SOLAR: A Consequence Finding System for Advanced Reasoning

DOI: 10.1007/978-3-540-45206-5_22 In book: Automated Reasoning with Analytic Tableaux and Related Methods, pp.257-263
Source: DBLP


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).

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    • "• SOLAR, a consequence finding system working on Skipping Ordered Linear tableaux (Nabeshima et al., 2003), which is complete for finding minimal explanations, to conduct abduction or induc- tion. "
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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.
    Full-text · Conference Paper · Jan 2011
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    ABSTRACT: Consequence finding has been recognized as an important technique in many intelligent systems involving inference. In previous work, propositional or first-order clausal theories have been considered for consequence finding. In this paper, we consider consequence finding within a default theory, which consists of a first-order clausal theory and a set of normal defaults. In each extension of a default theory, consequence finding can be performed with the ”generating defaults” for the extension. Alternatively, we consider all extensions in one theory with the ”conditional consequence” format, which explicitly represents how a conclusion depends on which defaults. We also propose a procedure to compute consequences from a default theory based on a first-order consequence-finding procedure SOL. The SOL calculus is then further refined using skip-preference and complement checking, which have a great ability of preventing irrational derivations. The proposed system can be well applied to a multi-agent system with speculative computation in an incomplete communication environment.
    No preview · Conference Paper · Jan 2004
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    ABSTRACT: This paper is concerned with a multi-agent system which performs speculative computation under incomplete communication environments. In a master–slave style multi-agent system with speculative computation, a master agent asks queries to slave agents in problem solving, and proceeds computation with default answers when answers from slave agents are delayed. In this paper, we first provide a semantics for speculative computation using default logic. Speculative computation is considered in which reply messages from slave agents to a master are tentative and may change from time to time. In this system, default values used in speculative computation are only partially determined in advance. Next, we propose a procedure to compute speculative computation using a first-order consequence-finding procedure SOL with the answer literal method. The use of a consequence-finding procedure is convenient for updating agents' beliefs according to situation changes in the world. Then, we further refine the SOL calculus using conditional answer computation and skip-preference in SOL. The conditional answer format has a great advantage of explicitly representing how a conclusion depends on tentative replies and defaults. This dependency representation is important to avoid unnecessary recomputation of tentative conclusions. On the other hand, the skip-preference method has the great ability of preventing irrational/redundant derivations. Finally, we implemented a mechanism of process maintenance to avoid duplicate computation when slave agents change their answers. As long as new answers from slave agents do not conflict with any previously encountered situation, the obtained conclusions are never recomputed. We applied the proposed system to the meeting-room reservation problem to see the usefulness of the framework.
    No preview · Article · Sep 2004 · Annals of Mathematics and Artificial Intelligence
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