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 #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 #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 #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 #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.

Paper #987197 has the title `Query, PACS and simple-PAC Learning  
` and the 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]. 
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Question: Which category should Paper #987197 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #987197 has the title `Query, PACS and simple-PAC Learning  
` and the 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]. 
`.
Question: Which category should Paper #987197 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