Carl P. L. Schultz

CLP(QS): a declarative spatial reasoning framework

We propose CLP(QS), a declarative spatial reasoning framework capable of representing and reasoning about high-level, qualitative spatial knowledge about the world. We systematically formalize and implement the semantics of a range of qualitative spatial calculi using a system of non-linear polynomial equations in the context of a classical constraint logic programming framework. Whereas CLP(QS) is a general framework, we demonstrate its applicability for the domain of Computer Aided Architecture Design. With CLP(QS) serving as a prototype, we position declarative spatial reasoning as a general paradigm open to other formalizations, reinterpretations, and extensions. We argue that the accessibility of qualitative spatial representation and reasoning mechanisms via the medium of high-level, logic-based formalizations is crucial for their utility toward solving real-world problems.

ASPMT(QS): Non-Monotonic Spatial Reasoning with Answer Set Programming Modulo Theories

The systematic modelling of dynamic spatial systems [9] is a key requirement in a wide range of application areas such as comonsense cognitive robotics, computer-aided architecture design, dynamic geographic information systems. We present ASPMT(QS), a novel approach and fully-implemented prototype for non-monotonic spatial reasoning —a crucial requirement within dynamic spatial systems– based on Answer Set Programming Modulo Theories (ASPMT). ASPMT(QS) consists of a (qualitative) spatial representation module (QS) and a method for turning tight ASPMT instances into Sat Modulo Theories (SMT) instances in order to compute stable models by means of SMT solvers. We formalise and implement concepts of default spatial reasoning and spatial frame axioms using choice formulas. Spatial reasoning is performed by encoding spatial relations as systems of polynomial constraints, and solving via SMT with the theory of real nonlinear arithmetic. We empirically evaluate ASPMT(QS) in comparison with other prominent contemporary spatial reasoning systems. Our results show that ASPMT(QS) is the only existing system that is capable of reasoning about indirect spatial effects (i.e. addressing the ramification problem), and integrating geometric and qualitative spatial information within a non-monotonic spatial reasoning context.

Towards a declarative spatial reasoning system

We present early results on the development of a declarative spatial reasoning system within the context of the Constraint Logic Programming (CLP) framework. The system is capable of modelling and reasoning about qualitative spatial relations pertaining to multiple spatial domains, i.e., one or more aspects of space such as topology, and intrinsic and extrinsic orientation. It provides a seamless mechanism for combining formal qualitative spatial calculi within one framework, and provides a Prolog-based declarative interface for AI applications to abstract and reason about quantitative, geometric information in a qualitative manner. Based on previous work concerning the formalisation of the framework [2], we present ongoing work to develop the theoretical result into a comprehensive reasoning system (and Prolog-based library) which may be used independently, or as a logic-based module within hybrid intelligent systems.

A multi-modal data access framework for spatial assistance systems: use-cases with the building information model (BIM/IFC)

Spatial assistance systems are computational embodiments of spatial decision-making and other forms of analytical abilities that otherwise typically require extensive domain-specific training, knowledge, and expertise. Broadly, such systems are essentially instruments of assistance, assurance and empowerment. Whereas these systems may vary in the domain of application and the precise manner of intelligent assistance, there exist several fundamental similarities from a systemic and information-theoretic viewpoint with regard to the ontological and computational foundations that underlie their practical design and implementation. We present a multi-modal spatial data access framework designed to serve the informational and computational requirements of the class of spatial assistance systems that are intended to provide intelligent spatial decision-making capabilities. The framework focuses on multi-perspective semantics, qualitative and artefactual abstractions, and industrial conformance and interoperability. We position the framework, and also provide use-cases with distinct application domains.

Multimodal spatial data access for architecture design assistance

Abstract We present a multimodal spatial data access framework designed to serve the informational and computational requirements of architectural design assistance systems that are intended to provide intelligent spatial decision support and analytical capabilities. The framework focuses on multiperspective semantics, qualitative and artifactual spatial abstractions, and industrial conformance and interoperability within the context of the industry foundation classes. The framework provides qualitative and cognitively adequate representational mechanisms, and the formal interpretation of the structural form of indoor spaces that are not directly provided by conventional computer-aided design based or quantitative models of space. We illustrate the manner in which these representations directly provide the spatial abstractions that are needed to enable the implementation of intelligent analytical capabilities in design assistance tools. We introduce the framework, and also provide detailed use cases that illustrate the usability of the framework and the manner of its utilization within architectural design assistance systems.

Declarative spatial reasoning with boolean combinations of axis-aligned rectangular polytopes

We present a formal framework and implementation for declarative spatial representation and reasoning about the topological relationships between boolean combinations of regions (i.e., union, intersection, difference, xor). Regions of space here correspond to arbitrary axis aligned n-polytope objects, with geometric parameters either fully grounded, partially grounded, or completely unspecified. The framework is implemented in the context of CLP(QS)2, a constraint logic programming based declarative spatial reasoning system providing support for geometric and qualitative spatial abstraction and inference capabilities. We demonstrate that our method can solve packing, contact, containment, and constructive proof problems that are unsolvable using standard relational algebraic approaches for qualitative spatial reasoning (QSR). Our approach is driven by general accessibility of spatial reasoning via KR languages for their application in domains such as design, geography, robotics, and cognitive vision.

Non-Monotonic Spatial Reasoning with Answer Set Programming Modulo Theories

The systematic modelling of dynamic spatial systems is a key requirement in a wide range of application areas such as commonsense cognitive robotics, computer-aided architecture design, and dynamic geographic information systems. We present ASPMT(QS), a novel approach and fully-implemented prototype for non-monotonic spatial reasoning -a crucial requirement within dynamic spatial systems- based on Answer Set Programming Modulo Theories (ASPMT). ASPMT(QS) consists of a (qualitative) spatial representation module (QS) and a method for turning tight ASPMT instances into Satisfiability Modulo Theories (SMT) instances in order to compute stable models by means of SMT solvers. We formalise and implement concepts of default spatial reasoning and spatial frame axioms. Spatial reasoning is performed by encoding spatial relations as systems of polynomial constraints, and solving via SMT with the theory of real nonlinear arithmetic. We empirically evaluate ASPMT(QS) in comparison with other contemporary spatial reasoning systems both within and outside the context of logic programming. ASPMT(QS) is currently the only existing system that is capable of reasoning about indirect spatial effects (i.e., addressing the ramification problem), and integrating geometric and qualitative spatial information within a non-monotonic spatial reasoning context. This paper is under consideration for publication in TPLP.

InSpace3D: A Middleware for Built Environment Data Access and Analytics

Abstract Standardisation, archiving, and digital access of spatial data pertaining to built-up environments is an area acquiring increasing attention amongst several interest groups: policy makers, designers and planners, civil engineers, infrastructure management and public service personnel, building users. Initiatives such as the Building Information Model (BIM), Industry Founda- tion Classes (IFC), and CityGML are creating the information-theoretic backbone that guides the crucial aspects of quality , exchange , and interoperability of spatial data at the environmental and urban scale. However, due to the inherent scale, com- plexity, and detailed geometric character of building information data, extracting useful semantic and qualitative knowledge for accomplishing high-level analytical tasks is still an extremely complex and error prone process involving data intensive computing. We propose a uniform spatial data access middleware that can provide a combination of high-level, multi-modal, semantic, and quantitative-qualitative spatial data access and analytical capability. We present the core computational capabil- ities for the proposed middleware and present an overview of the high-level spatial model and its compliance with the industry standard IFC. A key theoretical contribution is a framework for investigating the computational complexity of deriving spatial artefacts within the context of building informatics. Additionally, we empirically investigate the feasibility and practicality of the derivation of spatial artefacts by conducting experiments on seven industry-scale IFC models. The experiment results show that, despite having non-linear polynomial increase with respect to time, deriving spatial artefacts is practical with large designs.

A metaheuristic approach for efficient and effective sketch-to-metric map alignment

ABSTRACT Sketching as a natural mode for human communication and creative processes presents opportunities for improving human–computer interaction in geospatial information systems. However, to use a sketch map as user input, it must be localized within the underlying spatial data set of the information system, the base metric map. This can be achieved by a matching process called qualitative map alignment in which qualitative spatial representations of the two input maps are used to establish correspondences between each sketched object and one or more objects in the metric map. The challenge is that, to the best of our knowledge, no method for matching qualitative spatial representations suggested so far is applicable in realistic scenarios due to excessively long runtimes, incorrect algorithm design or the inability to use more than one spatial aspect at a time. We address these challenges with a metaheuristic algorithm which uses novel data structures to match qualitative spatial representations of a pair of maps. We present the design, data structures and performance evaluation of the algorithm using real-world sketch and metric maps as well as on synthetic data. Our algorithm is novel in two main aspects. Firstly, it employs a novel system of matrices known as local compatibility matrices, which facilitate the computation of estimates for the future size of a partial alignment and allow several types of constraints to be used at the same time. Secondly, the heuristic it computes has a higher accuracy than the state-of-the-art heuristic for this task, yet requires less computation. Our algorithm is also a general method for matching labelled graphs, a special case of which is the one involving complete graphs whose edges are labelled with spatial relations. The results of our evaluation demonstrate practical runtime performance and high solution quality.

Embodied visuo-locomotive experience analysis: immersive reality based summarisation of experiments in environment-behaviour studies

Evidence-based design (EBD) for architecture involves the study of post-occupancy behaviour of building users with the aim to provide an empirical basis for improving building performance [Hamilton and Watkins 2009]. Within EBD, the high-level, qualitative analysis of the embodied visuo-locomotive experience of representative groups of building users (e.g., children, senior citizens, individuals facing physical challenges) constitutes a foundational approach for understanding the impact of architectural design decisions, and functional building performance from the viewpoint of areas such as environmental psychology, wayfinding research, human visual perception studies, spatial cognition, and the built environment [Bhatt and Schultz 2016].