Above are papers related to the following paper: [Paper_ID] Paper #1104182. 	[Title] Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to [Abstract] In many learning tasks, data-query is neither free nor of constant cost. Often the cost of a query depends on the distance from the current location in state space to the desired query point. This is easiest to visualize in robotics environments where a robot must physically move to a location in order to learn something there. The cost of this learning is the time and effort it takes to reach the new location. Furthermore, this cost is characterized by a distance relationship: When the robot moves as directly as possible from a source state to a destination state, the states through which it passes are closer (i.e., cheaper to reach) than is the destination state. Distance relationships hold in many real-world non-robotics tasks also | any environment where states are not immediately accessible. Optimiz- ing the performance of a chemical plant, for example, requires the adjustment of analog controls which have a continuum of intermediate states. Querying possibly optimal regions of state space in these environments is inadvisable if the path to the query point intersects a region of known volatility. In discrete environments with small numbers of states, it's possible to keep track of precisely where and to what degree learning has already been done sufficiently and where it still needs to be done. It is also possible to keep best known estimates of the distances from each state to each other (see Kaelbling, 1993). Kael- bling's DG-learning algorithm is based on Floyd's all- pairs shortest-path algorithm (Aho, Hopcroft, & Ull- man 1983) and is just slightly different from that used here. These "all-goals" algorithms (after Kaelbling) can provide a highly satisfying representation of the distance/benefit tradeoff. where E x is the exploration value of state x (the potential benefit of exploring state x), D xy is the distance to state y, and A xy is the action to take in state x to move most cheaply to state y. This information can be learned incrementally and completely : That is, it can be guaranteed that if a path from any state x to any state y is deducible from the state transitions seen so far, then (1) the algorithm will have a non-null entry for S xy (i.e., the algorithm will know a path from x to y), and (2) The current value for D xy will be the best deducible value from all data seen so far. With this information, decisions about which areas to explore next can be based on not just the amount to be gained from such exploration but also on the cost of reaching each area together with the benefit of incidental exploration done on the way. Though optimal exploration is NP-hard (i.e., it's at least as difficult as TSP) good approximations are easily computable. One such good approximation is to take the action at each state that leads in the direction of greatest accumulated exploration benefit: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6209. 	[Title] Transfer of Learning by Composing Solutions of Elemental Sequential Tasks [Abstract] Although building sophisticated learning agents that operate in complex environments will require learning to perform multiple tasks, most applications of reinforcement learning have focussed on single tasks. In this paper I consider a class of sequential decision tasks (SDTs), called composite sequential decision tasks, formed by temporally concatenating a number of elemental sequential decision tasks. Elemental SDTs cannot be decomposed into simpler SDTs. I consider a learning agent that has to learn to solve a set of elemental and composite SDTs. I assume that the structure of the composite tasks is unknown to the learning agent. The straightforward application of reinforcement learning to multiple tasks requires learning the tasks separately, which can waste computational resources, both memory and time. I present a new learning algorithm and a modular architecture that learns the decomposition of composite SDTs, and achieves transfer of learning by sharing the solutions of elemental SDTs across multiple composite SDTs. The solution of a composite SDT is constructed by computationally inexpensive modifications of the solutions of its constituent elemental SDTs. I provide a proof of one aspect of the learning algorithm. 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.

Above are papers related to the following paper: [Paper_ID] Paper #6214. 	[Title] Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming [Abstract] This paper extends previous work with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate trial-and-error (reinforcement) learning and execution-time planning into a single process operating alternately on the world and on a learned model of the world. In this paper, I present and show results for two Dyna architectures. The Dyna-PI architecture is based on dynamic programming's policy iteration method and can be related to existing AI ideas such as evaluation functions and universal plans (reactive systems). Using a navigation task, results are shown for a simple Dyna-PI system that simultaneously learns by trial and error, learns a world model, and plans optimal routes using the evolving world model. The Dyna-Q architecture is based on Watkins's Q-learning, a new kind of reinforcement learning. Dyna-Q uses a less familiar set of data structures than does Dyna-PI, but is arguably simpler to implement and use. We show that Dyna-Q architectures are easy to adapt for use in changing environments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27623. 	[Title] Neural Network Exploration Using Optimal Experiment Design [Abstract] We consider the question "How should one act when the only goal is to learn as much as possible?" Building on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Optimal Experiment Design (OED) to guide the query/action selection of a neural network learner. We demonstrate that these techniques allow the learner to minimize its generalization error by exploring its domain efficiently and completely. We conclude that, while not a panacea, OED-based query/action has much to offer, especially in domains where its high computational costs can be tolerated. This report describes research done at the Center for Biological and Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC-9217041. The author was also funded by ATR Human Information Processing Laboratories, Siemens Corporate Research and NSF grant CDA-9309300. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #105899. 	[Title] In:  A Mixture Model System for Medical and Machine Diagnosis [Abstract] Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distribution of the data can be used to solve this problem. We model this with mixture models whose parameters are estimated by the EM algorithm. This gives the benefit that missing data in the database itself can also be handled correctly. The request for new information to refine the diagnosis is performed using the maximum utility principle. Since the system is based on learning it is domain independent and less labor intensive than expert systems or probabilistic networks. An example using a heart disease database is presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #686061. 	[Title] An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks. [Abstract] Macquarie University Technical Report No. 95-173C Department of Computing School of MPCE, Macquarie University, New South Wales, Australia Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #686030. 	[Title] Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons [Abstract] We propose an active learning method with hidden-unit reduction, which is devised specially for multilayer perceptrons (MLP). First, we review our active learning method, and point out that many Fisher-information-based methods applied to MLP have a critical problem: the information matrix may be singular. To solve this problem, we derive the singularity condition of an information matrix, and propose an active learning technique that is applicable to Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1126029. 	[Title] Exploration in Active Learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38480. 	[Title] Learning to Achieve Goals [Abstract] Temporal difference methods solve the temporal credit assignment problem for reinforcement learning. An important subproblem of general reinforcement learning is learning to achieve dynamic goals. Although existing temporal difference methods, such as Q learning, can be applied to this problem, they do not take advantage of its special structure. This paper presents the DG-learning algorithm, which learns efficiently to achieve dynamically changing goals and exhibits good knowledge transfer between goals. In addition, this paper shows how traditional relaxation techniques can be applied to the problem. Finally, experimental results are given that demonstrate the superiority of DG learning over Q learning in a moderately large, synthetic, non-deterministic domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27632. 	[Title] REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS [Abstract] For an agent living in a non-deterministic Markov environment (NME), what is, in theory, the fastest way of acquiring information about its statistical properties? The answer is: to design "optimal" sequences of "experiments" by performing action sequences that maximize expected information gain. This notion is implemented by combining concepts from information theory and reinforcement learning. Experiments show that the resulting method, reinforcement driven information acquisition, can explore certain NMEs much faster than conventional random exploration. 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 #117. 	[Title] Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms [Abstract] The problem of maximizing the expected total discounted reward in a completely observable Markovian environment, i.e., a Markov decision process (mdp), models a particular class of sequential decision problems. Algorithms have been developed for making optimal decisions in mdps given either an mdp specification or the opportunity to interact with the mdp over time. Recently, other sequential decision-making problems have been studied prompting the development of new algorithms and analyses. We describe a new generalized model that subsumes mdps as well as many of the recent variations. We prove some basic results concerning this model and develop generalizations of value iteration, policy iteration, model-based reinforcement-learning, and Q-learning that can be used to make optimal decisions in the generalized model under various assumptions. Applications of the theory to particular models are described, including risk-averse mdps, exploration-sensitive mdps, sarsa, Q-learning with spreading, two-player games, and approximate max picking via sampling. Central to the results are the contraction property of the value operator and a stochastic-approximation theorem that reduces asynchronous convergence to synchronous convergence. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #75674. 	[Title] Exploration Bonuses and Dual Control [Abstract] Finding the Bayesian balance between exploration and exploitation in adaptive optimal control is in general intractable. This paper shows how to compute suboptimal estimates based on a certainty equivalence approximation arising from a form of dual control. This systematizes and extends existing uses of exploration bonuses in reinforcement learning (Sutton, 1990). The approach has two components: a statistical model of uncertainty in the world and a way of turning this into exploratory behaviour. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28254. 	[Title] In  Active Learning with Statistical Models [Abstract] For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regres sion are both efficient and accurate. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #686015. 	[Title] "What is the best thing to do right now?": getting beyond greedy exploration [Abstract]  Please summarise all the given information.

Paper #1104182 has the title `Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to
` and the abstract `In many learning tasks, data-query is neither free nor of constant cost. Often the cost of a query depends on the distance from the current location in state space to the desired query point. This is easiest to visualize in robotics environments where a robot must physically move to a location in order to learn something there. The cost of this learning is the time and effort it takes to reach the new location. Furthermore, this cost is characterized by a distance relationship: When the robot moves as directly as possible from a source state to a destination state, the states through which it passes are closer (i.e., cheaper to reach) than is the destination state. Distance relationships hold in many real-world non-robotics tasks also | any environment where states are not immediately accessible. Optimiz- ing the performance of a chemical plant, for example, requires the adjustment of analog controls which have a continuum of intermediate states. Querying possibly optimal regions of state space in these environments is inadvisable if the path to the query point intersects a region of known volatility. In discrete environments with small numbers of states, it's possible to keep track of precisely where and to what degree learning has already been done sufficiently and where it still needs to be done. It is also possible to keep best known estimates of the distances from each state to each other (see Kaelbling, 1993). Kael- bling's DG-learning algorithm is based on Floyd's all- pairs shortest-path algorithm (Aho, Hopcroft, & Ull- man 1983) and is just slightly different from that used here. These "all-goals" algorithms (after Kaelbling) can provide a highly satisfying representation of the distance/benefit tradeoff. where E x is the exploration value of state x (the potential benefit of exploring state x), D xy is the distance to state y, and A xy is the action to take in state x to move most cheaply to state y. This information can be learned incrementally and completely : That is, it can be guaranteed that if a path from any state x to any state y is deducible from the state transitions seen so far, then (1) the algorithm will have a non-null entry for S xy (i.e., the algorithm will know a path from x to y), and (2) The current value for D xy will be the best deducible value from all data seen so far. With this information, decisions about which areas to explore next can be based on not just the amount to be gained from such exploration but also on the cost of reaching each area together with the benefit of incidental exploration done on the way. Though optimal exploration is NP-hard (i.e., it's at least as difficult as TSP) good approximations are easily computable. One such good approximation is to take the action at each state that leads in the direction of greatest accumulated exploration benefit: 
`.
Question: Which category should Paper #1104182 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1104182 has the title `Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to
` and the abstract `In many learning tasks, data-query is neither free nor of constant cost. Often the cost of a query depends on the distance from the current location in state space to the desired query point. This is easiest to visualize in robotics environments where a robot must physically move to a location in order to learn something there. The cost of this learning is the time and effort it takes to reach the new location. Furthermore, this cost is characterized by a distance relationship: When the robot moves as directly as possible from a source state to a destination state, the states through which it passes are closer (i.e., cheaper to reach) than is the destination state. Distance relationships hold in many real-world non-robotics tasks also | any environment where states are not immediately accessible. Optimiz- ing the performance of a chemical plant, for example, requires the adjustment of analog controls which have a continuum of intermediate states. Querying possibly optimal regions of state space in these environments is inadvisable if the path to the query point intersects a region of known volatility. In discrete environments with small numbers of states, it's possible to keep track of precisely where and to what degree learning has already been done sufficiently and where it still needs to be done. It is also possible to keep best known estimates of the distances from each state to each other (see Kaelbling, 1993). Kael- bling's DG-learning algorithm is based on Floyd's all- pairs shortest-path algorithm (Aho, Hopcroft, & Ull- man 1983) and is just slightly different from that used here. These "all-goals" algorithms (after Kaelbling) can provide a highly satisfying representation of the distance/benefit tradeoff. where E x is the exploration value of state x (the potential benefit of exploring state x), D xy is the distance to state y, and A xy is the action to take in state x to move most cheaply to state y. This information can be learned incrementally and completely : That is, it can be guaranteed that if a path from any state x to any state y is deducible from the state transitions seen so far, then (1) the algorithm will have a non-null entry for S xy (i.e., the algorithm will know a path from x to y), and (2) The current value for D xy will be the best deducible value from all data seen so far. With this information, decisions about which areas to explore next can be based on not just the amount to be gained from such exploration but also on the cost of reaching each area together with the benefit of incidental exploration done on the way. Though optimal exploration is NP-hard (i.e., it's at least as difficult as TSP) good approximations are easily computable. One such good approximation is to take the action at each state that leads in the direction of greatest accumulated exploration benefit: 
`.
Question: Which category should Paper #1104182 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