Maria M. Hedblom

Verified @gmail.com

Department of Computing
School of Engineering at Jonkoping University



                 

https://researchid.co/mariahedblom

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Theoretical Computer Science

47

Scopus Publications

721

Scholar Citations

14

Scholar h-index

20

Scholar i10-index

Scopus Publications

  • CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking
    Hansi Hettiarachchi, Amna Dridi, Mohamed Medhat Gaber, Pouyan Parsafard, Nicoleta Bocaneala, Katja Breitenfelder, Gonçal Costa, Maria Hedblom, Mihaela Juganaru-Mathieu, Thamer Mecharnia,et al.

    Springer Science and Business Media LLC
    Abstract Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced Machine Learning (ML). Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862 sentences from the building regulations of England and Finland. Only the self-contained sentences, which express complete rules without needing additional context, were considered as they are essential for ACC. Each sentence was manually annotated with entities and relations by a team of 12 annotators to facilitate machine-readable rule generation, followed by careful curation to ensure accuracy. The final dataset comprises 4,297 entities and 4,329 relations across various categories, serving as a robust ground truth. CODE-ACCORD supports a range of ML and Natural Language Processing (NLP) tasks, including text classification, entity recognition, and relation extraction. It enables applying recent trends, such as deep neural networks and large language models, to ACC.

  • Hanging Around: Cognitive Inspired Reasoning for Reactive Robotics
    Mihai Pomarlan, Stefano De Giorgis, Rachel Ringe, Maria M. Hedblom, and Nikolaos Tsiogkas

    IOS Press
    Situationally-aware artificial agents operating with competence in natural environments face several challenges: spatial awareness, object affordance detection, dynamic changes and unpredictability. A critical challenge is the agent’s ability to identify and monitor environmental elements pertinent to its objectives. Our research introduces a neurosymbolic modular architecture for reactive robotics. Our system combines a neural component performing object recognition over the environment and image processing techniques such as optical flow, with symbolic representation and reasoning. The reasoning system is grounded in the embodied cognition paradigm, via integrating image schematic knowledge in an ontological structure. The ontology is operatively used to create queries for the perception system, decide on actions, and infer entities’ capabilities derived from perceptual data. The combination of reasoning and image processing allows the agent to focus its perception for normal operation as well as discover new concepts for parts of objects involved in particular interactions. The discovered concepts allow the robot to autonomously acquire training data and adjust its subsymbolic perception to recognize the parts, as well as making planning for more complex tasks feasible by focusing search on those relevant object parts. We demonstrate our approach in a simulated world, in which an agent learns to recognize parts of objects involved in support relations. While the agent has no concept of handle initially, by observing examples of supported objects hanging from a hook it learns to recognize the parts involved in establishing support and becomes able to plan the establishment/destruction of the support relation. This underscores the agent’s capability to expand its knowledge through observation in a systematic way, and illustrates the potential of combining deep reasoning with reactive robotics in dynamic settings.

  • Twist and Snap: A Neuro-Symbolic System for Affordance Learning of Opening Jars and Bottles
    Jorge Aguirregomezcorta Aina and Maria M. Hedblom

    IEEE
    Autonomous robotic systems need a flexible and safe method to interact with their surroundings. When encountering unfamiliar objects, the agents should be able to identify and learn the involved affordances to apply appropriate actions. Focusing on affordance learning, we introduce a neuro-symbolic AI system with a robot simulation capable of inferring appropriate action. The system's core is a visuo-lingual attribute detection module coupled with a probabilistic knowledge base. The system is accompanied by a Unity robot simulation that is used for evaluation. The system is evaluated through caption-inferring capabilities using image captioning and machine translation metrics on a case study of opening containers. The two main affordance-action relation pairs are the jar/bottle lids that are open using either a ‘twist’ or a ‘snap’ action. The results show the system is successful in opening all 50 containers in the test case, based on an accurate attribute captioning rate of 71%. The mismatch is likely due to the ‘snapping’ lids being able to open also after a twisting motion. Our system demonstrates that affordance inference can be successfully implemented using a cognitive visuo-lingual method that could be generalized to other affordance cases.

  • Beyond Space and Time: An Initial Sketch of Formal Accounts to Non-Spatiotemporal Conceptual Sensory Primitives


  • Revising Defeasible Theories via Instructions
    Mihai Pomarlan, Maria M. Hedblom, Laura Spillner, and Robert Porzel

    Springer Nature Switzerland

  • The Diagrammatic Image Schema Language (DISL)
    Maria M. Hedblom, Fabian Neuhaus, and Till Mossakowski

    Informa UK Limited

  • Curiously exploring affordance spaces of a pouring task
    Mihai Pomarlan, Maria M. Hedblom, and Robert Porzel

    Wiley
    AbstractHuman beings and other biological agents appear driven by curiosity to explore the affordances of their environments. Such exploration is its own reward – children have fun when playing – but it probably also serves the practical purpose of learning theories with which to predict outcomes of actions. Cognitive robots however have yet to match the performance of human beings at learning and reusing manipulation skills. In this paper, we implement a method that emulates the curiosity drive and uses it as a heuristic to guide (simulated) exploration of a particular task – pouring liquids. The result of this exploration is a collection of symbolic rules linking qualitative descriptions of object arrangements and the pouring action with qualitative descriptions of likely outcomes. The manner in which qualitative descriptions of object arrangements and actions are converted to numerical descriptions for the purpose of simulation parametrization is via probability distributions, which themselves are adjusted in the process of simulated exploration. This allows the grounding of the symbolic descriptions to attempt to adapt itself to the task. The resulting symbolic rules form a theory that, together with the probability distributions that ground it in numerical parametrizations, is intended to be used to predict qualitative outcomes or select manners of pouring towards achieving a goal.

  • When Push Comes to Shove: A Formal Analysis on the Decomposed Conceptual Primitives in Pushing Scenarios


  • A balancing act: Ordering algorithm and image-schematic action descriptors for stacking objects by household robots


  • Thinking in front of the box: Towards intelligent robotic action selection for navigation in complex environments using image-schematic reasoning


  • Deep understanding of everyday activity commands for household robots
    Sebastian Höffner, Robert Porzel, Maria M. Hedblom, Mihai Pomarlan, Vanja Sophie Cangalovic, Johannes Pfau, John A. Bateman, and Rainer Malaka

    IOS Press
    Going from natural language directions to fully specified executable plans for household robots involves a challenging variety of reasoning steps. In this paper, a processing pipeline to tackle these steps for natural language directions is proposed and implemented. It uses the ontological Socio-physical Model of Activities (SOMA) as a common interface between its components. The pipeline includes a natural language parser and a module for natural language grounding. Several reasoning steps formulate simulation plans, in which robot actions are guided by data gathered using human computation. As a last step, the pipeline simulates the given natural language direction inside a virtual environment. The major advantage of employing an overarching ontological framework is that its asserted facts can be stored alongside the semantics of directions, contextual knowledge, and annotated activity models in one central knowledge base. This allows for a unified and efficient knowledge retrieval across all pipeline components, providing flexibility and reasoning capabilities as symbolic knowledge is combined with annotated sub-symbolic models.

  • Asymmetric Hybrids: Dialogues for Computational Concept Combination (Extended Abstract)
    Guendalina Righetti, Daniele Porello, Nicolas Troquard, Oliver Kutz, Maria Hedblom, and Pietro Galliani

    International Joint Conferences on Artificial Intelligence Organization
    When considering two concepts in terms of extensional logic, their combination will often be trivial, returning an empty extension. Consider e.g. “a Fish Vehicle”, i.e., “a Vehicle which is also a Fish”. Still, people use sophisticated strategies to produce new, non-empty concepts. All these strategies involve the human ability to mend the conflicting attributes of the input concepts and to create new properties of the combination. We focus in particular on the case where a Head concept has superior ‘asymmetric’ control over steering the resulting combination (or hybridisation) with a Modifier concept. Specifically, we propose a dialogical model of the cognitive and logical mechanics of this asymmetric form of hybridisation. Its implementation is then evaluated using a combination of example ontologies.

  • Getting On Top of Things: Towards Intelligent Robotic Object Stacking through Image-Schematic Reasoning


  • ISD6 The Image Schema Day 2022


  • Visualising Image Schemas: A Preliminary Look at the Diagrammatic Image Schema Language (DISL)


  • Asymmetric Hybrids: Dialogues for Computational Concept Combination
    Guendalina Righetti, Daniele Porello, Nicolas Troquard, Oliver Kutz, Maria M. Hedblom, and Pietro Galliani

    IOS Press
    When people combine concepts these are often characterised as “hybrid”, “impossible”, or “humorous”. However, when simply considering them in terms of extensional logic, the novel concepts understood as a conjunctive concept will often lack meaning having an empty extension (consider “a tooth that is a chair”, “a pet flower”, etc.). Still, people use different strategies to produce new non-empty concepts: additive or integrative combination of features, alignment of features, instantiation, etc. All these strategies involve the ability to deal with conflicting attributes and the creation of new (combinations of) properties. We here consider in particular the case where a Head concept has superior ‘asymmetric’ control over steering the resulting concept combination (or hybridisation) with a Modifier concept. Specifically, we propose a dialogical approach to concept combination and discuss an implementation based on axiom weakening, which models the cognitive and logical mechanics of this asymmetric form of hybridisation.

  • Cutting Events: Towards Autonomous Plan Adaption by Robotic Agents through Image-Schematic Event Segmentation
    Kaviya Dhanabalachandran, Vanessa Hassouna, Maria M. Hedblom, Michaela Küempel, Nils Leusmann, and Michael Beetz

    ACM
    Autonomous robots struggle with plan adaption in uncertain and changing environments. Although modern robots can make popcorn and pancakes, they are incapable of performing such tasks in unknown settings and unable to adapt action plans if ingredients or tools are missing. Humans are continuously aware of their surroundings. For robotic agents, real-time state updating is time-consuming and other methods for failure handling are required. Taking inspiration from human cognition, we propose a plan adaption method based on event segmentation of the image-schematic states of subtasks within action descriptors. For this, we reuse action plans of the robotic architecture CRAM and ontologically model the involved objects and image-schematic states of the action descriptor cutting. Our evaluation uses a robot simulation of the task of cutting bread and demonstrates that the system can reason about possible solutions to unexpected failures regarding tool use.

  • Deciphering The Cookie Monster: A case study in impossible combinations


  • Panta Rhei: Curiosity-Driven Exploration to Learn the Image-Schematic Affordances of Pouring Liquids


  • Dynamic action selection using image schema-based reasoning for robots


  • Defining concepts: The role of image schemas in object conceptualisation
    Maria M. Hedblom

    Springer International Publishing

  • Introducing isl<sup>FOL</sup>: A Logical Language for Image Schemas
    Maria M. Hedblom

    Springer International Publishing

  • Formal structure: image schemas as families of theories
    Maria M. Hedblom

    Springer International Publishing

  • Modelling conceptualisations: combining image schemas to model events
    Maria M. Hedblom

    Springer International Publishing


RECENT SCHOLAR PUBLICATIONS

  • The Wilhelm Tell Dataset of Affordance Demonstrations
    R Ringe, M Pomarlan, N Tsiogkas, S De Giorgis, M Hedblom, R Malaka
    Proceedings of the 2025 ACM/IEEE International Conference on Human-Robot 2025

  • CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking
    H Hettiarachchi, A Dridi, MM Gaber, P Parsafard, N Bocaneala, ...
    Scientific Data 12 (1), 170 2025

  • ISD8 2024, The Eighth Image Schema Day: Proceedings of the Eighth Image Schema Day, co-located with The 23rd Annual Conference on the Italian AI Association (AI*IA)
    MM Hedblom, O Kutz
    https://ceur-ws.org/Vol-3888/ 2024

  • Beyond Space and Time: An Initial Sketch of Formal Accounts to Non-Spatiotemporal Conceptual Sensory Primitives
    MM Hedblom
    The Eighth Image Schema Day (ISD8@AI*IA24) 3888 2024

  • Proceedings of the 15th International Conference on Computational Creativity
    K Grace, MT Llano, P Pedro Martins, MM Hedblom
    2024

  • Twist and Snap: A Neuro-symbolic System for Affordance Learning of Opening Jars and Bottles
    J Aguirregomezcorta Aina, MM Hedblom
    The Seventh Iberian Robotics Conference (ROBOT 2024) 2024

  • Hanging Around: Cognitive Inspired Reasoning for Reactive Robotics
    M Pomarlan, S De Giorgis, R Ringe, MM Hedblom, N Tsiogkas
    Formal Ontology in Information System (FOIS) 2024

  • The Diagrammatic Image Schema Language (DISL)
    MM Hedblom, F Neuhaus, T Mossakowski
    Spatial Cognition and Computation 2024

  • Discussing the Creativity of AutomaTone: an Interactive Music Generator based on Conway's Game of Life
    G Schaaps, MM Hedblom
    The 15th International Conference on Computational Creativity 2024

  • Every dog has its day: An in-depth analysis of the creative ability of visual generative AI
    MM Hedblom
    Cosmos + Taxis 12 (5-6), 88-103 2024

  • Proceedings of The Eighth Image Schema Dayco-located with The 23rd International Conference of the Italian Association for Artificial Intelligence (AI* IA 2024)
    MM Hedblom, O Kutz
    The Eighth Image Schema Day co-located with The 23rd International 2024

  • Revising Defeasible Theories
    M Pomarlan, MM Hedblom, L Spillner, R Porzel
    Rules and Reasoning: 8th International Joint Conference, RuleML+ RR 2024 2024

  • Formal ontology in information systems: Proceedings of the 13th International Conference (FOIS 2023)
    N Aussenac-Gilles, T Hahmann, A Galton, MM Hedblom
    13th International Conference On Formal Ontology In Information Systems 2023

  • ISD7 2023, The Seventh Image Schema Day: Proceedings of the Seventh Image Schema Day, co-located with The 20th International Conference on Principles of Knowledge
    MM Hedblom, O Kutz
    Seventh Image Schema Day, co-located with The 20th International Conference 2023

  • When Push Comes to Shove: A Formal Analysis on the Decomposed Conceptual Primitives in Pushing Scenarios
    MM Hedblom
    The Seventh Image Schema Day (ISD7) 2023

  • The Silent Expression: The ethical issues with meaningless AI-generated images entering the art scene
    MM Hedblom
    2nd International Conference on the Ethics of Artificial Intelligence 2023

  • Proceedings of the Joint Ontology Workshops 2023 Episode IX: The Quebec Summer of Ontology co-located with the 13th International Conference on Formal Ontology in Information
    R Baratella, S Borgo, F Toyoshima, M Katsumi, E Sanfilippo, G Righetti, ...
    CEUR-WS IAOA Series 2023

  • Using the diagrammatic image schema language for joint human-machine cognition
    T Mossakowski, MM Hedblom, F Neuhaus, SA Arboleda, A Raake
    TU Ilmenau 2023

  • Methodological reflection of the documentalysis of .co.kr from the %WRONG Browser series
    MM Hedblom
    Navigation. Begriffe des digitalen Bildes 2, 33-39 2023

  • Curiously exploring affordance spaces of a pouring task
    M Pomarlan, MM Hedblom, R Porzel
    Expert systems 2022

MOST CITED SCHOLAR PUBLICATIONS

  • Choosing the right path: Image schema theory as a foundation for concept invention
    MM Hedblom, O Kutz, F Neuhaus
    Journal of Artificial General Intelligence 6 (1), 21-54 2015
    Citations: 95

  • Image schemas and concept invention: cognitive, logical, and linguistic investigations
    MM Hedblom
    Otto-von-Guericke University Magdeburg 2019
    Citations: 67

  • Image schemas and concept invention: cognitive, logical, and linguistic investigations
    MM Hedblom
    Springer Nature 2020
    Citations: ept invention: cognitive, logical, and linguistic investigations

  • Image Schema Combinations and Complex Events
    MM Hedblom, O Kutz, R Pealoza, G Guizzardi
    KI-Knstliche Intelligenz, 1-13 2019
    Citations: 62

  • Image schemas in computational conceptual blending
    MM Hedblom, O Kutz, F Neuhaus
    Cognitive Systems Research 39, 42-57 2016
    Citations: 59

  • Between Contact and Support: Introducing aLogic for Image Schemas and Directed Movement
    MM Hedblom, O Kutz, T Mossakowski, F Neuhaus
    AI* IA 2017 Advances in Artificial Intelligence: XVIth International 2017
    Citations: 50

  • Ontology-Based Model Abstraction
    G Guizzardi, G Figueiredo, MM Hedblom, G Poels
    IEEE Thirteen International Conference on Research Challenges in Information 2019
    Citations: 38

  • Breaking into pieces: An ontological approach to conceptual model complexity management
    G Figueiredo, A Duchardt, MM Hedblom, G Guizzardi
    2018 12th International Conference on Research Challenges in Information 2018
    Citations: 38

  • Kinesthetic Mind Reader: A Method to Identify Image Schemas in Natural Language
    D Gromann, MM Hedblom
    Advances in Cognitive Systems 5, Paper 9 2017
    Citations: 32

  • A narrative in three acts: Using combinations of image schemas to model events
    TR Besold, MM Hedblom, O Kutz
    Biologically inspired cognitive architectures 19, 10-20 2017
    Citations: 28

  • Body-Mind-Language: Multilingual Knowledge Extraction Based on Embodied Cognition.
    D Gromann, MM Hedblom
    AIC, 20-33 2017
    Citations: 21

  • In, out and through: formalising some dynamic aspects of the image schema containment
    MM Hedblom, D Gromann, O Kutz
    Proceedings of the 33rd annual ACM symposium on applied computing, 918-925 2018
    Citations: 20

  • Dynamic Action Selection Using Image Schema-based Reasoning for Robots
    MM Hedblom, M Pomarlan, R Porzel, R Malaka, M Beetz
    7th Joint Ontology Workshops 2021
    Citations: 19

  • Image schemas and conceptual dependency primitives: A comparison
    JC Macbeth, D Gromann, MM Hedblom
    2017
    Citations: 18

  • A Formal Representation of Affordances as Reciprocal Dispositions.
    F Toyoshima, A Barton, O Kutz, MM Hedblom
    TriCoLore (C3GI/ISD/SCORE) 2347 2018
    Citations: 16

  • Cutting Events: Towards Autonomous Plan Adaption by Robotic Agents through Image-Schematic Event Segmentation
    K Dhanabalachandran, V Hassouna, MM Hedblom, M Kmpel, ...
    The Eleventh International Conference on Knowledge Capture 2021
    Citations: 14

  • The Mouse and the Ball-Towards a Cognitively-Based and Ontologically-Grounded Logic of Agency
    O Kutz, N Troquard, MM Hedblom, D Porello
    Formal Ontology in Information Systems, 141-148 2018
    Citations: 14

  • Asymmetric hybrids: Dialogues for computational concept combination
    G Righetti, D Porello, N Troquard, O Kutz, MM Hedblom, P Galliani
    Formal Ontology in Information Systems (FOIS) 344, 81-96 2021
    Citations: 12

  • Breaking down finance: A method for concept simplification by identifying movement structures from the image schema path-following
    D Gromann, MM Hedblom
    JOWO 2016 2016
    Citations: 12

  • Image schemas as families of theories
    MM Hedblom, O Kutz, F Neuhaus
    Besold, TR; Khnberger, K.-U.; Schorlemmer, M, 19-33 2015
    Citations: 12