=Paper=
{{Paper
|id=Vol-3833/keynote2
|storemode=property
|title=Neurosymbolic Visual Commonsense
|pdfUrl=https://ceur-ws.org/Vol-3833/keynote2.pdf
|volume=Vol-3833
|authors=Mehul Bhatt
|dblpUrl=https://dblp.org/rec/conf/daoxai/Bhatt24
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==Neurosymbolic Visual Commonsense==
https://ceur-ws.org/Vol-3833/keynote2.pdf
Neurosymbolic Visual Commonsense
On Integrated Reasoning and Learning about Space and Motion in
Embodied Multimodal Interaction
Mehul Bhatt
School of Science and Technology, Örebro University, Sweden
CoDesign Lab EU (Artificial and Human Intelligence)
Cognitive Vision and Perception » https:// codesign-lab.org/ cognitive-vision
Abstract
We present recent and emerging advances in computational cognitive vision addressing artificial visual and
spatial intelligence at the interface of (spatial) language, (spatial) logic and (spatial) cognition research. With a
primary focus on explainable sensemaking of dynamic visuospatial imagery, we highlight the (systematic and
modular) integration of methods from knowledge representation and reasoning, computer vision, spatial informat-
ics, and computational cognitive modelling. A key emphasis here is on generalised (declarative) neurosymbolic
reasoning & learning about space, motion, actions, and events relevant to embodied multimodal interaction
under ecologically valid naturalistic settings in everyday life. Practically, this translates to general-purpose
mechanisms for computational visual commonsense encompassing capabilities such as (neurosymbolic) semantic
question-answering, relational spatio-temporal learning, visual abduction etc.
The presented work is motivated by and demonstrated in the applied backdrop of areas as diverse as autonomous
driving, cognitive robotics, design of digital visuoauditory media, and behavioural visual perception research
in cognitive psychology and neuroscience. More broadly, our emerging work is driven by an interdisciplinary
research mindset addressing human-centred responsible AI through a methodological confluence of AI, Vision,
Psychology, and (human-factors centred) Interaction Design.
Keywords
Cognitive vision, Knowlede representation and reasoning (KR), Machine Learning, Integration of reasoning &
learning, Commonsense reasoning, Declarative spatial reasoning, Relational Learning, Computational cognitive
modelling, Human-Centred AI, Responsible AI
1. Motivation
Multimodality in embodied interaction is an inherent aspect of human activity, be it in social, profes-
sional, or everyday mundane contexts. Next-generation human-centred AI technologies, operating in
such contextualised everyday settings, will require an inherent foundational capacity to “make sense”
of —e.g., perceive, understand, explain, anticipate— everyday, naturalistic interactional multimodality.
This would be essential towards successfully achieving technology mediated (“human-in-the-loop” )
collaborative assistance, as well as ensuring compliance with emerging human-centred ethical and legal
requirements, performance benchmarks, and inclusive usability expectations. It is therefore crucial
that the foundational building blocks of such next-generation systems be semantically aligned with the
descriptive, analytical, and explanatory characteristics and complexity of human task conceptualisation,
performance benchmarks, and usability expectations. Against this backdrop, we define artificial visual
intelligence [1] as:
DAO-XAI 2024: Workshop on Data meets Applied Ontologies in Explainable AI, October 19, 2024, Santiago de Compostela, Spain
$ mehul.bhatt@oru.se (M. Bhatt)
https://mehulbhatt.org (M. Bhatt)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
» The computational capability to semantically process and interpret diverse forms of
visual stimuli (typically, but not necessarily) emanating from sensing embodied multimodal
interactions of / amongst humans and other artefacts in diverse naturalistic situations of
everyday life and work.
Within the scope of artificial visual intelligence are a wide-spectrum of high-level human-centred
sensemaking capabilities. These capabilities encompass operational functions such as:
• Visuospatial conception formation, commonsense/qualitative generalisation, analogical inference;
• Hypothetical reasoning, argumentation, explanation, counterfactual reasoning;
• Event based episodic maintenance & retrieval for perceptual narrativisation.
The afore enumeration is by no means exhaustive: in essence, in scope of artificial visual intelligence
are diverse high-level cognitive visuospatial sensemaking capabilities —be it mundane, analytical, or
creative— that humans acquire developmentally or through specialised training, and are routinely adept
at performing seamlessly in their everyday life and work (e.g., driving a vehicle, tracking moving objects,
navigating a crowded urban environment, engaging in sports, interpreting subtle cues in everyday
people-communication from visual / gestural and auditory signals).
Our central focus is on the development of general, domain-independent methods that may be
seamlessly integrated as part of hybrid computational cognitive system, or even within computational
cognitive models / cognitive architectures [2]. We also contextualise and demonstrate in the backdrop
of applications in autonomous driving, cognitive robotics, visuoauditory media design, and cognitive
psychology (e.g. [3, 4, 5, 6], [7, 8] ). Through applied case-studies, we provide a systematic model
and general methodology showcasing the integration of diverse, multi-faceted AI methods pertaining
Knowledge Representation and Reasoning, Computer Vision, Machine Learning, and Visual Perception
towards realising practical, human-centred, computational visual intelligence.
2. Neurosymbolic Visual Commonsense: Integrated Reasoning and
Learning about Space, Motion, and Inter(A)ction
In the present status quo, our research in (computational) neurosymbolic visual commonsense categori-
cally addresses three key questions:
I. What kind of (relational) abstraction mechanisms are needed to computationally “make-sense” of
embodied multimodal interaction ?
II. How can (and why should) abstraction mechanisms (such as in I) be founded on behaviourally
established cognitive human- factors emanating from naturalistic empirical observation in real-
world applied contexts?
III. How to articulate behaviourally established abstraction mechanisms, preferences (etc) as for-
mal declarative models suited for computational modelling aimed at operational“sensemaking”
(encompassing capabilities such as abduction, relational learning, counterfactual inference) ?
Present work is particularly aimed at developing general methods for the semantic interpretation
of (multimodal) dynamic visuospatial imagery with an emphasis on the ability to neurosymbolically
perform abstraction, reasoning, and learning with cognitively rooted structured characterisations of
commonsense knowledge pertaining to space and motion. Here, we specifically emphasise:
• General foundational commonsense abstractions of space, time, and motion needed for repre-
sentation mediated (grounded) reasoning and learning with dynamic visuospatial stimuli (e.g.,
emanating from multimodal human behavioural signals in modalities such as RGB(D), video,
audio, eye-tracking and possibly even bio signals [9]);
• Deep (visuospatial) semantics, entailing systematically formalised declarative (neurosymbolic)
reasoning and learning with aspects pertaining to space, space-time, motion, actions & events,
spatio-linguistic conceptual knowledge. Here, it is of the essence that an expressive ontology
consisting of, for instance, space, time, space-time motion primitives as first-class ‘neurosymbolic’
objects is accessible within the (declarative) programming paradigm under consideration; and
• Explainable models of computational visuospatial commonsense based on a systematic integration
of symbolic/relational methods on the one hand, and neural techniques aimed at low level
quantitative (e.g., visual) data processing on the other;
At a higher level of abstraction, deep (visualspatial) semantics (or deep semantics for short) entails
inherent support for tackling a range of challenges concerning epistemological and phenomenological
aspects relevant to dynamic spatial systems [10] where integrated reasoning about action and change
[11, 12] is involved:
• interpolation and projection of missing information, e.g., what could be hypothesised about
missing information (e.g., moments of occlusion [13]); how can this hypothesis support planning
an immediate next step?
• object identity maintenance at a semantic level, e.g., in the presence of occlusions, missing and
noisy quantitative data, error in detection and tracking
• ability to make default assumptions, e.g., pertaining to persistence objects and/or object
attributes
• maintaining consistent beliefs respecting (domain-neutral) commonsense criteria, e.g., related
to compositionality & indirect effects, space-time continuity, positional changes resulting from
motion
• inferring / computing counterfactuals [14], in a manner akin to human cognitive ability to
perform mental simulation for purposes of introspection about the past or anticipation of the
future, or performing “what-if” reasoning tasks etc
We particularly emphasise the abilities to abstract, learn, and reason with cognitively rooted struc-
tured characterisations of commonsense knowledge about space and motion, encompassing visuospa-
tial question-answering, abduction, and relational learning:
I. Visuospatial Question-Answering. Focus is on a computational framework for semantic-question
answering with video and eye-tracking data founded in constraint logic programming; we also demon-
strate an application in cognitive film & media studies, where human perception of films vis-a-via
cinematographic devices is of interest. » [4, 6, 7, 8]
II. Visuospatial Abduction. Focus is on a hybrid architecture for systematically computing robust
visual explanation(s) encompassing hypothesis formation, belief revision, and default reasoning with
video data (for active vision for autonomous driving, as well as for offline processing). The architecture
supports visual abduction with space-time histories as native entities, and founded in (functional)
answer set programming based spatial reasoning. » [3, 13, 15][16, 17]
III. Relational Visuospatial Learning. Focus is on a general framework and pipeline for: relational
spatio-temporal (inductive) learning with an elaborate ontology supporting a range of space-time
features; and generating semantic, (declaratively) explainable interpretation models in a neurosymbolic
pipeline demonstrated for the case of analysing visuospatial symmetry in visual art. » [18][5][19]
Formal semantics and computational models of deep semantics manifest themselves as neurosymbolic
spatio-temporal extensions of established declarative AI frameworks such as Constraint Logic Program-
ming (CLP) [20], Inductive Logic Programming (ILP) [21], and Answer Set Programming (ASP) [22].
The more foundational aspects pertaining declarative spatial reasoning (built on top of CLP, ILP, ASP)
independent of its relationship to cognitive vision research may be consulted in [23], [16, 24], [18].
3. Discussion
The vision that drives our scientific methodology is:
» To shape the nature and character of (machine-based) artificial visual intelligence with
respect to human-centred cognitive considerations, demonstrating an exemplar for develop-
ing, applying, and disseminating such methods in socio-technologically relevant application
areas where:
(a) embodied (multimodal) human interaction is inherent;
(b) human-in-the-loop collaborative work is of the essence; and
(c) normative ethico-legal compliance based on regulatory requirement and human-factors
driven inclusive or universal design criteria is to be ensured.
Towards realising this vision, we adopt an interdisciplinary approach –at the confluence of Cognition,
AI, Interaction, and Design– which we deem necessary to better appreciate the complexity and spectrum
of varied human-centred challenges for the design and (usable) implementation of (explainable) artificial
visual intelligence solutions in diverse human-system interaction contexts.
One of the key technical driving forces in our work is that of “representation mediated multimodal
sensemaking”. In essence, we consider (neurosymbolic) representation mediated grounding as being
significant in semiotic construction, e.g., enabling high-level meaning-making. This view stems from the
long-established value of “grounding” in Artificial Intelligence and related disciplines [25]. Our research
advances the theoretical, methodological, and applied understanding of “grounded representation”
mediated multimodal sensemaking of embodied human interaction at the interface of spatial language,
spatial logic, and spatial cognition. In our view, the significance of this form of (neurosymbolic)
grounding must now be reiterated, re-asserted even, in view of recent advances in neural machine
learning and the well-recognised “explainability” and “interpretability” requirements from the viewpoint
of human-centred AI [26, 27, 28]. We believe that research in knowledge representation and reasoning
(KR) has, since its inception, concerned itself with the “hard” problem of semantics, emphasising
explainability, formal verification and diagnosis, elaboration tolerance amongst other things. Research
in KR, and more broadly in symbolic AI and semantics, and their role and contribution towards large-
scale hybrid “human-in-the-loop” intelligence is of even greater significance now than ever before given
the tremendous synergistic opportunities afforded by the widely demonstrated power of deep learning
driven techniques in computer vision (and beyond). The onus now, we posit, is on KR research to drive
itself towards developing methods that can seamlessly integrate (and be “usable”) with other kinds of
AI methods, be data-centric neural learning techniques, or otherwise.
In this invited position statement, we have attempted to summarise our mindset and ongoing work in
the CoDesign Lab towards:
» Establishing a human-centric foundation and roadmap for the development of neurosym-
bolically grounded inference about embodied multimodal interaction as identifiable in a
range of real-world application contexts.
The summary is not meant to be a comprehensive literature review; this may be obtained through the
cited works. For key technical details and to obtain a summary of open directions, we direct interested
readers to select publications: a compact starting point may be obtained via the comprehensive summary
in [1], or through the shorter/focussed components in [15, 5, 4, 13, 3]. Longer summaries in the form of
(recent) doctoral dissertations are available in [29] and [30, 31].
Acknowledgments
We acknowledge funding by the Swedish Research Council (VR - Vetenskapsrådet) - https://www.vr.se,
and the Swedish Foundation for Strategic Research (SSF – Stiftelsens för Strategisk Forskning) - https:
//strategiska.se. Previously, this research has been supported by the German Research Foundation (DFG
– Deutsche Forschungsgemeinschaft) - https://www.dfg.de.
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