Above are papers related to the following paper: [Paper_ID] Paper #1152663. 	[Title] Quick 'n' Dirty Generalization For Mobile Robot Learning Content Areas: robotics, reinforcement learning, machine learning, [Abstract] The mobile robot domain challenges policy-iteration reinforcement learning algorithms with difficult problems of structural credit assignment and uncertainty. Structural credit assignment is particularly acute in domains where "real-time" trial length is a limiting factor on the number of learning steps that physical hardware can perform. Noisy sensors and effectors in complex dynamic environments further complicate the learning problem, leading to situations where speed of learning and policy flexibility may be more important than policy optimality. Input generalization addresses these problems but is typically too time consuming for robot domains. We present two algorithms, YB-learning and YB , that perform simple and fast generalization of the input space based on bit-similarity. The algorithms trade off long-term optimality for immediate performance and flexibility. The algorithms were tested in simulation against non-generalized learning across different numbers of discounting steps, and YB was shown to perform better during the earlier stages of learning, particularly in the presence of noise. In trials performed on a sonar-based mobile robot subject to uncertainty of the "real world," YB surpassed the simulation results by a wide margin, strongly supporting the role of such "quick and dirty" generalization strategies in noisy real-time mobile robot domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #134128. 	[Title] Learning To Play the Game of Chess [Abstract] This paper presents NeuroChess, a program which learns to play chess from the final outcome of games. NeuroChess learns chess board evaluation functions, represented by artificial neural networks. It integrates inductive neural network learning, temporal differencing, and a variant of explanation-based learning. Performance results illustrate some of the strengths and weaknesses of this approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #218410. 	[Title] Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches [Abstract] Explanation based learning has typically been considered a symbolic learning method. An explanation based learning method that utilizes purely neural network representations (called EBNN) has recently been developed, and has been shown to have several desirable properties, including robustness to errors in the domain theory. This paper briefly summarizes the EBNN algorithm, then explores the correspondence between this neural network based EBL method and EBL methods based on symbolic representations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6213. 	[Title] Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction, [Abstract] This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage. Please summarise all the given information.

Paper #1152663 has the title `Quick 'n' Dirty Generalization For Mobile Robot Learning Content Areas: robotics, reinforcement learning, machine learning,
` and the abstract `The mobile robot domain challenges policy-iteration reinforcement learning algorithms with difficult problems of structural credit assignment and uncertainty. Structural credit assignment is particularly acute in domains where "real-time" trial length is a limiting factor on the number of learning steps that physical hardware can perform. Noisy sensors and effectors in complex dynamic environments further complicate the learning problem, leading to situations where speed of learning and policy flexibility may be more important than policy optimality. Input generalization addresses these problems but is typically too time consuming for robot domains. We present two algorithms, YB-learning and YB , that perform simple and fast generalization of the input space based on bit-similarity. The algorithms trade off long-term optimality for immediate performance and flexibility. The algorithms were tested in simulation against non-generalized learning across different numbers of discounting steps, and YB was shown to perform better during the earlier stages of learning, particularly in the presence of noise. In trials performed on a sonar-based mobile robot subject to uncertainty of the "real world," YB surpassed the simulation results by a wide margin, strongly supporting the role of such "quick and dirty" generalization strategies in noisy real-time mobile robot domains.
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Question: Which category should Paper #1152663 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1152663 has the title `Quick 'n' Dirty Generalization For Mobile Robot Learning Content Areas: robotics, reinforcement learning, machine learning,
` and the abstract `The mobile robot domain challenges policy-iteration reinforcement learning algorithms with difficult problems of structural credit assignment and uncertainty. Structural credit assignment is particularly acute in domains where "real-time" trial length is a limiting factor on the number of learning steps that physical hardware can perform. Noisy sensors and effectors in complex dynamic environments further complicate the learning problem, leading to situations where speed of learning and policy flexibility may be more important than policy optimality. Input generalization addresses these problems but is typically too time consuming for robot domains. We present two algorithms, YB-learning and YB , that perform simple and fast generalization of the input space based on bit-similarity. The algorithms trade off long-term optimality for immediate performance and flexibility. The algorithms were tested in simulation against non-generalized learning across different numbers of discounting steps, and YB was shown to perform better during the earlier stages of learning, particularly in the presence of noise. In trials performed on a sonar-based mobile robot subject to uncertainty of the "real world," YB surpassed the simulation results by a wide margin, strongly supporting the role of such "quick and dirty" generalization strategies in noisy real-time mobile robot domains.
`.
Question: Which category should Paper #1152663 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: Reinforcement_Learning