Post-Proceedings of the First International Workshop on Learning and Nonmonotonic Reasoning

Knowledge Representation and Reasoning and Machine Learning are two important fields in AI. Nonmonotonic logic programming (NMLP) and Answer Set Programming (ASP) provide formal languages for representing and reasoning with commonsense knowledge and realize declarative problem solving in AI. On the other side, Inductive Logic Programming (ILP) realizes Machine Learning in logic programming, which provides a formal background to inductive learning and the techniques have been applied to the fields of relational learning and data mining. Generally speaking, NMLP and ASP realize nonmonotonic reasoning while lack the ability of learning. By contrast, ILP realizes inductive learning while most techniques have been developed under the classical monotonic logic. With this background, some researchers attempt to combine techniques in the context of nonmonotonic ILP. Such combination will introduce a learning mechanism to programs and would exploit new applications on the NMLP side, while on the ILP side it will extend the representation language and enable us to use existing solvers. Cross-fertilization between learning and nonmonotonic reasoning can also occur in such as the use of answer set solvers for ILP, speed-up learning while running answer set solvers, learning action theories, learning transition rules in dynamical systems, abductive learning, learning biological networks with inhibition, and applications involving default and negation. This workshop is the first attempt to provide an open forum for the identification of problems and discussion of possible collaborations among researchers with complementary expertise. The workshop was held on September 15th of 2013 in Corunna, Spain. This post-proceedings contains five technical papers (out of six accepted papers) and the abstract of the invited talk by Luc De Raedt.

[1]  Gabriele Kern-Isberner,et al.  On probabilistic inference in relational conditional logics , 2012, Log. J. IGPL.

[2]  Takahisa Toda,et al.  Hypergraph Transversal Computation with Binary Decision Diagrams , 2013, SEA.

[3]  Katsumi Inoue,et al.  Completing causal networks by meta-level abduction , 2013, Machine Learning.

[4]  Vladimir Lifschitz,et al.  Answer set programming and plan generation , 2002, Artif. Intell..

[5]  Luis Fariñas del Cerro,et al.  A logical model for metabolic networks with inhibition , 2013 .

[6]  Ashish Tiwari,et al.  Analyzing Pathways Using SAT-Based Approaches , 2007, AB.

[7]  V. S. Subrahmanian,et al.  Stable Semantics for Probabilistic Deductive Databases , 1994, Inf. Comput..

[8]  Chiaki Sakama,et al.  Learning from interpretation transition , 2013, Machine Learning.

[9]  Fahiem Bacchus,et al.  Lp, a logic for representing and reasoning with statistical knowledge , 1990, Comput. Intell..

[10]  David A. Rosenblueth,et al.  "Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis , 2011, BMC Bioinformatics.

[11]  David Poole,et al.  The Independent Choice Logic for Modelling Multiple Agents Under Uncertainty , 1997, Artif. Intell..

[12]  Leonid Khachiyan,et al.  On the Complexity of Dualization of Monotone Disjunctive Normal Forms , 1996, J. Algorithms.

[13]  Alberto Pettorossi,et al.  Synthesizing Concurrent Programs using Answer Set Programming , 2012, CILC.

[14]  Luis Fariñas del Cerro,et al.  Information About a Given Entity: From Semantics Towards Automated Deduction , 2010, J. Log. Comput..

[15]  Taisuke Sato,et al.  PRISM: A Language for Symbolic-Statistical Modeling , 1997, IJCAI.

[16]  Steffen Klamt,et al.  A methodology for the structural and functional analysis of signaling and regulatory networks , 2006, BMC Bioinformatics.

[17]  Martin Gebser,et al.  Conflict-Driven Answer Set Solving , 2007, IJCAI.

[18]  B. Di Camillo,et al.  A Boolean Approach to Linear Prediction for Signaling Network Modeling , 2010, PloS one.

[19]  Katsumi Inoue,et al.  Evaluation of the Prediction of Gene Knockout Effects by Minimal Pathway Enumeration , 2012 .

[20]  Edmund M. Clarke,et al.  Design and Synthesis of Synchronization Skeletons Using Branching Time Temporal Logic , 2008, 25 Years of Model Checking.

[21]  Katsumi Inoue,et al.  Linear Resolution for Consequence Finding , 1992, Artif. Intell..

[22]  Ilya Shmulevich,et al.  On Learning Gene Regulatory Networks Under the Boolean Network Model , 2003, Machine Learning.

[23]  Nils J. Nilsson,et al.  Probabilistic Logic * , 2022 .

[24]  Stephen Muggleton,et al.  Developing a Logical Model of Yeast Metabolism , 2001, Electron. Trans. Artif. Intell..

[25]  David Avis,et al.  Reverse Search for Enumeration , 1996, Discret. Appl. Math..

[26]  Peter K. Sorger,et al.  Logic-Based Models for the Analysis of Cell Signaling Networks† , 2010, Biochemistry.

[27]  Katsumi Inoue,et al.  Non-monotone Dualization via Monotone Dualization , 2012, ILP.

[28]  Marius Thomas Lindauer,et al.  Potassco: The Potsdam Answer Set Solving Collection , 2011, AI Commun..

[29]  Alberto L. Sangiovanni-Vincentelli,et al.  Modeling digital substrate noise injection in mixed-signal IC's , 1999, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[30]  Nicolas Le Novère,et al.  Systems Biology Graphical Notation: Activity Flow language Level 1 , 2009 .

[31]  Age K. Smilde,et al.  Metabolic network discovery through reverse engineering of metabolome data , 2009, Metabolomics.

[32]  Eugenia Ternovska,et al.  Model Checking Abstract State Machines with Answer Set Programming , 2007, Fundam. Informaticae.

[33]  Luc De Raedt,et al.  ProbLog: A Probabilistic Prolog and its Application in Link Discovery , 2007, IJCAI.

[34]  Yves Pommier,et al.  Targeting chk2 kinase: molecular interaction maps and therapeutic rationale. , 2005, Current pharmaceutical design.

[35]  Bart Selman,et al.  Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints , 2006, NIPS.

[36]  S. Kauffman Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[37]  Maxim Teslenko,et al.  A SAT-Based Algorithm for Finding Attractors in Synchronous Boolean Networks , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[38]  Martin Gebser,et al.  Conflict-driven answer set solving: From theory to practice , 2012, Artif. Intell..

[39]  Katsumi Inoue,et al.  Heuristic Inverse Subsumption in Full-Clausal Theories , 2012, ILP.

[40]  Lise Getoor,et al.  Learning Probabilistic Relational Models , 1999, IJCAI.

[41]  K. Sachs,et al.  Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.

[42]  Henry A. Kautz,et al.  Performing Bayesian Inference by Weighted Model Counting , 2005, AAAI.

[43]  Stephen Muggleton,et al.  Modelling Inhibition in Metabolic Pathways Through Abduction and Induction , 2004, ILP.

[44]  Fausto Giunchiglia,et al.  NUSMV: a new symbolic model checker , 2000, International Journal on Software Tools for Technology Transfer.

[45]  Joseph Sifakis,et al.  Specification and verification of concurrent systems in CESAR , 1982, Symposium on Programming.

[46]  Joseph Y. Halpern An Analysis of First-Order Logics of Probability , 1989, IJCAI.

[47]  Martin Erwig,et al.  Causal Reasoning with Neuron Diagrams , 2010, 2010 IEEE Symposium on Visual Languages and Human-Centric Computing.

[48]  G. Favre,et al.  HuR-dependent loading of miRNA RISC to the mRNA encoding the Ras-related small GTPase RhoB controls its translation during UV-induced apoptosis , 2011, Cell Death and Differentiation.

[49]  Luis Fariñas del Cerro,et al.  An Inference Rule for Hypothesis Generation , 1991, IJCAI.

[50]  Takeaki Uno A Practical Fast Algorithm for Enumerating Minimal SetCoverings , 2002 .

[51]  Pedro M. Domingos,et al.  Efficient Weight Learning for Markov Logic Networks , 2007, PKDD.

[52]  Jane Hillston,et al.  Translation from the Quantified Implicit Process Flow Abstraction in SBGN-PD Diagrams to Bio-PEPA Illustrated on the Cholesterol Pathway , 2011, Trans. Comp. Sys. Biology.

[53]  Stephen Muggleton,et al.  Theory Completion Using Inverse Entailment , 2000, ILP.

[54]  Ron Shamir,et al.  Chain functions and scoring functions in genetic networks , 2003, ISMB.

[55]  Christine Froidevaux,et al.  Towards a logic-based method to infer provenance-aware molecular networks , 2012 .

[56]  François Fages,et al.  Machine Learning Biochemical Networks from Temporal Logic Properties , 2006, Trans. Comp. Sys. Biology.

[57]  Ronan M. T. Fleming,et al.  A community-driven global reconstruction of human metabolism , 2013, Nature Biotechnology.

[58]  Yukiko Matsuoka,et al.  Software support for SBGN maps: SBGN-ML and LibSBGN , 2012, Bioinform..

[59]  Stephen Muggleton,et al.  Learning Stochastic Logic Programs , 2000, Electron. Trans. Artif. Intell..

[60]  Katsumi Inoue,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Logic Programming for Boolean Networks , 2022 .

[61]  Julio Saez-Rodriguez,et al.  Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli , 2011, PLoS Comput. Biol..

[62]  Chris Cornelis,et al.  Modelling gene and protein regulatory networks with Answer Set Programming , 2011, Int. J. Data Min. Bioinform..

[63]  Luc De Raedt,et al.  Bayesian Logic Programs , 2001, ILP Work-in-progress reports.

[64]  Shin-ichi Minato,et al.  Zero-suppressed BDDs and their applications , 2001, International Journal on Software Tools for Technology Transfer.

[65]  Thomas Eiter,et al.  Abduction and the Dualization Problem , 2003, Discovery Science.

[66]  Takeaki Uno,et al.  Efficient algorithms for dualizing large-scale hypergraphs , 2011, Discret. Appl. Math..

[67]  Sarala M. Wimalaratne,et al.  The Systems Biology Graphical Notation , 2009, Nature Biotechnology.

[68]  Chiaki Sakama,et al.  Brave induction: a logical framework for learning from incomplete information , 2009, Machine Learning.

[69]  Takeaki Uno,et al.  Enumerating Maximal Frequent Sets Using Irredundant Dualization , 2003, Discovery Science.

[70]  Georg Gottlob,et al.  Computational aspects of monotone dualization: A brief survey , 2008, Discret. Appl. Math..

[71]  Katsumi Inoue,et al.  SOLAR: An automated deduction system for consequence finding , 2010, AI Commun..

[72]  Luc De Raedt,et al.  Logical Settings for Concept-Learning , 1997, Artif. Intell..

[73]  Yves Pommier,et al.  Molecular interaction map of the p53 and Mdm2 logic elements, which control the Off-On switch of p53 in response to DNA damage. , 2005, Biochemical and biophysical research communications.

[74]  Luc De Raedt,et al.  Probabilistic Inductive Logic Programming , 2004, Probabilistic Inductive Logic Programming.

[75]  Robert Demolombe Syntactical characterization of a subset of domain-independent formulas , 1992, JACM.

[76]  Joohyung Lee,et al.  System f2lp - Computing Answer Sets of First-Order Formulas , 2009, LPNMR.

[77]  Stijn Heymans,et al.  Synthesis from Temporal Specifications Using Preferred Answer Set Programming , 2005, ICTCS.

[78]  James Cussens,et al.  Parameter Estimation in Stochastic Logic Programs , 2001, Machine Learning.

[79]  J. Borwein,et al.  Two-Point Step Size Gradient Methods , 1988 .

[80]  D. di Bernardo,et al.  How to infer gene networks from expression profiles , 2007, Molecular systems biology.

[81]  Katsumi Inoue,et al.  Hypothesizing about Causal Networks with Positive and Negative Effects by Meta-level Abduction , 2010, ILP.

[82]  Oliver Ray,et al.  Automated Abduction in Scientific Discovery , 2007, Model-Based Reasoning in Science, Technology, and Medicine.

[83]  Tania G. Leishman,et al.  The Emergence of Social Consensus in Boolean Networks , 2007, 2007 IEEE Symposium on Artificial Life.

[84]  Katsumi Inoue,et al.  Probabilistic Rule Learning in Nonmonotonic Domains , 2011, CLIMA.

[85]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[86]  Ruth Nussinov,et al.  A formal MIM specification and tools for the common exchange of MIM diagrams: an XML-Based format, an API, and a validation method , 2011, BMC Bioinformatics.

[87]  Nicolas Le Novère,et al.  Model storage, exchange and integration , 2006, BMC neuroscience.

[88]  Oliver Ray,et al.  Logic-Based Steady-State Analysis and Revision of Metabolic Networks with Inhibition , 2010, 2010 International Conference on Complex, Intelligent and Software Intensive Systems.

[89]  Katsumi Inoue,et al.  Induction as Consequence Finding , 2004, Machine Learning.

[90]  François Fages,et al.  BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge , 2006, Bioinform..

[91]  Akihiro Yamamoto,et al.  Hypothesis finding based on upward refinement of residue hypotheses , 2003, Theor. Comput. Sci..

[92]  Johan van Benthem,et al.  Modal Languages and Bounded Fragments of Predicate Logic , 1998, J. Philos. Log..

[93]  Ron Rymon An SE-tree-based prime implicant generation algorithm , 2005, Annals of Mathematics and Artificial Intelligence.

[94]  Enrico Pontelli,et al.  Hybrid Probabilistic Logic Programs with Non-monotonic Negation , 2005, ICLP.