PRODIGY 4.0: The Manual and Tutorial. PRODIGY is a general-purpose problem-solving architecture that serves as a basis for research in planning, machine learning, apprentice-type knowledge-refinement interfaces, and expert systems. This document is a manual for the latest version of the PRODIGY system, PRODIGY4.0, and includes descriptions of the PRODIGY representation language, control structure, user interface, abstraction module, and other features. The tutorial style is meant to provide the reader with the ability to run PRODIGY and make use of all the basic features, as well as gradually learning the more esoteric aspects of PRODIGY4.0.

References in zbMATH (referenced in 37 articles )

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  1. Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders: Computing programs for generalized planning using a classical planner (2019)
  2. Malone, Brandon; Kangas, Kustaa; Järvisalo, Matti; Koivisto, Mikko; Myllymäki, Petri: Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction (2018)
  3. Agostini, Alejandro; Torras, Carme; Wörgötter, Florentin: Efficient interactive decision-making framework for robotic applications (2017)
  4. Zhuo, Hankz Hankui; Kambhampati, Subbarao: Model-lite planning: case-based vs. model-based approaches (2017)
  5. Ji, Jianmin; Lin, Fangzhen: Position systems in dynamic domains (2015)
  6. Yang, Liu; Hanneke, Steve; Carbonell, Jaime: A theory of transfer learning with applications to active learning (2013)
  7. Kitzelmann, Emanuel: A combined analytical and search-based approach for the inductive synthesis of functional programs (2011) ioport
  8. Silva, Ricardo; Heller, Katherine; Ghahramani, Zoubin; Airoldi, Edoardo M.: Ranking relations using analogies in biological and information networks (2010)
  9. Klenk, Matthew; Forbus, Ken: Analogical model formulation for transfer learning in AP physics (2009) ioport
  10. Roberts, Mark; Howe, Adele: Learning from planner performance (2009)
  11. Melis, Erica; Meier, Andreas; Siekmann, Jörg: Proof planning with multiple strategies (2008)
  12. Bod, Rens: Getting rid of derivational redundancy or how to solve Kuhn’s problem (2007) ioport
  13. Gupta, Manish; Fu, Jicheng; Bastani, Farokh B.; Khan, Latifur; Yen, I-Ling: Rapid goal-oriented automated software testing using MEA-graph planning. (2007) ioport
  14. Camacho, David; Aler, Ricardo; Borrajo, Daniel; Molina, José M.: Multi-agent plan based information gathering (2006)
  15. Camacho, David; Aler, Ricardo; Borrajo, Daniel; Molina, José M.: Multi-agent plan based information gathering (2006) ioport
  16. Craw, Susan; Wiratunga, Nirmalie; Rowe, Ray C.: Learning adaptation knowledge to improve case-based reasoning (2006)
  17. Santos, Eugene; DeLoach, Scott A.; Cox, Michael T.: Achieving dynamic, multi-commander, multi-mission planning and execution (2006) ioport
  18. Urdiales, Cristina; Pérez, Eduardo J.; Vázquez-Salceda, Javier; Sànchez-Marrè, Miquel; Hernández, Francisco Sandoval: A purely reactive navigation scheme for dynamic environments using case-based reasoning. (2006) ioport
  19. Stolle, Reinhard; Hogan, Apollo; Bradley, Elizabeth: Agenda control for heterogeneous reasoners (2005)
  20. Wu, Dekai: MT model space: statistical versus compositional versus example-based machine translation (2005) ioport

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