Logical Reduction of Metarules

International audienceMany forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called derivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times

MILCS: A mutual information learning classifier system

This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. MILCS's design draws on an analogy to the structural learning approach of cascade correlation networks. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets, and introduce a new technique for visualizing explanatory power. Final comments include future directions for this research, including investigations in neural networks and other systems. Copyright 2007 ACM

On Redundancy Elimination Tolerant Scheduling Rules

In (Ferrucci, Pacini and Sessa, 1995) an extended form of resolution, called Reduced SLD resolution (RSLD), is introduced. In essence, an RSLD derivation is an SLD derivation such that redundancy elimination from resolvents is performed after each rewriting step. It is intuitive that redundancy elimination may have positive effects on derivation process. However, undesiderable effects are also possible. In particular, as shown in this paper, program termination as well as completeness of loop checking mechanisms via a given selection rule may be lost. The study of such effects has led us to an analysis of selection rule basic concepts, so that we have found convenient to move the attention from rules of atom selection to rules of atom scheduling. A priority mechanism for atom scheduling is built, where a priority is assigned to each atom in a resolvent, and primary importance is given to the event of arrival of new atoms from the body of the applied clause at rewriting time. This new computational model proves able to address the study of redundancy elimination effects, giving at the same time interesting insights into general properties of selection rules. As a matter of fact, a class of scheduling rules, namely the specialisation independent ones, is defined in the paper by using not trivial semantic arguments. As a quite surprising result, specialisation independent scheduling rules turn out to coincide with a class of rules which have an immediate structural characterisation (named stack-queue rules). Then we prove that such scheduling rules are tolerant to redundancy elimination, in the sense that neither program termination nor completeness of equality loop check is lost passing from SLD to RSLD.Comment: 53 pages, to appear on TPL

KNOWLEDGE REPRESENTATION AND INFERENCE FOR ANALYSIS AND DESIGN OF DATABASES AND TABULAR RULE-BASED SYSTEMS

Rulc-based Systems constitute a powerful tool for speciftcation of knowledge in design and implementation of knowledge-based Systems. They provide also a universal programming paradigm for domains such as intelligent control, decision support, situation classification and opcrational knowledge encoding. In order to assure safe and reliable performance, such Systems should satisfy certain format reąuirements, including completeness and consistency. This paper addresses the issue of analysis and verification of selected properties of a class of such Systems in a systematic way. A uniform, tabular scheme of single-levcl rule-bascd Systems is considered. Such systcms can be applied as a generalized form of databases for speciftcation of data pattems (unconditional knowledge), or can be used for deftning attributive decision tables (conditional knowledge in form of rules). They can also serve as lower-level componcnts of a hierarchical, multi-lcvcl control and decision support knowledge-based systcms. An algebraic knowledge rcprescntation paradigm using extcnded tabular rcprcsentation, similar to relational databasc tables is prcsentcd and algebraic bascs for system analysis, vcrification and design support arc outlined

Constraint satisfaction problems in clausal form

This is the report-version of a mini-series of two articles on the foundations of satisfiability of conjunctive normal forms with non-boolean variables, to appear in Fundamenta Informaticae, 2011. These two parts are here bundled in one report, each part yielding a chapter. Generalised conjunctive normal forms are considered, allowing literals of the form "variable not-equal value". The first part sets the foundations for the theory of autarkies, with emphasise on matching autarkies. Main results concern various polynomial time results in dependency on the deficiency. The second part considers translations to boolean clause-sets and irredundancy as well as minimal unsatisfiability. Main results concern classification of minimally unsatisfiable clause-sets and the relations to the hermitian rank of graphs. Both parts contain also discussions of many open problems.Comment: 91 pages, to appear in Fundamenta Informaticae, 2011, as Constraint satisfaction problems in clausal form I: Autarkies and deficiency, Constraint satisfaction problems in clausal form II: Minimal unsatisfiability and conflict structur

Object-oriented data mining

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Logic Programs as Declarative and Procedural Bias in Inductive Logic Programming

Machine Learning is necessary for the development of Artificial Intelligence, as pointed out by Turing in his 1950 article ``Computing Machinery and Intelligence''. It is in the same article that Turing suggested the use of computational logic and background knowledge for learning. This thesis follows a logic-based machine learning approach called Inductive Logic Programming (ILP), which is advantageous over other machine learning approaches in terms of relational learning and utilising background knowledge. ILP uses logic programs as a uniform representation for hypothesis, background knowledge and examples, but its declarative bias is usually encoded using metalogical statements. This thesis advocates the use of logic programs to represent declarative and procedural bias, which results in a framework of single-language representation. We show in this thesis that using a logic program called the top theory as declarative bias leads to a sound and complete multi-clause learning system MC-TopLog. It overcomes the entailment-incompleteness of Progol, thus outperforms Progol in terms of predictive accuracies on learning grammars and strategies for playing Nim game. MC-TopLog has been applied to two real-world applications funded by Syngenta, which is an agriculture company. A higher-order extension on top theories results in meta-interpreters, which allow the introduction of new predicate symbols. Thus the resulting ILP system Metagol can do predicate invention, which is an intrinsically higher-order logic operation. Metagol also leverages the procedural semantic of Prolog to encode procedural bias, so that it can outperform both its ASP version and ILP systems without an equivalent procedural bias in terms of efficiency and accuracy. This is demonstrated by the experiments on learning Regular, Context-free and Natural grammars. Metagol is also applied to non-grammar learning tasks involving recursion and predicate invention, such as learning a definition of staircases and robot strategy learning. Both MC-TopLog and Metagol are based on a $\top$-directed framework, which is different from other multi-clause learning systems based on Inverse Entailment, such as CF-Induction, XHAIL and IMPARO. Compared to another $\top$-directed multi-clause learning system TAL, Metagol allows the explicit form of higher-order assumption to be encoded in the form of meta-rules.Open Acces