Theoretical foundations
Contemporary Computer-Aided Architecture Design (CAAD) systems explicitly represent objects such as doors, walls, slabs based on a domain-specific data model referred to as the Building Information Model (BIM), using a standard range of geometric constructs involving points, line-segments, polygons, meshes and other complex aggregates of basic geometric primitives. Building analysis and advanced simulation is conducted on BIMs with highly specialised algorithms for ensuring structural consistency and to investigate aspects such as access, lines-of-sight, egress, energy, air-flow, cost-estimation. However, what state of the art CAAD systems and advanced structural engineering and simulation methods lack is a human-centred, semantic perspective of the building: they retain their essential structure-centred "geometric" character. There are no concepts of "people" and perceptual-locomotion in building data models, making it impossible to express human-centred constraints about health, safety, well-being that are based on e.g. visibility, movement, function (whereas positional and structural constraints can be easily expressed about doors, walls, etc.). Thus, decades of research is cognitive psychology, evidence-based design and architectural engineering is completely disconnected from state of the art CAAD tools. A further critical computational challenge is constraint checking large industry-scale building models (e.g. thousands of BIM objects, over 1 million geometric mesh faces) across an enormous number of combinations of situations: different people (wheelchair users, hearing impaired etc.), activities (washing hands, moving, etc.), contexts (time of day, (un)crowded corridors, etc.). We address these limitations by (1) modelling the semantics of empty space, and (2) tightly integrating geometric constraint solving in logic programming languages.

The shape of empty space
Consider the region of empty space around an object such as a laptop or washbasin - this region is meaningful because a person must be located in that region to perform a particular act (e.g. washing hands). The geometry of this functional space region depends on properties of the person (consider wheelchair users, children, etc.), the task, and the object. Doors have an operational space required for opening and closing; people and sensors have range spaces (which can be further refined: visibility space, hearing space,..), and so on. These are examples of spatial artefacts: regions of empty space that are rich with perceptual-locomotive semantics.

Key Contribution: Our key insight is to introduce spatial artefacts as first-class objects within building data models, on the same ontological level as material objects such as walls and doors. By augmenting BIM with these primitive building blocks of perceptual-locomotion we can readily formalise human-centred qualitative constraints e.g. safety: "The functional space of the wash basin must not intersect with the operational space of the door".

Key Contribution: Provide a unifying formal framework for checking human-centred architecture design constraints that overcomes the challenges of BIM scale and combinatorial explosion. We will implement these augmented building data models in an advanced logic programming software language from artificial intelligence: Answer Set Programming extended to support spatial reasoning, ASPMT(QS). ASP is specifically designed to handle NP-hard problems with many advanced search-space pruning features (e.g. DPLL with learnt constraints, back-jumping, symmetry breaking etc.). Our extension will enable the seamless integration of semantic-level search-space pruning (e.g. hierarchical decomposition of buildings), and highly efficient geometric constraint solving (reasoning about polyhedra, 3D meshes, moving points, etc.)