Above are papers related to the following paper: [Paper_ID] Paper #135765. 	[Title] Specialization of Recursive Predicates [Abstract] When specializing a recursive predicate in order to exclude a set of negative examples without excluding a set of positive examples, it may not be possible to specialize or remove any of the clauses in a refutation of a negative example without excluding any positive exam ples. A previously proposed solution to this problem is to apply program transformation in order to obtain non-recursive target predicates from recursive ones. However, the application of this method prevents recursive specializations from being found. In this work, we present the algorithm spectre ii which is not limited to specializing non-recursive predicates. The key idea upon which the algorithm is based is that it is not enough to specialize or remove clauses in refutations of negative examples in order to obtain correct specializations, but it is sometimes necessary to specialize clauses that appear only in refutations of positive examples. In contrast to its predecessor spectre, the new algorithm is not limited to specializing clauses defining one predicate only, but may specialize clauses defining multiple predicates. Furthermore, the positive and negative examples are no longer required to be instances of the same predicate. It is proven that the algorithm produces a correct specialization when all positive examples are logical consequences of the original program, there is a finite number of derivations of positive and negative examples and when no positive and negative examples have the same sequence of input clauses in their refutations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6917. 	[Title] Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs [Abstract] covering has been formalized and used extensively. In this work, the divide-and-conquer technique is formalized as well and compared to the covering technique in a logic programming framework. Covering works by repeatedly specializing an overly general hypothesis, on each iteration focusing on finding a clause with a high coverage of positive examples. Divide-and-conquer works by specializing an overly general hypothesis once, focusing on discriminating positive from negative examples. Experimental results are presented demonstrating that there are cases when more accurate hypotheses can be found by divide-and-conquer than by covering. Moreover, since covering considers the same alternatives repeatedly it tends to be less efficient than divide-and-conquer, which never considers the same alternative twice. On the other hand, covering searches a larger hypothesis space, which may result in that more compact hypotheses are found by this technique than by divide-and-conquer. Furthermore, divide-and-conquer is, in contrast to covering, not applicable to learn ing recursive definitions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2440. 	[Title] Quinlan, 1990 J.R. Quinlan. Learning logical definitions from relations. Machine Learning, First-order theory revision. In [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4649. 	[Title] Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning [Abstract] An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational learning system. We compare the accuracy of concepts learned with this analytic strategy to concepts learned with an analytic strategy that operationalizes the domain theory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1127851. 	[Title] Generating Declarative Language Bias for Top-Down ILP Algorithms [Abstract] Many of today's algorithms for Inductive Logic Programming (ILP) put a heavy burden and responsibility on the user, because their declarative bias have to be defined in a rather low-level fashion. To address this issue, we developed a method for generating declarative language bias for top-down ILP systems from high-level declarations. The key feature of our approach is the distinction between a user level and an expert level of language bias declarations. The expert provides abstract meta-declarations, and the user declares the relationship between the meta-level and the given database to obtain a low-level declarative language bias. The suggested languages allow for compact and abstract specifications of the declarative language bias for top-down ILP systems using schemata. We verified several properties of the translation algorithm that generates schemata, and applied it successfully to a few chemical domains. As a consequence, we propose to use a two-level approach to generate declarative language bias. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8703. 	[Title] Theory Refinement Combining Analytical and Empirical Methods [Abstract] This article describes a comprehensive approach to automatic theory revision. Given an imperfect theory, the approach combines explanation attempts for incorrectly classified examples in order to identify the failing portions of the theory. For each theory fault, correlated subsets of the examples are used to inductively generate a correction. Because the corrections are focused, they tend to preserve the structure of the original theory. Because the system starts with an approximate domain theory, in general fewer training examples are required to attain a given level of performance (classification accuracy) compared to a purely empirical system. The approach applies to classification systems employing a propositional Horn-clause theory. The system has been tested in a variety of application domains, and results are presented for problems in the domains of molecular biology and plant disease diagnosis. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #449841. 	[Title] Theory-Guided Induction of Logic Programs by Inference of Regular Languages recursive clauses. merlin on the [Abstract] resent allowed sequences of resolution steps for the initial theory. There are, however, many characterizations of allowed sequences of resolution steps that cannot be expressed by a set of resolvents. One approach to this problem is presented, the system mer-lin, which is based on an earlier technique for learning finite-state automata that represent allowed sequences of resolution steps. merlin extends the previous technique in three ways: i) negative examples are considered in addition to positive examples, ii) a new strategy for performing generalization is used, and iii) a technique for converting the learned automaton to a logic program is included. Results from experiments are presented in which merlin outperforms both a system using the old strategy for performing generalization, and a traditional covering technique. The latter result can be explained by the limited expressiveness of hypotheses produced by covering and also by the fact that covering needs to produce the correct base clauses for a recursive definition before Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #434. 	[Title] Learning Analytically and Inductively [Abstract] Learning is a fundamental component of intelligence, and a key consideration in designing cognitive architectures such as Soar [ Laird et al., 1986 ] . This chapter considers the question of what constitutes an appropriate general-purpose learning mechanism. We are interested in mechanisms that might explain and reproduce the rich variety of learning capabilities of humans, ranging from learning perceptual-motor skills such as how to ride a bicycle, to learning highly cognitive tasks such as how to play chess. Research on learning in fields such as cognitive science, artificial intelligence, neurobiology, and statistics has led to the identification of two distinct classes of learning methods: inductive and analytic. Inductive methods, such as neural network Backpropagation, learn general laws by finding statistical correlations and regularities among a large set of training examples. In contrast, analytical methods, such as Explanation-Based Learning, acquire general laws from many fewer training examples. They rely instead on prior knowledge to analyze individual training examples in detail, then use this analysis to distinguish relevant example features from the irrelevant. The question considered in this chapter is how to best combine inductive and analytical learning in an architecture that seeks to cover the range of learning exhibited by intelligent systems such as humans. We present a specific learning mechanism, Explanation Based Neural Network learning (EBNN), that blends these two types of learning, and present experimental results demonstrating its ability to learn control strategies for a mobile robot using Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153811. 	[Title] Structural Regression Trees [Abstract] In many real-world domains the task of machine learning algorithms is to learn a theory predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relational and mostly non-determinate background knowledge. However, so far no ILP algorithm except one can predict numbers and cope with non-determinate background knowledge. (The only exception is a covering algorithm called FORS.) In this paper we present Structural Regression Trees (SRT), a new algorithm which can be applied to the above class of problems by integrating the statistical method of regression trees into ILP. SRT constructs a tree containing a literal (an atomic formula or its negation) or a conjunction of literals in each node, and assigns a numerical value to each leaf. SRT provides more comprehensible results than purely statistical methods, and can be applied to a class of problems most other ILP systems cannot handle. Experiments in several real-world domains demonstrate that the approach is competitive with existing methods, indicating that the advantages are not at the expense of predictive accuracy. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #411092. 	[Title] LEARNING LOGICAL EXCEPTIONS IN CHESS [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #135766. 	[Title] Specialization of Logic Programs by Pruning SLD-Trees [Abstract] program w.r.t. positive and negative examples can be viewed as the problem of pruning an SLD-tree such that all refutations of negative examples and no refutations of positive examples are excluded. It is shown that the actual pruning can be performed by applying unfolding and clause removal. The algorithm spectre is presented, which is based on this idea. The input to the algorithm is, besides a logic program and positive and negative examples, a computation rule, which determines the shape of the SLD-tree that is to be pruned. It is shown that the generality of the resulting specialization is dependent on the computation rule, and experimental results are presented from using three different computation rules. The experiments indicate that the computation rule should be formulated so that the number of applications of unfolding is kept as low as possible. The algorithm, which uses a divide-and-conquer method, is also compared with a covering algorithm. The experiments show that a higher predictive accuracy can be achieved if the focus is on discriminating positive from negative examples rather than on achieving a high coverage of positive examples only. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1272. 	[Title] Learning a set of primitive actions with an Induction of decision trees. Machine Learning, 1(1):81-106, [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105887. 	[Title] Knowledge Acquisition with a Knowledge-Intensive Machine Learning System [Abstract] In this paper, we investigate the integration of knowledge acquisition and machine learning techniques. We argue that existing machine learning techniques can be made more useful as knowledge acquisition tools by allowing the expert to have greater control over and interaction with the learning process. We describe a number of extensions to FOCL (a multistrategy Horn-clause learning program) that have greatly enhanced its power as a knowledge acquisition tool, paying particular attention to the utility of maintaining a connection between a rule and the set of examples explained by the rule. The objective of this research is to make the modification of a domain theory analogous to the use of a spread sheet. A prototype knowledge acquisition tool, FOCL-1-2-3, has been constructed in order to evaluate the strengths and weaknesses of this approach. Please summarise all the given information.

Paper #135765 has the title `Specialization of Recursive Predicates  
` and the abstract `When specializing a recursive predicate in order to exclude a set of negative examples without excluding a set of positive examples, it may not be possible to specialize or remove any of the clauses in a refutation of a negative example without excluding any positive exam ples. A previously proposed solution to this problem is to apply program transformation in order to obtain non-recursive target predicates from recursive ones. However, the application of this method prevents recursive specializations from being found. In this work, we present the algorithm spectre ii which is not limited to specializing non-recursive predicates. The key idea upon which the algorithm is based is that it is not enough to specialize or remove clauses in refutations of negative examples in order to obtain correct specializations, but it is sometimes necessary to specialize clauses that appear only in refutations of positive examples. In contrast to its predecessor spectre, the new algorithm is not limited to specializing clauses defining one predicate only, but may specialize clauses defining multiple predicates. Furthermore, the positive and negative examples are no longer required to be instances of the same predicate. It is proven that the algorithm produces a correct specialization when all positive examples are logical consequences of the original program, there is a finite number of derivations of positive and negative examples and when no positive and negative examples have the same sequence of input clauses in their refutations.
`.
Question: Which category should Paper #135765 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #135765 has the title `Specialization of Recursive Predicates  
` and the abstract `When specializing a recursive predicate in order to exclude a set of negative examples without excluding a set of positive examples, it may not be possible to specialize or remove any of the clauses in a refutation of a negative example without excluding any positive exam ples. A previously proposed solution to this problem is to apply program transformation in order to obtain non-recursive target predicates from recursive ones. However, the application of this method prevents recursive specializations from being found. In this work, we present the algorithm spectre ii which is not limited to specializing non-recursive predicates. The key idea upon which the algorithm is based is that it is not enough to specialize or remove clauses in refutations of negative examples in order to obtain correct specializations, but it is sometimes necessary to specialize clauses that appear only in refutations of positive examples. In contrast to its predecessor spectre, the new algorithm is not limited to specializing clauses defining one predicate only, but may specialize clauses defining multiple predicates. Furthermore, the positive and negative examples are no longer required to be instances of the same predicate. It is proven that the algorithm produces a correct specialization when all positive examples are logical consequences of the original program, there is a finite number of derivations of positive and negative examples and when no positive and negative examples have the same sequence of input clauses in their refutations.
`.
Question: Which category should Paper #135765 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Rule_Learning