Above are papers related to the following paper: [Paper_ID] Paper #13205. 	[Title] Two Methods for Hierarchy Learning in Reinforcement Environments [Abstract] This paper describes two methods for hierarchically organizing temporal behaviors. The first is more intuitive: grouping together common sequences of events into single units so that they may be treated as individual behaviors. This system immediately encounters problems, however, because the units are binary, meaning the behaviors must execute completely or not at all, and this hinders the construction of good training algorithms. The system also runs into difficulty when more than one unit is (or should be) active at the same time. The second system is a hierarchy of transition values. This hierarchy dynamically modifies the values that specify the degree to which one unit should follow another. These values are continuous, allowing the use of gradient descent during learning. Furthermore, many units are active at the same time as part of the system's normal functionings. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13212. 	[Title] DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997 [Abstract] Many neural net learning algorithms aim at finding "simple" nets to explain training data. The expectation is: the "simpler" the networks, the better the generalization on test data (! Occam's razor). Previous implementations, however, use measures for "simplicity" that lack the power, universality and elegance of those based on Kolmogorov complexity and Solomonoff's algorithmic probability. Likewise, most previous approaches (especially those of the "Bayesian" kind) suffer from the problem of choosing appropriate priors. This paper addresses both issues. It first reviews some basic concepts of algorithmic complexity theory relevant to machine learning, and how the Solomonoff-Levin distribution (or universal prior) deals with the prior problem. The universal prior leads to a probabilistic method for finding "algorithmically simple" problem solutions with high generalization capability. The method is based on Levin complexity (a time-bounded generalization of Kolmogorov complexity) and inspired by Levin's optimal universal search algorithm. For a given problem, solution candidates are computed by efficient "self-sizing" programs that influence their own runtime and storage size. The probabilistic search algorithm finds the "good" programs (the ones quickly computing algorithmically probable solutions fitting the training data). Simulations focus on the task of discovering "algorithmically simple" neural networks with low Kolmogorov complexity and high generalization capability. It is demonstrated that the method, at least with certain toy problems where it is computationally feasible, can lead to generalization results unmatchable by previous neural net algorithms. Much remains do be done, however, to make large scale applications and "incremental learning" feasible. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #131318. 	[Title] FLAT MINIMA Neural Computation 9(1):1-42 (1997) [Abstract] We present a new algorithm for finding low complexity neural networks with high generalization capability. The algorithm searches for a "flat" minimum of the error function. A flat minimum is a large connected region in weight-space where the error remains approximately constant. An MDL-based, Bayesian argument suggests that flat minima correspond to "simple" networks and low expected overfitting. The argument is based on a Gibbs algorithm variant and a novel way of splitting generalization error into underfitting and overfitting error. Unlike many previous approaches, ours does not require Gaussian assumptions and does not depend on a "good" weight prior instead we have a prior over input/output functions, thus taking into account net architecture and training set. Although our algorithm requires the computation of second order derivatives, it has backprop's order of complexity. Automatically, it effectively prunes units, weights, and input lines. Various experiments with feedforward and recurrent nets are described. In an application to stock market prediction, flat minimum search outperforms (1) conventional backprop, (2) weight decay, (3) "optimal brain surgeon" / "optimal brain damage". We also provide pseudo code of the algorithm (omitted from the NC-version). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13208. 	[Title] ON LEARNING HOW TO LEARN LEARNING STRATEGIES [Abstract] This paper introduces the "incremental self-improvement paradigm". Unlike previous methods, incremental self-improvement encourages a reinforcement learning system to improve the way it learns, and to improve the way it improves the way it learns ..., without significant theoretical limitations | the system is able to "shift its inductive bias" in a universal way. Its major features are: (1) There is no explicit difference between "learning", "meta-learning", and other kinds of information processing. Using a Turing machine equivalent programming language, the system itself occasionally executes self-delimiting, initially highly random "self-modification programs" which modify the context-dependent probabilities of future action sequences (including future self-modification programs). (2) The system keeps only those probability modifications computed by "useful" self-modification programs: those which bring about more payoff (reward, reinforcement) per time than all previous self-modification programs. (3) The computation of payoff per time takes into account all the computation time required for learning | the entire system life is considered: boundaries between learning trials are ignored (if there are any). A particular implementation based on the novel paradigm is presented. It is designed to exploit what conventional digital machines are good at: fast storage addressing, arithmetic operations etc. Experiments illustrate the system's mode of operation. Keywords: Self-improvement, self-reference, introspection, machine-learning, reinforcement learning. Note: This is the revised and extended version of an earlier report from November 24, 1994. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105148. 	[Title] ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time. [Abstract] A reinforcement learning system with limited computational resources interacts with an unrestricted, unknown environment. Its goal is to maximize cumulative reward, to be obtained throughout its limited, unknown lifetime. System policy is an arbitrary modifiable algorithm mapping environmental inputs and internal states to outputs and new internal states. The problem is: in realistic, unknown environments, each policy modification process (PMP) occurring during system life may have unpredictable influence on environmental states, rewards and PMPs at any later time. Existing reinforcement learning algorithms cannot properly deal with this. Neither can naive exhaustive search among all policy candidates | not even in case of very small search spaces. In fact, a reasonable way of measuring performance improvements in such general (but typical) situations is missing. I define such a measure based on the novel "reinforcement acceleration criterion" (RAC). At a given time, RAC is satisfied if the beginning of each completed PMP that computed a currently valid policy modification has been followed by long-term acceleration of average reinforcement intake (the computation time for later PMPs is taken into account). I present a method called "environment-independent reinforcement acceleration" (EIRA) which is guaranteed to achieve RAC. EIRA does neither care whether the system's policy allows for changing itself, nor whether there are multiple, interacting learning systems. Consequences are: (1) a sound theoretical framework for "meta-learning" (because the success of a PMP recursively depends on the success of all later PMPs, for which it is setting the stage). (2) A sound theoretical framework for multi-agent learning. The principles have been implemented (1) in a single system using an assembler-like programming language to modify its own policy, and (2) a system consisting of multiple agents, where each agent is in fact just a connection in a fully recurrent reinforcement learning neural net. A by-product of this research is a general reinforcement learning algorithm for such nets. Preliminary experiments illustrate the theory. Please summarise all the given information.

Paper #13205 has the title `Two Methods for Hierarchy Learning in Reinforcement Environments  
` and the abstract `This paper describes two methods for hierarchically organizing temporal behaviors. The first is more intuitive: grouping together common sequences of events into single units so that they may be treated as individual behaviors. This system immediately encounters problems, however, because the units are binary, meaning the behaviors must execute completely or not at all, and this hinders the construction of good training algorithms. The system also runs into difficulty when more than one unit is (or should be) active at the same time. The second system is a hierarchy of transition values. This hierarchy dynamically modifies the values that specify the degree to which one unit should follow another. These values are continuous, allowing the use of gradient descent during learning. Furthermore, many units are active at the same time as part of the system's normal functionings.
`.
Question: Which category should Paper #13205 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #13205 has the title `Two Methods for Hierarchy Learning in Reinforcement Environments  
` and the abstract `This paper describes two methods for hierarchically organizing temporal behaviors. The first is more intuitive: grouping together common sequences of events into single units so that they may be treated as individual behaviors. This system immediately encounters problems, however, because the units are binary, meaning the behaviors must execute completely or not at all, and this hinders the construction of good training algorithms. The system also runs into difficulty when more than one unit is (or should be) active at the same time. The second system is a hierarchy of transition values. This hierarchy dynamically modifies the values that specify the degree to which one unit should follow another. These values are continuous, allowing the use of gradient descent during learning. Furthermore, many units are active at the same time as part of the system's normal functionings.
`.
Question: Which category should Paper #13205 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