Above are papers related to the following paper: [Paper_ID] Paper #1237. 	[Title] Toward Learning Systems That Integrate Different Strategies and Representations TR93-22 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102400. 	[Title] An Incremental Interactive Algorithm for Regular Grammar Inference [Abstract] We present provably correct interactive algorithms for learning regular grammars from positive examples and membership queries. A structurally complete set of strings from a language L(G) corresponding to a target regular grammar G implicitly specifies a lattice of finite state automata (FSA) which contains a FSA M G corresponding to G. The lattice is compactly represented as a version-space and M G is identified by searching the version-space using membership queries. We explore the problem of regular grammar inference in a setting where positive examples are provided intermittently. We provide an incremental version of the algorithm along with a set of sufficient conditions for its convergence. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28385. 	[Title] A Distributed Reinforcement Learning Scheme for Network Routing [Abstract] In this paper we describe a self-adjusting algorithm for packet routing in which a reinforcement learning method is embedded into each node of a network. Only local information is used at each node to keep accurate statistics on which routing policies lead to minimal routing times. In simple experiments involving a 36-node irregularly-connected network, this learning approach proves superior to routing based on precomputed shortest paths. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #152226. 	[Title] A Polynomial Time Incremental Algorithm for Regular Grammar Inference [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106112. 	[Title] Parameterized Heuristics for Intelligent Adaptive Network Routing in Large Communication Networks [Abstract] Parameterized heuristics offers an elegant and powerful theoretical framework for design and analysis of autonomous adaptive communication networks. Routing of messages in such networks presents a real-time instance of a multi-criterion optimization problem in a dynamic and uncertain environment. This paper describes a framework for heuristic routing in large networks. The effectiveness of the heuristic routing mechanism upon which Quo Vadis is based is described as part of a simulation study within a network with grid topology. A formal analysis of the underlying principles is presented through the incremental design of a set of heuristic decision functions that can be used to guide messages along a near-optimal (e.g., minimum delay) path in a large network. This paper carefully derives the properties of such heuristics under a set of simplifying assumptions about the network topology and load dynamics and identify the conditions under which they are guaranteed to route messages along an optimal path. The paper concludes with a discussion of the relevance of the theoretical results presented in the paper to the design of intelligent autonomous adaptive communication networks and an outline of some directions of future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #114. 	[Title] Learning to Act using Real-Time Dynamic Programming [Abstract] fl The authors thank Rich Yee, Vijay Gullapalli, Brian Pinette, and Jonathan Bachrach for helping to clarify the relationships between heuristic search and control. We thank Rich Sutton, Chris Watkins, Paul Werbos, and Ron Williams for sharing their fundamental insights into this subject through numerous discussions, and we further thank Rich Sutton for first making us aware of Korf's research and for his very thoughtful comments on the manuscript. We are very grateful to Dimitri Bertsekas and Steven Sullivan for independently pointing out an error in an earlier version of this article. Finally, we thank Harry Klopf, whose insight and persistence encouraged our interest in this class of learning problems. This research was supported by grants to A.G. Barto from the National Science Foundation (ECS-8912623 and ECS-9214866) and the Air Force Office of Scientific Research, Bolling AFB (AFOSR-89-0526). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #102938. 	[Title] A Neural Network Architecture for High-Speed Database Query Processing [Abstract] Artificial neural networks (ANN), due to their inherent parallelism and potential fault tolerance, offer an attractive paradigm for robust and efficient implementations of large modern database and knowledge base systems. This paper explores a neural network model for efficient implementation of a database query system. The application of the proposed model to a high-speed library query system for retrieval of multiple items is based on partial match of the specified query criteria with the stored records. The performance of the ANN realization of the database query module is analyzed and compared with other techniques commonly in current computer systems. The results of this analysis suggest that the proposed ANN design offers an attractive approach for the realization of query modules in large database and knowledge base systems, especially for retrieval based on partial matches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #102939. 	[Title] A Neural Network Architecture for Syntax Analysis [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #143676. 	[Title] A Neural Architecture for Content as well as Address-Based Storage and Recall: Theory and Applications [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1246. 	[Title] DESIGN AND ANALYSIS OF EFFICIENT REINFORCEMENT LEARNING ALGORITHMS [Abstract]  Please summarise all the given information.

Paper #1237 has the title `Toward Learning Systems That Integrate Different Strategies and Representations TR93-22  
` and the abstract `nan`.
Question: Which category should Paper #1237 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1237 has the title `Toward Learning Systems That Integrate Different Strategies and Representations TR93-22  
` and the abstract `nan`.
Question: Which category should Paper #1237 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: Neural_Networks