Above are papers related to the following paper: [Paper_ID] Paper #49895. 	[Title] The Origins of Inductive Logic Programming: A Prehistoric Tale [Abstract] This paper traces the development of the main ideas that have led to the present state of knowledge in Inductive Logic Programming. The story begins with research in psychology on the subject of human concept learning. Results from this research influenced early efforts in Artificial Intelligence which combined with the formal methods of inductive inference to evolve into the present discipline of Inductive Logic Programming. Inductive Logic Programming is often considered to be a young discipline. However, it has its roots in research dating back nearly 40 years. This paper traces the development of ideas beginning in psychology and the effect they had on concept learning research in Artificial Intelligence. Independent of any requirement for a psychological basis, formal methods of inductive inference were developed. These separate streams eventually gave rise to Inductive Logic Programming. This account is not entirely unbiased. More attention is given to the work of those researchers who most influenced my own interest in machine learning. Being a retrospective paper, I do not attempt to describe recent developments in ILP. This account only includes research prior to 1991 the year in which the term Inductive Logic Programming was first used (Muggleton, 1991). This is the reason for the subtitle A Prehistoric Tale. The major headings in the paper are taken from the names of periods in the evolution of life on Earth. Please summarise all the given information.

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Above are papers related to the following paper: [Paper_ID] Paper #1153160. 	[Title] Inductive Logic Programming [Abstract] A new research area, Inductive Logic Programming, is presently emerging. While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of the limitations of its forebears. The background to present developments within this area is discussed and various goals and aspirations for the increasing body of researchers are identified. Inductive Logic Programming needs to be based on sound principles from both Logic and Statistics. On the side of statistical justification of hypotheses we discuss the possible relationship between Algorithmic Complexity theory and Probably-Approximately-Correct (PAC) Learning. In terms of logic we provide a unifying framework for Muggleton and Buntine's Inverse Resolution (IR) and Plotkin's Relative Least General Generali-sation (RLGG) by rederiving RLGG in terms of IR. This leads to a discussion of the feasibility of extending the RLGG framework to allow for the invention of new predicates, previously discussed only within the context of IR. 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.

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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 #417017. 	[Title] Relating Relational Learning Algorithms [Abstract] Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research. Please summarise all the given information.

Paper #49895 has the title `The Origins of Inductive Logic Programming: A Prehistoric Tale  
` and the abstract `This paper traces the development of the main ideas that have led to the present state of knowledge in Inductive Logic Programming. The story begins with research in psychology on the subject of human concept learning. Results from this research influenced early efforts in Artificial Intelligence which combined with the formal methods of inductive inference to evolve into the present discipline of Inductive Logic Programming. Inductive Logic Programming is often considered to be a young discipline. However, it has its roots in research dating back nearly 40 years. This paper traces the development of ideas beginning in psychology and the effect they had on concept learning research in Artificial Intelligence. Independent of any requirement for a psychological basis, formal methods of inductive inference were developed. These separate streams eventually gave rise to Inductive Logic Programming. This account is not entirely unbiased. More attention is given to the work of those researchers who most influenced my own interest in machine learning. Being a retrospective paper, I do not attempt to describe recent developments in ILP. This account only includes research prior to 1991 the year in which the term Inductive Logic Programming was first used (Muggleton, 1991). This is the reason for the subtitle A Prehistoric Tale. The major headings in the paper are taken from the names of periods in the evolution of life on Earth. 
`.
Question: Which category should Paper #49895 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #49895 has the title `The Origins of Inductive Logic Programming: A Prehistoric Tale  
` and the abstract `This paper traces the development of the main ideas that have led to the present state of knowledge in Inductive Logic Programming. The story begins with research in psychology on the subject of human concept learning. Results from this research influenced early efforts in Artificial Intelligence which combined with the formal methods of inductive inference to evolve into the present discipline of Inductive Logic Programming. Inductive Logic Programming is often considered to be a young discipline. However, it has its roots in research dating back nearly 40 years. This paper traces the development of ideas beginning in psychology and the effect they had on concept learning research in Artificial Intelligence. Independent of any requirement for a psychological basis, formal methods of inductive inference were developed. These separate streams eventually gave rise to Inductive Logic Programming. This account is not entirely unbiased. More attention is given to the work of those researchers who most influenced my own interest in machine learning. Being a retrospective paper, I do not attempt to describe recent developments in ILP. This account only includes research prior to 1991 the year in which the term Inductive Logic Programming was first used (Muggleton, 1991). This is the reason for the subtitle A Prehistoric Tale. The major headings in the paper are taken from the names of periods in the evolution of life on Earth. 
`.
Question: Which category should Paper #49895 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