A Framework for Hybrid Planning

Sentential and analogical representations constitute two complementary formalisms for describing problems and domains. Experimental evidence indicates that different domain types can have their most efficient encoding in different representations. While real-world problems typically involve a combination of different types of domains, all modern planning domain description languages are purely sentential. This paper proposes a framework for planning with hybrid models, in which sentential and analogical descriptions can be integrated and used interchangeably, thereby allowing a more efficient description of realistically complex planning problems

Intelligent Systems XXI (Proc. of AI-2004), pp.214-227. Springer-Verlag, 2005 A Framework for Hybrid Planning

Sentential and analogical representations constitute two complementary formalisms for describing problems and domains. Experimental evidence indicates that different domain types can have their most efficient encoding in different representations. While real-world problems typically involve a combination of different types of domains, all modern planning domain description languages are purely sentential. This paper proposes a framework for planning with hybrid models, in which sentential and analogical descriptions can be integrated and used interchangeably, thereby allowing a more efficient description of realistically complex planning problems.