2023   /   Paphos.   Macao.

|   August to September 2023   |

Paphos, Cyprus   /   Tutorial @ ECVP 2023

Spatial Cognition and Artificial Intelligence:  
Methods for In-The-Wild Behavioural Research in Visual Perception

The tutorial on “Spatial Cognition and Artificial Intelligence” addresses the confluence of empirically based behavioural research in the cognitive and psychological sciences with computationally driven analytical methods rooted in artificial intelligence and machine learning. This confluence is addressed in the backdrop of human behavioural research concerned with “in-the-wild” naturalistic embodied multimodal interaction. The tutorial presents:

  • an interdisciplinary perspective on conducting evidence-based (possibly large-scale) human behaviour research from the viewpoints of visual perception, environmental psychology, and spatial cognition.
  • artificial intelligence methods for the semantic interpretation of embodied multimodal interaction (e.g., rooted in behavioural data), and the (empirically driven) synthesis of interactive embodied cognitive experiences in real-world settings relevant to both everyday life as well to professional creative-technical spatial thinking.
  • 3. the relevance and impact of research in cognitive human-factors (e.g., in spatial cognition) for the design and implementation of next-generation human-centred AI technologies.
Keeping in mind an interdisciplinary audience, the focus of the tutorial is to provide a high-level demonstration of the potential of general AI-based computational methods and tools that can be used for multimodal human behavioral studies concerned with visuospatial, visuo-locomotive, and visuo-auditory cognition in everyday and specialized visuospatial problem solving. Presented methods are rooted in foundational research in artificial intelligence, spatial cognition and computation, spatial informatics, human- computer interaction, and design science. We highlight practical examples involving the analysis and synthesis of human cognitive experiences in the context of application areas such as (evidence-based) architecture and built environment design, narrative media design, product design, and visual sensemaking in autonomous cognitive systems (e.g., social robotics, autonomous vehicles).

   ECVP 2023 - 45th European Conference on Visual Perception   

Spatio-Temporal Reasoning and Learning   /   IJCAI 2023 Workshop

Spatio-Temporal Reasoning and Learning (STRL)

Opposing the false dilemma of logical reasoning vs machine learning, we argue for a synergy between these two paradigms in order to obtain hybrid, human-centred AI systems that will be robust, generalisable, explainable, and ecologically valid. Indeed, it is well-known that machine learning only includes statistical information and, therefore, on its own is inherently unable to capture perturbations (interventions or changes in the environment), or perform explainable reasoning and planning. Ideally, (the training of) machine learning models should be tied to assumptions that align with physics and human cognition to allow for these models to be re-used and re-purposed in novel scenarios. On the other hand, it is also the case that logic in itself can be brittle too, and logic further assumes that the symbols with which it can reason are available a priori. It is becoming ever more evident in the literature that modular AI architectures should be prioritised, where the involved knowledge about the world and the reality that we are operating in is decomposed into independent and recomposable pieces, as such an approach should only increase the chances that these systems behave in a causally sound manner.

The aim of this workshop is to formalize such a synergy between logical reasoning and machine learning that will be grounded on spatial and temporal knowledge. We argue that the formal methods developed within the spatial and temporal reasoning community, be it qualitative or quantitative, naturally build upon (commonsense) physics and human cognition, and could therefore form a module that would be beneficial towards causal representation learning. A (relational) spatio-temporal knowledge base could provide a foundation upon which machine learning models could generalise, and exploring this direction from various perspectives is the main theme of this workshop.

   STRL Workshop @ IJCAI 2023