Above are papers related to the following paper: [Paper_ID] Paper #81350. 	[Title] Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks [Abstract] This paper presents U-Tree, a reinforcement learning algorithm that uses selective attention and short-term memory to simultaneously address the intertwined problems of large perceptual state spaces and hidden state. By combining the advantages of work in instance-based (or memory-based) learning and work with robust statistical tests for separating noise from task structure, the method learns quickly, creates only task-relevant state distinctions, and handles noise well. U-Tree uses a tree-structured representation, and is related to work on Prediction Suffix Trees [ Ron et al., 1994 ] , Parti-game [ Moore, 1993 ] , G-algorithm [ Chap-man and Kaelbling, 1991 ] , and Variable Resolution Dynamic Programming [ Moore, 1991 ] . It builds on Utile Suffix Memory [ McCallum, 1995c ] , which only used short-term memory, not selective perception. The algorithm is demonstrated solving a highway driving task in which the agent weaves around slower and faster traffic. The agent uses active perception with simulated eye movements. The environment has hidden state, time pressure, stochasticity, over 21,000 world states and over 2,500 percepts. From this environment and sensory system, the agent uses a utile distinction test to build a tree that represents depth-three memory where necessary, and has just 143 internal statesfar fewer than the 2500 3 states that would have resulted from a fixed-sized history-window ap proach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135746. 	[Title] Learning Markov chains with variable memory length from noisy output [Abstract] The problem of modeling complicated data sequences, such as DNA or speech, often arises in practice. Most of the algorithms select a hypothesis from within a model class assuming that the observed sequence is the direct output of the underlying generation process. In this paper we consider the case when the output passes through a memoryless noisy channel before observation. In particular, we show that in the class of Markov chains with variable memory length, learning is affected by factors, which, despite being super-polynomial, are still small in some practical cases. Markov models with variable memory length, or probabilistic finite suffix automata, were introduced in learning theory by Ron, Singer and Tishby who also described a polynomial time learning algorithm [11, 12]. We present a modification of the algorithm which uses a noise-corrupted sample and has knowledge of the noise structure. The same algorithm is still viable if the noise is not known exactly but a good estimation is available. Finally, some experimental results are presented for removing noise from corrupted English text, and to measure how the performance of the learning algorithm is affected by the size of the noisy sample and the noise rate. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18832. 	[Title] Reinforcement Learning: A Survey [Abstract] This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18833. 	[Title] Adding Memory to XCS [Abstract] We add internal memory to the XCS classifier system. We then test XCS with internal memory, named XCSM, in non-Markovian environments with two and four aliasing states. Experimental results show that XCSM can easily converge to optimal solutions in simple environments; moreover, XCSM's performance is very stable with respect to the size of the internal memory involved in learning. However, the results we present evidence that in more complex non-Markovian environments, XCSM may fail to evolve an optimal solution. Our results suggest that this happens because, the exploration strategies currently employed with XCS, are not adequate to guarantee the convergence to an optimal policy with XCSM, in complex non-Markovian environments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #55968. 	[Title] Reinforcement Learning in the Multi-Robot Domain [Abstract] This paper describes a formulation of reinforcement learning that enables learning in noisy, dynamic environemnts such as in the complex concurrent multi-robot learning domain. The methodology involves minimizing the learning space through the use behaviors and conditions, and dealing with the credit assignment problem through shaped reinforcement in the form of heterogeneous reinforcement functions and progress estimators. We experimentally validate the ap proach on a group of four mobile robots learning a foraging task. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153056. 	[Title]  [Abstract] Multigrid Q-Learning Charles W. Anderson and Stewart G. Crawford-Hines Technical Report CS-94-121 October 11, 1994 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12195. 	[Title] Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract) [Abstract] This paper describes new and efficient algorithms for learning deterministic finite automata. Our approach is primarily distinguished by two features: (1) the adoption of an average-case setting to model the "typical" labeling of a finite automaton, while retaining a worst-case model for the underlying graph of the automaton, along with (2) a learning model in which the learner is not provided with the means to experiment with the machine, but rather must learn solely by observing the automaton's output behavior on a random input sequence. The main contribution of this paper is in presenting the first efficient algorithms for learning non-trivial classes of automata in an entirely passive learning model. We adopt an on-line learning model in which the learner is asked to predict the output of the next state, given the next symbol of the random input sequence; the goal of the learner is to make as few prediction mistakes as possible. Assuming the learner has a means of resetting the target machine to a fixed start state, we first present an efficient algorithm that makes an expected polynomial number of mistakes in this model. Next, we show how this first algorithm can be used as a subroutine by a second algorithm that also makes a polynomial number of mistakes even in the absence of a reset. Along the way, we prove a number of combinatorial results for randomly labeled automata. We also show that the labeling of the states and the bits of the input sequence need not be truly random, but merely semi-random. Finally, we discuss an extension of our results to a model in which automata are used to represent distributions over binary strings. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #682666. 	[Title] Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm [Abstract] In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games to a broader framework. We design a multiagent Q-learning method under this framework, and prove that it converges to a Nash equilibrium under specified conditions. This algorithm is useful for finding the optimal strategy when there exists a unique Nash equilibrium in the game. When there exist multiple Nash equilibria in the game, this algorithm should be combined with other learning techniques to find optimal strategies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #58540. 	[Title] Machine Learning,  Explanation-Based Learning and Reinforcement Learning: A Unified View [Abstract] In speedup-learning problems, where full descriptions of operators are known, both explanation-based learning (EBL) and reinforcement learning (RL) methods can be applied. This paper shows that both methods involve fundamentally the same process of propagating information backward from the goal toward the starting state. Most RL methods perform this propagation on a state-by-state basis, while EBL methods compute the weakest preconditions of operators, and hence, perform this propagation on a region-by-region basis. Barto, Bradtke, and Singh (1995) have observed that many algorithms for reinforcement learning can be viewed as asynchronous dynamic programming. Based on this observation, this paper shows how to develop dynamic programming versions of EBL, which we call region-based dynamic programming or Explanation-Based Reinforcement Learning (EBRL). The paper compares batch and online versions of EBRL to batch and online versions of point-based dynamic programming and to standard EBL. The results show that region-based dynamic programming combines the strengths of EBL (fast learning and the ability to scale to large state spaces) with the strengths of reinforcement learning algorithms (learning of optimal policies). Results are shown in chess endgames and in synthetic maze tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5038. 	[Title] Using and combining predictors that specialize [Abstract] We study online learning algorithms that predict by combining the predictions of several subordinate prediction algorithms, sometimes called experts. These simple algorithms belong to the multiplicative weights family of algorithms. The performance of these algorithms degrades only logarithmically with the number of experts, making them particularly useful in applications where the number of experts is very large. However, in applications such as text categorization, it is often natural for some of the experts to abstain from making predictions on some of the instances. We show how to transform algorithms that assume that all experts are always awake to algorithms that do not require this assumption. We also show how to derive corresponding loss bounds. Our method is very general, and can be applied to a large family of online learning algorithms. We also give applications to various prediction models including decision graphs and switching experts. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6196. 	[Title] The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces [Abstract] Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. In high dimensions it is essential that learning does not plan uniformly over a state-space. Parti-game maintains a decision-tree partitioning of state-space and applies techniques from game-theory and computational geometry to efficiently and adaptively concentrate high resolution only on critical areas. The current version of the algorithm is designed to find feasible paths or trajectories to goal regions in high dimensional spaces. Future versions will be designed to find a solution that optimizes a real-valued criterion. Many simulated problems have been tested, ranging from two-dimensional to nine-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and planar snake robots in restricted spaces. In all cases, a good solution is found in less than ten trials and a few minutes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38722. 	[Title] Learning Probabilistic Automata with Variable Memory Length [Abstract] We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Finite Suffix Automata. The learning algorithm is motivated by real applications in man-machine interaction such as handwriting and speech recognition. Conventionally used fixed memory Markov and hidden Markov models have either severe practical or theoretical drawbacks. Though general hardness results are known for learning distributions generated by sources with similar structure, we prove that our algorithm can indeed efficiently learn distributions generated by our more restricted sources. In Particular, we show that the KL-divergence between the distribution generated by the target source and the distribution generated by our hypothesis can be made small with high confidence in polynomial time and sample complexity. We demonstrate the applicability of our algorithm by learning the structure of natural English text and using our hy pothesis for the correction of corrupted text. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17476. 	[Title] Markov games as a framework for multi-agent reinforcement learning [Abstract] In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic. 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 #976334. 	[Title] Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local [Abstract] In this paper, we describe methods for efficiently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boost the power of very approximate value functions discovered by traditional reinforcement learning techniques. We examine local searches, where the agent performs a finite-depth lookahead search, and global searches, where the agent performs a search for a trajectory all the way from the current state to a goal state. The key to the success of the global methods lies in using aggressive state-space search techniques such as uniform-cost search and A fl , tamed into a tractable form by exploiting neighborhood relations and trajectory constraints that arise from continuous-space dynamic control. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #67415. 	[Title] On the Learnability of Discrete Distributions (extended abstract) [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120731. 	[Title] A Cognitive Model of Learning to Navigate [Abstract] Our goal is to develop a cognitive model of how humans acquire skills on complex cognitive tasks. We are pursuing this goal by designing computational architectures for the NRL Navigation task, which requires competent sensorimotor coordination. In this paper, we analyze the NRL Navigation task in depth. We then use data from experiments with human subjects learning this task to guide us in constructing a cognitive model of skill acquisition for the task. Verbal protocol data augments the black box view provided by execution traces of inputs and outputs. Computational experiments allow us to explore a space of alternative architectures for the task, guided by the quality of fit to human performance data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28387. 	[Title] On the Complexity of Solving Markov Decision Problems [Abstract] Markov decision problems (MDPs) provide the foundations for a number of problems of interest to AI researchers studying automated planning and reinforcement learning. In this paper, we summarize results regarding the complexity of solving MDPs and the running time of MDP solution algorithms. We argue that, although MDPs can be solved efficiently in theory, more study is needed to reveal practical algorithms for solving large problems quickly. To encourage future research, we sketch some alternative methods of analysis that rely on the struc ture of MDPs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4584. 	[Title] References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages [Abstract] Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #892139. 	[Title] Using Communication to Reduce Locality in Distributed Multi-Agent Learning [Abstract] This paper attempts to bridge the fields of machine learning, robotics, and distributed AI. It discusses the use of communication in reducing the undesirable effects of locality in fully distributed multi-agent systems with multiple agents/robots learning in parallel while interacting with each other. Two key problems, hidden state and credit assignment, are addressed by applying local undirected broadcast communication in a dual role: as sensing and as reinforcement. The methodology is demonstrated on two multi-robot learning experiments. The first describes learning a tightly-coupled coordination task with two robots, the second a loosely-coupled task with four robots learning social rules. Communication is used to 1) share sensory data to overcome hidden state and 2) share reinforcement to overcome the credit assignment problem between the agents and bridge the gap between local/individual and global/group payoff. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51180. 	[Title] On the Learnability and Usage of Acyclic Probabilistic Finite Automata [Abstract] We propose and analyze a distribution learning algorithm for a subclass of Acyclic Probabilistic Finite Automata (APFA). This subclass is characterized by a certain distinguishability property of the automata's states. Though hardness results are known for learning distributions generated by general APFAs, we prove that our algorithm can efficiently learn distributions generated by the subclass of APFAs we consider. In particular, we show that the KL-divergence between the distribution generated by the target source and the distribution generated by our hypothesis can be made arbitrarily small with high confidence in polynomial time. We present two applications of our algorithm. In the first, we show how to model cursively written letters. The resulting models are part of a complete cursive handwriting recognition system. In the second application we demonstrate how APFAs can be used to build multiple-pronunciation models for spoken words. We evaluate the APFA based pronunciation models on labeled speech data. The good performance (in terms of the log-likelihood obtained on test data) achieved by the APFAs and the little time needed for learning suggests that the learning algorithm of APFAs might be a powerful alternative to commonly used probabilistic models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6385. 	[Title] Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents [Abstract] Intelligent human agents exist in a cooperative social environment that facilitates learning. They learn not only by trial-and-error, but also through cooperation by sharing instantaneous information, episodic experience, and learned knowledge. The key investigations of this paper are, "Given the same number of reinforcement learning agents, will cooperative agents outperform independent agents who do not communicate during learning?" and "What is the price for such cooperation?" Using independent agents as a benchmark, cooperative agents are studied in following ways: (1) sharing sensation, (2) sharing episodes, and (3) sharing learned policies. This paper shows that (a) additional sensation from another agent is beneficial if it can be used efficiently, (b) sharing learned policies or episodes among agents speeds up learning at the cost of communication, and (c) for joint tasks, agents engaging in partnership can significantly outperform independent agents although they may learn slowly in the beginning. These tradeoffs are not just limited to multi-agent reinforcement learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #65650. 	[Title] Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree [Abstract] Many algorithms for inferring a decision tree from data involve a two-phase process: First, a very large decision tree is grown which typically ends up "over-fitting" the data. To reduce over-fitting, in the second phase, the tree is pruned using one of a number of available methods. The final tree is then output and used for classification on test data. In this paper, we suggest an alternative approach to the pruning phase. Using a given unpruned decision tree, we present a new method of making predictions on test data, and we prove that our algorithm's performance will not be "much worse" (in a precise technical sense) than the predictions made by the best reasonably small pruning of the given decision tree. Thus, our procedure is guaranteed to be competitive (in terms of the quality of its predictions) with any pruning algorithm. We prove that our procedure is very efficient and highly robust. Our method can be viewed as a synthesis of two previously studied techniques. First, we apply Cesa-Bianchi et al.'s [4] results on predicting using "expert advice" (where we view each pruning as an "expert") to obtain an algorithm that has provably low prediction loss, but that is com-putationally infeasible. Next, we generalize and apply a method developed by Buntine [3], [2] and Willems, Shtarkov and Tjalkens [20], [21] to derive a very efficient implementation of this procedure. 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 #1122425. 	[Title] Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems [Abstract] The application of decision making and learning algorithms to multi-agent systems presents many interestingresearch challenges and opportunities. Among these is the ability for agents to learn how to act by observing or imitating other agents. We describe an algorithm, the IQ-algorithm, that integrates imitation with Q-learning. Roughly, a Q-learner uses the observations it has made of an expert agent to bias its exploration in promising directions. This algorithm goes beyond previous work in this direction by relaxing the oft-made assumptions that the learner (observer) and the expert (observed agent) share the same objectives and abilities. Our preliminary experiments demonstrate significant transfer between agents using the IQ-model and in many cases reductions in training time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106172. 	[Title] Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation. [Abstract] This paper presents Fringe Exploration, a technique for efficient exploration in partially observable domains. The key idea, (applicable to many exploration techniques), is to keep statistics in the space of possible short-term memories, instead of in the agent's current state space. Experimental results in a partially observable maze and in a difficult driving task with visual routines show dramatic performance improvements. Please summarise all the given information.

Paper #81350 has the title `Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks  
` and the abstract `This paper presents U-Tree, a reinforcement learning algorithm that uses selective attention and short-term memory to simultaneously address the intertwined problems of large perceptual state spaces and hidden state. By combining the advantages of work in instance-based (or memory-based) learning and work with robust statistical tests for separating noise from task structure, the method learns quickly, creates only task-relevant state distinctions, and handles noise well. U-Tree uses a tree-structured representation, and is related to work on Prediction Suffix Trees [ Ron et al., 1994 ] , Parti-game [ Moore, 1993 ] , G-algorithm [ Chap-man and Kaelbling, 1991 ] , and Variable Resolution Dynamic Programming [ Moore, 1991 ] . It builds on Utile Suffix Memory [ McCallum, 1995c ] , which only used short-term memory, not selective perception. The algorithm is demonstrated solving a highway driving task in which the agent weaves around slower and faster traffic. The agent uses active perception with simulated eye movements. The environment has hidden state, time pressure, stochasticity, over 21,000 world states and over 2,500 percepts. From this environment and sensory system, the agent uses a utile distinction test to build a tree that represents depth-three memory where necessary, and has just 143 internal statesfar fewer than the 2500 3 states that would have resulted from a fixed-sized history-window ap proach.
`.
Question: Which category should Paper #81350 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #81350 has the title `Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks  
` and the abstract `This paper presents U-Tree, a reinforcement learning algorithm that uses selective attention and short-term memory to simultaneously address the intertwined problems of large perceptual state spaces and hidden state. By combining the advantages of work in instance-based (or memory-based) learning and work with robust statistical tests for separating noise from task structure, the method learns quickly, creates only task-relevant state distinctions, and handles noise well. U-Tree uses a tree-structured representation, and is related to work on Prediction Suffix Trees [ Ron et al., 1994 ] , Parti-game [ Moore, 1993 ] , G-algorithm [ Chap-man and Kaelbling, 1991 ] , and Variable Resolution Dynamic Programming [ Moore, 1991 ] . It builds on Utile Suffix Memory [ McCallum, 1995c ] , which only used short-term memory, not selective perception. The algorithm is demonstrated solving a highway driving task in which the agent weaves around slower and faster traffic. The agent uses active perception with simulated eye movements. The environment has hidden state, time pressure, stochasticity, over 21,000 world states and over 2,500 percepts. From this environment and sensory system, the agent uses a utile distinction test to build a tree that represents depth-three memory where necessary, and has just 143 internal statesfar fewer than the 2500 3 states that would have resulted from a fixed-sized history-window ap proach.
`.
Question: Which category should Paper #81350 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