SOLAR: A Consequence Finding System for Advanced Reasoning
ABSTRACT SOLAR is an efficient first-order consequence finding system based on a connection tableau format with Skip operation. Consequence finding 1,2,3,4 is a generalization of refutation finding or theorem proving, and is useful for many reasoning tasks such as knowledge compilation, inductive logic programming, abduction. One of the most significant calculus of consequence finding is SOL 2. SOL is complete for consequence finding and can find all minimal-length consequences with respect to subsumption. SOLAR (SOL for Advanced Reasoning) is an efficient implementation of SOL and can avoid producing non-minimal/redundant consequences due to various state of the art pruning methods, such as skip-regularity, local failure caching, folding-up (see 5,6).
Conference Paper: Discovering Rules by Meta-level Abduction.[Show abstract] [Hide abstract]
ABSTRACT: This paper addresses discovery of unknown relations from incomplete network data by abduction. Given a network information such as causal relations and metabolic pathways, we want to infer missing links and nodes in the network to account for observations. To this end, we introduce a framework of meta-level abduction, which performs abduction in the meta level. This is implemented in SOLAR, an automated deduction system for consequence finding, using a first-order representation for algebraic properties of causality and the full-clausal form of network information and constraints. Meta-level abduction by SOLAR is powerful enough to infer missing rules, missing facts, and unknown causes that involve predicate invention in the form of existentially quantified hypotheses. We also show an application of rule abduction to discover certain physical techniques and related integrity constraints within the subject area of Skill Science.Inductive Logic Programming, 19th International Conference, ILP 2009, Leuven, Belgium, July 02-04, 2009. Revised Papers; 01/2009
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ABSTRACT: This paper presents a method for enabling the relational learning or inductive logic programming (ILP) frame-work to deal with quantitative information from experimental data in systems biology. The study of systems biology through ILP aims at improving the understanding of the physiological state of the cell and the interpre-tation of the interactions between metabolites and signaling networks. A logical model of the glycolysis and pentose phosphate pathways of E. Coli is proposed to support our method description. We explain our original approach to building a symbolic model applied to kinetics based on Michaelis-Menten equation, starting with the discretization of the changes in concentration of some of the metabolites over time into relevant levels. We can then use them in our ILP-based model. Logical formulae on concentrations of some metabolites, which could not be measured during the dynamic state, are produced through logical abduction. Finally, as this re-sults in a large number of hypotheses, they are ranked with an expectation maximization algorithm working on binary decision diagrams.BIOINFORMATICS 2011 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, Rome, Italy, 26-29 January, 2011; 01/2011
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ABSTRACT: This paper addresses discovery of unknown relations from incomplete network data using abduction. Given a network information such as causal relations and metabolic pathways, we want to infer miss-ing links and nodes in the network to account for observations. This is implemented in SOLAR, an automated deduction system for conse-quence finding, using first-order representation of algebraic relations and full-clausal ground formulas of network information. Abduction by SO-LAR is powerful enough to infer unknown rules and to realize predicate invention by inferring unknown causes. In particular, we point out the importance of existentially quantified formulas to express hypotheses in-cluding new variables representing missing nodes to be introduced.01/2009;