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 #63915. 	[Title] Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models [Abstract] Learning the past tense of English verbs | a seemingly minor aspect of language acquisition | has generated heated debates since 1986, and has become a landmark task for testing the adequacy of cognitive modeling. Several artificial neural networks (ANNs) have been implemented, and a challenge for better symbolic models has been posed. In this paper, we present a general-purpose Symbolic Pattern Associator (SPA) based upon the decision-tree learning algorithm ID3. We conduct extensive head-to-head comparisons on the generalization ability between ANN models and the SPA under different representations. We conclude that the SPA generalizes the past tense of unseen verbs better than ANN models by a wide margin, and we offer insights as to why this should be the case. We also discuss a new default strategy for decision-tree learning algorithms. Please summarise all the given information.

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 #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 #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 #1116922 has the title `An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions  
` and the 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. 
`.
Question: Which category should Paper #1116922 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1116922 has the title `An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions  
` and the 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. 
`.
Question: Which category should Paper #1116922 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