An Abstract Specification of VoxML as an Annotation Language

VoxML is a modeling language used to map natural language expressions into real-time visualizations using commonsense semantic knowledge of objects and events. Its utility has been demonstrated in embodied simulation environments and in agent-object interactions in situated multimodal human-agent collaboration and communication. It introduces the notion of object affordance (both Gibsonian and Telic) from HRI and robotics, as well as the concept of habitat (an object's context of use) for interactions between a rational agent and an object. This paper aims to specify VoxML as an annotation language in general abstract terms. It then shows how it works on annotating linguistic data that express visually perceptible human-object interactions. The annotation structures thus generated will be interpreted against the enriched minimal model created by VoxML as a modeling language while supporting the modeling purposes of VoxML linguistically.

[1]  Nikhil Krishnaswamy,et al.  Grounding and Distinguishing Conceptual Vocabulary Through Similarity Learning in Embodied Simulations , 2023, IWCS.

[2]  J. Pustejovsky,et al.  Grounding human-object interaction to affordance behavior in multimodal datasets , 2023, Frontiers in Artificial Intelligence.

[3]  Nikhil Krishnaswamy,et al.  Detecting and Accommodating Novel Types and Concepts in an Embodied Simulation Environment , 2022, ArXiv.

[4]  J. Pustejovsky,et al.  Affordance embeddings for situated language understanding , 2022, Frontiers in Artificial Intelligence.

[5]  James Pustejovsky,et al.  Embodied Human Computer Interaction , 2021, KI - Künstliche Intelligenz.

[6]  James Pustejovsky,et al.  Situated Multimodal Control of a Mobile Robot: Navigation through a Virtual Environment , 2020, ArXiv.

[7]  James Pustejovsky,et al.  Diana's World: A Situated Multimodal Interactive Agent , 2020, AAAI.

[8]  James Pustejovsky,et al.  User-Aware Shared Perception for Embodied Agents , 2019, 2019 IEEE International Conference on Humanized Computing and Communication (HCC).

[9]  Kiyong Lee,et al.  Semantic Annotation of Anaphoric Links in Language , 2017 .

[10]  James Pustejovsky,et al.  VoxML: A Visualization Modeling Language , 2016, LREC.

[11]  채현식,et al.  What is the Lexicon , 2013 .

[12]  三嶋 博之 The theory of affordances , 2008 .

[13]  Graham Katz,et al.  Towards a Denotational Semantics for TimeML , 2005, Annotating, Extracting and Reasoning about Time and Events.

[14]  Adam Pease,et al.  Towards a standard upper ontology , 2001, FOIS.

[15]  James Pustejovsky,et al.  The Generative Lexicon , 1995, CL.

[16]  James Pustejovsky,et al.  Lexical Knowledge Representation and Natural Language Processing , 1993, Artif. Intell..

[17]  J. Pustejovsky,et al.  The VoxWorld Platform for Multimodal Embodied Agents , 2022, LREC.

[18]  Bruce A. Draper,et al.  Communicating and Acting: Understanding Gesture in Simulation Semantics , 2017, IWCS.

[19]  Nikhil Krishnaswamy,et al.  Monte Carlo Simulation Generation Through Operationalization of Spatial Primitives , 2017 .

[20]  James Pustejovsky,et al.  Object Embodiment in a Multimodal Simulation , 2016 .

[21]  J. Pustejovsky Dynamic Event Structure and Habitat Theory , 2013 .

[22]  James Pustejovsky,et al.  ISO/DIS 24617-1 Language Resources management - semantic annotation framework Part 1 : Time and events , 2009 .

[23]  Bernhard Beckert,et al.  Dynamic Logic , 2007, The KeY Approach.

[24]  Mark de Berg,et al.  Computational geometry: algorithms and applications , 1997 .