The compilability framework

Several problems in AI can be structured in two parts based on the nature of their data, being it fixed or varying. As an example, in automated planning an instance is made of the actions that can be performed and the specific initial state and goal; given a domain of application, the actions will stay the same, while over time planning may be required to be performed several time with differing initial and final states. The compilability classes formalize the complexity of such problems, and can also be used to establish the relative size of certain data structures and the space efficiency of knowledge representation formalisms. In this paper we review the compilability framework and its applications in various areas of Artificial Intelligence.

[1]  Marco Cadoli,et al.  A Survey on Knowledge Compilation , 1997, AI Commun..

[2]  Francesco M. Donini,et al.  Space Efficiency of Propositional Knowledge Representation Formalisms , 2000, J. Artif. Intell. Res..

[3]  Hubie Chen,et al.  A Theory of Average-Case Compilability in Knowledge Representation , 2003, IJCAI.

[4]  Bernhard Nebel.  Compilation Schemes: A Theoretical Tool for Assessing the Expressive Power of Planning Formalisms , 1999, KI.

[5]  Ulrich Endriss,et al.  Compactly representing utility functions using weighted goals and the max aggregator , 2010, Artif. Intell..

[6]  Carsten Lutz,et al.  Updating Description Logic ABoxes , 2006, KR.

[7]  Bernhard Nebel,et al.  In Defense of PDDL Axioms , 2003, IJCAI.

[8]  Paolo Liberatore.  Monotonic reductions, representative equivalence, and compilation of intractable problems , 2001, JACM.

[9]  Hubie Chen.  Parameterized Compilability , 2005, IJCAI.

[10]  Kewen Wang,et al.  Semantic forgetting in answer set programming , 2008, Artif. Intell..

[11]  Bernhard Nebel.  What Is the Expressive Power of Disjunctive Preconditions? , 1999, ECP.

[12]  Francesco M. Donini,et al.  On Compact Representations of Propositional Circumscription , 1995, STACS.

[13]  Paolo Liberatore,et al.  On the complexity of case-based planning , 2004, J. Exp. Theor. Artif. Intell..

[14]  Bernhard Nebel,et al.  On the Compilability and Expressive Power of Propositional Planning Formalisms , 2000, J. Artif. Intell. Res..

[15]  Pierre Marquis,et al.  Compiling propositional weighted bases , 2004, Artif. Intell..

[16]  Francesco M. Donini,et al.  k-approximating circuits , 2006, IEEE Transactions on Computers.

[17]  Francesco M. Donini,et al.  Preprocessing of Intractable Problems , 2002, Inf. Comput..

[18]  Yann Chevaleyre,et al.  Representing Utility Functions via Weighted Goals , 2009, Math. Log. Q..

[19]  Carsten Lutz,et al.  Foundations of instance level updates in expressive description logics , 2011, Artif. Intell..

[20]  Toni Mancini,et al.  Knowledge compilation = query rewriting + view synthesis , 2002, PODS '02.

[21]  Matthew Suderman,et al.  Compositional Belief Update , 2007, FLAIRS Conference.

[22]  Nic Wilson,et al.  From Preference Logics to Preference Languages, and Back , 2010, KR.