Increasing the Numeric Expressiveness of the Planning Domain Definition Language

The technology of artificial intelligence (AI) planning is being adopted across many different disciplines. This has resulted in the wider use of the Planning Domain Definition Language (PDDL), where it is being used to model planning problems of different natures. One such area where AI planning is particularly attractive is engineering, where the optimisation problems are mathematically rich. The example used throughout this paper is the optimisation (minimisation) of machine tool measurement uncertainty. This planning problem highlights the limits of PDDL's numerical expressiveness in the absence of the square root function. A workaround method using the Babylonian algorithm is then evaluated before the extension of PDDL to include more mathematics functions is discussed.

[1]  Andrew Coles,et al.  COLIN: Planning with Continuous Linear Numeric Change , 2012, J. Artif. Intell. Res..

[2]  Maria Fox,et al.  PDDL+ : Modelling Continuous Time-dependent Effects , 1999 .

[3]  Simon Parkinson,et al.  Hierarchical Task Based Process Planning For Machine Tool Calibration , 2011 .

[4]  Simon Parkinson,et al.  Automatic planning for machine tool calibration: A case study , 2012, Expert Syst. Appl..

[5]  Maria Fox,et al.  PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains , 2003, J. Artif. Intell. Res..

[6]  Simon Parkinson,et al.  Representing the process of machine tool calibration on first-order logic , 2011, The 17th International Conference on Automation and Computing.

[7]  Maria Fox,et al.  Automatic Construction of Efficient Multiple Battery Usage Policies , 2011, IJCAI.

[8]  Ivan Serina,et al.  Planning Through Stochastic Local Search and Temporal Action Graphs in LPG , 2003, J. Artif. Intell. Res..

[9]  Andrew Coles,et al.  Automated Planning for Liner Shipping Fleet Repositioning , 2012, ICAPS.

[10]  M Goldsmith,et al.  A beginner's guide to measurement. , 2010 .

[11]  Simon Parkinson,et al.  The Application of Automated Planning to Machine Tool Calibration , 2012, ICAPS.

[12]  Wheeler Ruml,et al.  On-line Planning and Scheduling: An Application to Controlling Modular Printers , 2008, AAAI.

[13]  N. P. Muravskaya,et al.  Estimating uncertainties in preparing calibration and test solutions of metal ions and adenosine-5′-triphosphate , 2008 .