Above are papers related to the following paper: [Paper_ID] Paper #318187. 	[Title] Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming [Abstract] This paper demonstrates the capabilities of Foidl, an inductive logic programming (ILP) system whose distinguishing characteristics are the ability to produce first-order decision lists, the use of an output completeness assumption as a substitute for negative examples, and the use of intensional background knowledge. The development of Foidl was originally motivated by the problem of learning to generate the past tense of English verbs; however, this paper demonstrates its superior performance on two different sets of benchmark ILP problems. Tests on the finite element mesh design problem show that Foidl's decision lists enable it to produce generally more accurate results than a range of methods previously applied to this problem. Tests with a selection of list-processing problems from Bratko's introductory Prolog text demonstrate that the combination of implicit negatives and intensionality allow Foidl to learn correct programs from far fewer examples than Foil. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116922. 	[Title] An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions [Abstract] This paper experimentally compares three approaches to program induction: inductive logic programming (ILP), genetic programming (GP), and genetic logic programming (GLP) (a variant of GP for inducing Pro-log programs). Each of these methods was used to induce four simple, recursive, list-manipulation functions. The results indicate that ILP is the most likely to induce a correct program from small sets of random examples, while GP is generally less accurate. GLP performs the worst, and is rarely able to induce a correct program. Interpretations of these results in terms of differences in search methods and inductive biases are presented. Keywords: Genetic Programming, Inductive Logic Programming, Empiri cal Comparison This paper will also be submitted to the 8th Int. Workshop on Inductive Logic Programming, 1998. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6939. 	[Title] Learning First-Order Definitions of Functions [Abstract] First-order learning involves finding a clause-form definition of a relation from examples of the relation and relevant background information. In this paper, a particular first-order learning system is modified to customize it for finding definitions of functional relations. This restriction leads to faster learning times and, in some cases, to definitions that have higher predictive accuracy. Other first-order learning systems might benefit from similar specialization. Please summarise all the given information.

Paper #318187 has the title `Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming  
` and the abstract `This paper demonstrates the capabilities of Foidl, an inductive logic programming (ILP) system whose distinguishing characteristics are the ability to produce first-order decision lists, the use of an output completeness assumption as a substitute for negative examples, and the use of intensional background knowledge. The development of Foidl was originally motivated by the problem of learning to generate the past tense of English verbs; however, this paper demonstrates its superior performance on two different sets of benchmark ILP problems. Tests on the finite element mesh design problem show that Foidl's decision lists enable it to produce generally more accurate results than a range of methods previously applied to this problem. Tests with a selection of list-processing problems from Bratko's introductory Prolog text demonstrate that the combination of implicit negatives and intensionality allow Foidl to learn correct programs from far fewer examples than Foil.
`.
Question: Which category should Paper #318187 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #318187 has the title `Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming  
` and the abstract `This paper demonstrates the capabilities of Foidl, an inductive logic programming (ILP) system whose distinguishing characteristics are the ability to produce first-order decision lists, the use of an output completeness assumption as a substitute for negative examples, and the use of intensional background knowledge. The development of Foidl was originally motivated by the problem of learning to generate the past tense of English verbs; however, this paper demonstrates its superior performance on two different sets of benchmark ILP problems. Tests on the finite element mesh design problem show that Foidl's decision lists enable it to produce generally more accurate results than a range of methods previously applied to this problem. Tests with a selection of list-processing problems from Bratko's introductory Prolog text demonstrate that the combination of implicit negatives and intensionality allow Foidl to learn correct programs from far fewer examples than Foil.
`.
Question: Which category should Paper #318187 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