Combinatorial Planning with Numerical Parameter Optimization for Local Control in Multi-agent Systems

Abstract Planning with numeric state variables and goal systems today still poses a challenging task within the field of computational intelligence. In this paper a two-tier planning system is presented that enables the optimization of continuous numeric action parameters in combinatorially enumerated plans. It allows resorting to a “satisficing” strategy by means of partial execution and subsequent repair of infeasible plans in order to deal with certain difficulties concerning reliable and fast detection of action applicability that arise when planning with real-valued action parameters. The functioning of the system is evaluated in a multi-agent simulation of a shop floor control scenario with focus on the effects the possible problem cases and the satisficing approach have on attained plan quality.

[1]  Mordecai Avriel,et al.  Nonlinear programming , 1976 .

[2]  Eldon Hansen,et al.  Global optimization using interval analysis , 1992, Pure and applied mathematics.

[3]  Huaxin Liu,et al.  Modelling dynamic bottlenecks in production networks , 2011, Int. J. Comput. Integr. Manuf..

[4]  Guillaume Melquiond,et al.  The design of the Boost interval arithmetic library , 2006, Theor. Comput. Sci..

[5]  Till Becker,et al.  A classification pattern for autonomous control methods in logistics , 2010, Logist. Res..

[6]  Matthias Ehrgott,et al.  Multicriteria Optimization , 2005 .

[7]  Malte Helmert,et al.  Decidability and Undecidability Results for Planning with Numerical State Variables , 2002, PuK.

[8]  Florian Pantke,et al.  Intelligent Agent Control and Coordination with User-Configurable Key Performance Indicators , 2009, LDIC.

[9]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[10]  H. Simon,et al.  Theories of Decision-Making in Economics and Behavioural Science , 1966 .

[11]  Jörg Hoffmann,et al.  On the Instantiation of ADL Operators Involving Arbitrary First-Order Formulas , 2000, PuK.

[12]  Ingo J. Timm,et al.  Integrated Process Planning and Production Control , 2006 .

[13]  Frédéric Benhamou,et al.  Algorithm 852: RealPaver: an interval solver using constraint satisfaction techniques , 2006, TOMS.

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

[15]  Klaus-Dieter Thoben,et al.  Dynamics in Logistics, Proceedings of the 4th International Conference LDIC 2014, Bremen, Germany, February 10-14, 2014 , 2016, LDIC.

[16]  Malte Helmert,et al.  Understanding Planning Tasks: Domain Complexity and Heuristic Decomposition , 2008, Lecture Notes in Computer Science.

[17]  Arne Schuldt,et al.  Multiagent Coordination Enabling Autonomous Logistics , 2011, KI - Künstliche Intelligenz.

[18]  Paolo Traverso,et al.  Automated planning - theory and practice , 2004 .

[19]  R. Baker Kearfott,et al.  Introduction to Interval Analysis , 2009 .