Above are papers related to the following paper: [Paper_ID] Paper #136767. 	[Title] Learning Horn Definitions with Equivalence and Membership Queries [Abstract] A Horn definition is a set of Horn clauses with the same head literal. In this paper, we consider learning non-recursive, function-free first-order Horn definitions. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Our results have been shown to be applicable to learning efficient goal-decomposition rules in planning domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #136768. 	[Title] Theory-guided Empirical Speedup Learning of Goal Decomposition Rules [Abstract] Speedup learning is the study of improving the problem-solving performance with experience and from outside guidance. We describe here a system that successfully combines the best features of Explanation-based learning and empirical learning to learn goal decomposition rules from examples of successful problem solving and membership queries. We demonstrate that our system can efficiently learn effective decomposition rules in three different domains. Our results suggest that theory-guided empirical learning can overcome the problems of purely explanation-based learning and purely empirical learning, and be an effective speedup learning method. 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 #9708. 	[Title] Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to [Abstract] University of Wisconsin Computer Sciences Technical Report 876 (September 1989) Abstract In explanation-based learning, a specific problem's solution is generalized into a form that can be later used to solve conceptually similar problems. Most research in explanation-based learning involves relaxing constraints on the variables in the explanation of a specific example, rather than generalizing the graphical structure of the explanation itself. However, this precludes the acquisition of concepts where an iterative or recursive process is implicitly represented in the explanation by a fixed number of applications. This paper presents an algorithm that generalizes explanation structures and reports empirical results that demonstrate the value of acquiring recursive and iterative concepts. The BAGGER2 algorithm learns recursive and iterative concepts, integrates results from multiple examples, and extracts useful subconcepts during generalization. On problems where learning a recursive rule is not appropriate, the system produces the same result as standard explanation-based methods. Applying the learned recursive rules only requires a minor extension to a PROLOG-like problem solver, namely, the ability to explicitly call a specific rule. Empirical studies demonstrate that generalizing the structure of explanations helps avoid the recently reported negative effects of learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #348437. 	[Title] Learning First-Order Acyclic Horn Programs from Entailment [Abstract] In this paper, we consider learning first-order Horn programs from entailment. In particular, we show that any subclass of first-order acyclic Horn programs with constant arity is exactly learnable from equivalence and entailment membership queries provided it allows a polynomial-time subsumption procedure and satisfies some closure conditions. One consequence of this is that first-order acyclic determinate Horn programs with constant arity are exactly learnable from equiv alence and entailment membership queries. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6741. 	[Title] LEARNING CONCEPTS BY ASKING QUESTIONS [Abstract] Tw o important issues in machine learning are explored: the role that memory plays in acquiring new concepts; and the extent to which the learner can take an active part in acquiring these concepts. This chapter describes a program, called Marvin, which uses concepts it has learned previously to learn new concepts. The program forms hypotheses about the concept being learned and tests the hypotheses by asking the trainer questions. Learning begins when the trainer shows Marvin an example of the concept to be learned. The program determines which objects in the example belong to concepts stored in the memory. A description of the new concept is formed by using the information obtained from the memory to generalize the description of the training example. The generalized description is tested when the program constructs new examples and shows these to the trainer, asking if they belong to the target concept. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63931. 	[Title] Combining FOIL and EBG to Speed-up Logic Programs [Abstract] This paper presents an algorithm that combines traditional EBL techniques and recent developments in inductive logic programming to learn effective clause selection rules for Prolog programs. When these control rules are incorporated into the original program, significant speed-up may be achieved. The algorithm is shown to be an improvement over competing EBL approaches in several domains. Additionally, the algorithm is capable of automatically transforming some intractable algorithms into ones that run in polynomial time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #136766. 	[Title] Learning Goal-Decomposition Rules using Exercises [Abstract] Exercises are problems ordered in increasing order of difficulty. Teaching problem-solving through exercises is a widely used pedagogic technique. A computational reason for this is that the knowledge gained by solving simple problems is useful in efficiently solving more difficult problems. We adopt this approach of learning from exercises to acquire search-control knowledge in the form of goal-decomposition rules (d-rules). D-rules are first order, and are learned using a new "generalize-and-test" algorithm which is based on inductive logic programming techniques. We demonstrate the feasibility of the approach by applying it in two planning do mains. Please summarise all the given information.

Paper #136767 has the title `Learning Horn Definitions with Equivalence and Membership Queries  
` and the abstract `A Horn definition is a set of Horn clauses with the same head literal. In this paper, we consider learning non-recursive, function-free first-order Horn definitions. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Our results have been shown to be applicable to learning efficient goal-decomposition rules in planning domains.
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Question: Which category should Paper #136767 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #136767 has the title `Learning Horn Definitions with Equivalence and Membership Queries  
` and the abstract `A Horn definition is a set of Horn clauses with the same head literal. In this paper, we consider learning non-recursive, function-free first-order Horn definitions. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Our results have been shown to be applicable to learning efficient goal-decomposition rules in planning domains.
`.
Question: Which category should Paper #136767 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: Theory