Maria M. Hedblom
Verified @gmail.com
Department of Computing
School of Engineering at Jonkoping University
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Theoretical Computer Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Mihai Pomarlan, Maria M. Hedblom, Laura Spillner, and Robert Porzel
Springer Nature Switzerland
Maria M. Hedblom, Fabian Neuhaus, and Till Mossakowski
Informa UK Limited
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.
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.
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.
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.
Maria M. Hedblom
Springer International Publishing
Maria M. Hedblom
Springer International Publishing
Maria M. Hedblom
Springer International Publishing
Maria M. Hedblom
Springer International Publishing
Maria M. Hedblom
Springer International Publishing
Maria M. Hedblom
Springer International Publishing
Maria M. Hedblom
Springer International Publishing
Maria M. Hedblom
Springer International Publishing
Maria M. Hedblom, Oliver Kutz, Rafael Peñaloza, and Giancarlo Guizzardi
Springer Science and Business Media LLC