete system implementation, scalability, and robust performance & integration with industry-scale architecture industry tools (e.g., Revit, ArchiCAD) & standards (BIM, IFC).

Angry Birds is a popular video game where the task is to kill pigs protected by a structure composed of different building blocks that observe the laws of physics. The structure can be destroyed by shooting the angrybirds at it. The fewer birds we use and the more blocks we destroy, the higher the score. One approach to solve the game is by analysing the structure and identifying its strength and weaknesses. This can then be used to decide where to hit the structure with the birds. In this paper we use a qualitative spatial reasoning approach for this task. We develop a novel qualitative spatial calculus for representing and analysing the structure. Our calculus allows us to express and evaluate structural properties and rules, and to infer for each building block which of these properties and rules are satisfied. We use this to compute a heuristic value for each block that corresponds to how useful it is to hit that block. We evaluate our approach by comparing the suggested shot with other possible shots.

Space and time are two critical components of many real world systems. For this reason, analysis of anomalies in spatiotemporal data has been a great of interest. In this work, application of tensor decomposition and eigenspace techniques on spatiotemporal hotspot detection is investigated. An algorithm called SST-Hotspot is proposed which accounts for spatiotemporal variations in data and detect hotspots using matching of eigenvector elements of two cases and population tensors. The experimental results reveal the interesting application of tensor decomposition and eigenvector-based techniques in hotspot analysis.

We tackle the problem of multi-task learning with copula process. Multivariable prediction in spatial and spatial-temporal processes such as natural resource estimation and pollution monitoring have been typically addressed using techniques based on Gaussian processes and co-Kriging. While the Gaussian prior assumption is convenient from analytical and computational perspectives, nature is dominated by non-Gaussian likelihoods. Copula processes are an elegant and flexible solution to handle various non-Gaussian likelihoods by capturing the dependence structure of random variables with cumulative distribution functions rather than their marginals. We show how multi-task learning for copula processes can be used to improve multivari-able prediction for problems where the simple Gaussianity prior assumption does not hold. Then, we present a trans-ductive approximation for multi-task learning and derive analytical expressions for the copula process model. The approach is evaluated and compared to other techniques in one artificial dataset and two publicly available datasets for natural resource estimation and concrete slump prediction.