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 #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 #1102625. 	[Title] A systematic description of greedy optimisation algorithms for cost sensitive generalisation [Abstract] This paper defines a class of problems involving combinations of induction and (cost) optimisation. A framework is presented that systematically describes problems that involve construction of decision trees or rules, optimising accuracy as well as measurement- and misclassification costs. It does not present any new algorithms but shows how this framework can be used to configure greedy algorithms for constructing such trees or rules. The framework covers a number of existing algorithms. Moreover, the framework can also be used to define algorithm configurations with new functionalities, as expressed in their evaluation functions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #152227. 	[Title] Learning DFA from Simple Examples [Abstract] We present a framework for learning DFA from simple examples. We show that efficient PAC learning of DFA is possible if the class of distributions is restricted to simple distributions where a teacher might choose examples based on the knowledge of the target concept. This answers an open research question posed in Pitt's seminal paper: Are DFA's PAC-identifiable if examples are drawn from the uniform distribution, or some other known simple distribution? Our approach uses the RPNI algorithm for learning DFA from labeled examples. In particular, we describe an efficient learning algorithm for exact learning of the target DFA with high probability when a bound on the number of states (N ) of the target DFA is known in advance. When N is not known, we show how this algorithm can be used for efficient PAC learning of DFAs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12350. 	[Title] Cryptographic Limitations on Learning Boolean Formulae and Finite Automata [Abstract] In this paper we prove the intractability of learning several classes of Boolean functions in the distribution-free model (also called the Probably Approximately Correct or PAC model) of learning from examples. These results are representation independent, in that they hold regardless of the syntactic form in which the learner chooses to represent its hypotheses. Our methods reduce the problems of cracking a number of well-known public-key cryptosys- tems to the learning problems. We prove that a polynomial-time learning algorithm for Boolean formulae, deterministic finite automata or constant-depth threshold circuits would have dramatic consequences for cryptography and number theory: in particular, such an algorithm could be used to break the RSA cryptosystem, factor Blum integers (composite numbers equivalent to 3 modulo 4), and detect quadratic residues. The results hold even if the learning algorithm is only required to obtain a slight advantage in prediction over random guessing. The techniques used demonstrate an interesting duality between learning and cryptography. We also apply our results to obtain strong intractability results for approximating a gener - alization of graph coloring. fl This research was conducted while the author was at Harvard University and supported by an A.T.& T. Bell Laboratories scholarship. y Supported by grants ONR-N00014-85-K-0445, NSF-DCR-8606366 and NSF-CCR-89-02500, DAAL03-86-K-0171, DARPA AFOSR 89-0506, and by SERC. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5959. 	[Title] Chunking in soar: The anatomy of a general learn ing mechanism. Machine Learning, 1(1). Learning [Abstract] gers University. Also appears as tech. report ML- TR-7. Minton, S. (1988). Quantitative results concerning the utility of explanation-based learning. In Proceedings of National Conference on Artificial Intelli gence, pages 564-569. St. Paul, MN. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #37483. 	[Title] Learning Approximate Control Rules Of High Utility [Abstract] One of the difficult problems in the area of explanation based learning is the utility problem; learning too many rules of low utility can lead to swamping, or degradation of performance. This paper introduces two new techniques for improving the utility of learned rules. The first technique is to combine EBL with inductive learning techniques to learn a better set of control rules; the second technique is to use these inductive techniques to learn approximate control rules. The two techniques are synthesized in an algorithm called approximating abductive explanation based learning (AxA-EBL). AxA-EBL is shown to improve substantially over standard EBL in several domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #582511. 	[Title] The Problem with Noise and Small Disjuncts [Abstract] Systems that learn from examples often create a disjunctive concept definition. The disjuncts in the concept definition which cover only a few training examples are referred to as small disjuncts. The problem with small disjuncts is that they are more error prone than large disjuncts, but may be necessary to achieve a high level of predictive accuracy [Holte, Acker, and Porter, 1989]. This paper extends previous work done on the problem of small disjuncts by taking noise into account. It investigates the assertion that it is hard to learn from noisy data because it is difficult to distinguish between noise and true exceptions. In the process of evaluating this assertion, insights are gained into the mechanisms by which noise affects learning. Two domains are investigated. The experimental results in this paper suggest that for both Shapiro's chess endgame domain [Shapiro, 1987] and for the Wisconsin breast cancer domain [Wolberg, 1990], the assertion is true, at least for low levels (5-10%) of class noise. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #545647. 	[Title] Learning with Rare Cases and Small Disjuncts [Abstract] Systems that learn from examples often create a disjunctive concept definition. Small disjuncts are those disjuncts which cover only a few training examples. The problem with small disjuncts is that they are more error prone than large disjuncts. This paper investigates the reasons why small disjuncts are more error prone than large disjuncts. It shows that when there are rare cases within a domain, then factors such as attribute noise, missing attributes, class noise and training set size can result in small disjuncts being more error prone than large disjuncts and in rare cases being more error prone than common cases. This paper also assesses the impact that these error prone small disjuncts and rare cases have on inductive learning (i.e., on error rate). One key conclusion is that when low levels of attribute noise are applied only to the training set (the ability to learn the correct concept is being evaluated), rare cases within a domain are primarily responsible for making learning difficult. Please summarise all the given information.

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 #1139928. 	[Title] Planning and Learning in an Adversarial Robotic Game [Abstract] 1 This paper demonstrates the tandem use of a finite automata learning algorithm and a utility planner for an adversarial robotic domain. For many applications, robot agents need to predict the movement of objects in the environment and plan to avoid them. When the robot has no reasoning model of the object, machine learning techniques can be used to generate one. In our project, we learn a DFA model of an adversarial robot and use the automaton to predict the next move of the adversary. The robot agent plans a path to avoid the adversary at the predicted location while fulfilling the goal requirements. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #987197. 	[Title] Query, PACS and simple-PAC Learning [Abstract] We study a distribution dependent form of PAC learning that uses probability distributions related to Kolmogorov complexity. We relate the PACS model, defined by Denis, D'Halluin and Gilleron in [3], with the standard simple-PAC model and give a general technique that subsumes the results in [3] and [6]. 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 #152226 has the title `A Polynomial Time Incremental Algorithm for Regular Grammar Inference  
` and the abstract `nan`.
Question: Which category should Paper #152226 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #152226 has the title `A Polynomial Time Incremental Algorithm for Regular Grammar Inference  
` and the abstract `nan`.
Question: Which category should Paper #152226 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: Theory