Processing Natural Language About Ongoing Actions

Actions may not proceed as planned; they may be interrupted, resumed or overridden. This is a challenge to handle in a natural language understanding system. We describe extensions to an existing implementation for the control of autonomous systems by natural language, to enable such systems to handle incoming language requests regarding actions. Language Communication with Autonomous Systems (LCAS) has been extended with support for X-nets, parameterized executable schemas representing actions. X-nets enable the system to control actions at a desired level of granularity, while providing a mechanism for language requests to be processed asynchronously. Standard semantics supported include requests to stop, continue, or override the existing action. The specific domain demonstrated is the control of motion of a simulated robot, but the approach is general, and could be applied to other domains.

[1]  Jerome A. Feldman,et al.  Application-Independent and Integration-Friendly Natural Language Understanding , 2016, GCAI.

[2]  Jerome A. Feldman,et al.  Exploiting deep semantics and compositionality of natural language for Human-Robot-Interaction , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[3]  Yi Li,et al.  Robot Learning Manipulation Action Plans by "Watching" Unconstrained Videos from the World Wide Web , 2015, AAAI.

[4]  Yiannis Aloimonos,et al.  Minimalist plans for interpreting manipulation actions , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Srini Narayanan,et al.  Communicating with Executable Action Representations , 2013, AAAI Spring Symposium: Designing Intelligent Robots.

[6]  Douglas Summers-Stay,et al.  Using a minimal action grammar for activity understanding in the real world , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  B. Bergen Louder Than Words: The New Science of How the Mind Makes Meaning , 2012 .

[8]  Yiannis Aloimonos,et al.  The minimalist grammar of action , 2012, Philosophical Transactions of the Royal Society B: Biological Sciences.

[9]  Gilberto Echeverria,et al.  Modular open robots simulation engine: MORSE , 2011, 2011 IEEE International Conference on Robotics and Automation.

[10]  Nicholas J. Dingle,et al.  PIPE2: a tool for the performance evaluation of generalised stochastic Petri Nets , 2009, PERV.

[11]  Steven Kumar Sinha,et al.  Answering Questions about Complex Events , 2008 .

[12]  G. Hesslow Conscious thought as simulation of behaviour and perception , 2002, Trends in Cognitive Sciences.

[13]  T. Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[14]  Jerome A. Feldman,et al.  Natural Language Understanding and Communication for Multi-Agent Systems , 2015, AAAI Fall Symposia.

[15]  Huda Khayrallah,et al.  Natural Language For Human Robot Interaction , 2015 .

[16]  S. Narayanan Mind changes: A simulation semantics account of counterfactuals , 2010 .

[17]  Nancy Chang,et al.  A computational model of the emergence of early constructions , 2008 .

[18]  Ellen Dodge,et al.  A Neural Theory of Language and Embodied Construction Grammar , 2008 .

[19]  Jerome A. Feldman,et al.  Best-fit constructional analysis , 2008 .

[20]  Catalina M. Lladó,et al.  PIPE v 2 . 5 : a Petri Net Tool for Performance Modeling , 2007 .

[21]  Moore Hall Honolulu Simulated Action in an Embodied Construction Grammar , 2004 .

[22]  Nancy Chang,et al.  Context-Driven Construction Learning , 2004 .