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Onward! 2018
Wed 7 - Thu 8 November 2018 Boston, Massachusetts, United States
co-located with SPLASH 2018
Wed 7 Nov 2018 14:00 - 14:30 at The Loft - DSLs and Language Design Principles Chair(s): Stefan Marr

We present an expressive, concise, and extendable domain specific language for planning of assembly systems, such as industrial human robot cooperation. Increased flexibility requirements in manufacturing processes call for more automation at the description and planning stages of manufacturing. Procedural models are good candidates to meet this demand as programs offer a high degree of flexibility and are easily composed.

Furthermore, we aim to make our programs close to declarative specification and integrate automatic reasoning tools to help the users. The constraints come both from specific programs and pre-existing knowledge base from the target domain. The case of human robot collaboration is interesting as there is a number of constraints and regulations around this domain. Unfortunately, automated reasoners are often too unpredictable and cannot be used directly by non-experts.

In this paper, we present our domain specific language ``Tool Ontology and Optimization Language'' (TOOL) and describe how we integrated automated reasoners and planners in a way that makes them accessible to users which have little programming knowledge, but expertise in manufacturing domain and no previous experience with or knowledge about the underlying reasoners. We present encouraging results by applying TOOL to a case study from the automotive and aerospace industry.

Wed 7 Nov

Displayed time zone: Guadalajara, Mexico City, Monterrey change

13:30 - 15:00
DSLs and Language Design Principles Onward! Papers at The Loft
Chair(s): Stefan Marr University of Kent
The Open Authorial Principle - Supporting Networks of Authors in Creating Externalisable Designs
Onward! Papers
Antranig Basman , Clayton Lewis University of Colorado Boulder, Colin Clark
Tool: Accessible Automated Reasoning for Human Robot Collaboration
Onward! Papers