Above are papers related to the following paper: [Paper_ID] Paper #130. 	[Title] Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function [Abstract] Almost all the work in Average-reward Re- inforcement Learning (ARL) so far has focused on table-based methods which do not scale to domains with large state spaces. In this paper, we propose two extensions to a model-based ARL method called H-learning to address the scale-up problem. We extend H-learning to learn action models and reward functions in the form of Bayesian networks, and approximate its value function using local linear regression. We test our algorithms on several scheduling tasks for a simulated Automatic Guided Vehicle (AGV) and show that they are effective in significantly reducing the space requirement of H-learning and making it converge faster. To the best of our knowledge, our results are the first in apply Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #128. 	[Title] Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes [Abstract] Reinforcement learning (RL) has become a central paradigm for solving learning-control problems in robotics and artificial intelligence. RL researchers have focussed almost exclusively on problems where the controller has to maximize the discounted sum of payoffs. However, as emphasized by Schwartz (1993), in many problems, e.g., those for which the optimal behavior is a limit cycle, it is more natural and com-putationally advantageous to formulate tasks so that the controller's objective is to maximize the average payoff received per time step. In this paper I derive new average-payoff RL algorithms as stochastic approximation methods for solving the system of equations associated with the policy evaluation and optimal control questions in average-payoff RL tasks. These algorithms are analogous to the popular TD and Q-learning algorithms already developed for the discounted-payoff case. One of the algorithms derived here is a significant variation of Schwartz's R-learning algorithm. Preliminary empirical results are presented to validate these new algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #37888. 	[Title] Stochastic simulation algorithms for dynamic probabilistic networks [Abstract] Stochastic simulation algorithms such as likelihood weighting often give fast, accurate approximations to posterior probabilities in probabilistic networks, and are the methods of choice for very large networks. Unfortunately, the special characteristics of dynamic probabilistic networks (DPNs), which are used to represent stochastic temporal processes, mean that standard simulation algorithms perform very poorly. In essence, the simulation trials diverge further and further from reality as the process is observed over time. In this paper, we present simulation algorithms that use the evidence observed at each time step to push the set of trials back towards reality. The first algorithm, "evidence reversal" (ER) restructures each time slice of the DPN so that the evidence nodes for the slice become ancestors of the state variables. The second algorithm, called "survival of the fittest" sampling (SOF), "repopulates" the set of trials at each time step using a stochastic reproduction rate weighted by the likelihood of the evidence according to each trial. We compare the performance of each algorithm with likelihood weighting on the original network, and also investigate the benefits of combining the ER and SOF methods. The ER/SOF combination appears to maintain bounded error independent of the number of time steps in the simulation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #7432. 	[Title] How to Dynamically Merge Markov Decision Processes [Abstract] We are frequently called upon to perform multiple tasks that compete for our attention and resource. Often we know the optimal solution to each task in isolation; in this paper, we describe how this knowledge can be exploited to efficiently find good solutions for doing the tasks in parallel. We formulate this problem as that of dynamically merging multiple Markov decision processes (MDPs) into a composite MDP, and present a new theoretically-sound dynamic programming algorithm for finding an optimal policy for the composite MDP. We analyze various aspects of our algorithm and Every day, we are faced with the problem of doing multiple tasks in parallel, each of which competes for our attention and resource. If we are running a job shop, we must decide which machines to allocate to which jobs, and in what order, so that no jobs miss their deadlines. If we are a mail delivery robot, we must find the intended recipients of the mail while simultaneously avoiding fixed obstacles (such as walls) and mobile obstacles (such as people), and still manage to keep ourselves sufficiently charged up. Frequently we know how to perform each task in isolation; this paper considers how we can take the information we have about the individual tasks and combine it to efficiently find an optimal solution for doing the entire set of tasks in parallel. More importantly, we describe a theoretically-sound algorithm for doing this merging dynamically; new tasks (such as a new job arrival at a job shop) can be assimilated online into the solution being found for the ongoing set of simultaneous tasks. illustrate its use on a simple merging problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13960. 	[Title] Using a Case Base of Surfaces to Speed-Up Reinforcement Learning [Abstract] This paper demonstrates the exploitation of certain vision processing techniques to index into a case base of surfaces. The surfaces are the result of reinforcement learning and represent the optimum choice of actions to achieve some goal from anywhere in the state space. This paper shows how strong features that occur in the interaction of the system with its environment can be detected early in the learning process. Such features allow the system to identify when an identical, or very similar, task has been solved previously and to retrieve the relevant surface. This results in an orders of magnitude increase in learning rate. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6155. 	[Title] On the Computational Economics of Reinforcement Learning [Abstract] Following terminology used in adaptive control, we distinguish between indirect learning methods, which learn explicit models of the dynamic structure of the system to be controlled, and direct learning methods, which do not. We compare an existing indirect method, which uses a conventional dynamic programming algorithm, with a closely related direct reinforcement learning method by applying both methods to an infinite horizon Markov decision problem with unknown state-transition probabilities. The simulations show that although the direct method requires much less space and dramatically less computation per control action, its learning ability in this task is superior to, or compares favorably with, that of the more complex indirect method. Although these results do not address how the methods' performances compare as problems become more difficult, they suggest that given a fixed amount of computational power available per control action, it may be better to use a direct reinforcement learning method augmented with indirect techniques than to devote all available resources to a computation-ally costly indirect method. Comprehensive answers to the questions raised by this study depend on many factors making up the eco nomic context of the computation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1111052. 	[Title] Robust Value Function Approximation by Working Backwards Computing an accurate value function is the key [Abstract] In this paper, we examine the intuition that TD() is meant to operate by approximating asynchronous value iteration. We note that on the important class of discrete acyclic stochastic tasks, value iteration is inefficient compared with the DAG-SP algorithm, which essentially performs only one sweep instead of many by working backwards from the goal. The question we address in this paper is whether there is an analogous algorithm that can be used in large stochastic state spaces requiring function approximation. We present such an algorithm, analyze it, and give comparative results to TD on several domains. the state). Using VI to solve MDPs belonging to either of these special classes can be quite inefficient, since VI performs backups over the entire space, whereas the only backups useful for improving V fl are those on the "frontier" between already-correct and not-yet-correct V fl values. In fact, there are classical algorithms for both problem classes which compute V fl more efficiently by explicitly working backwards: for the deterministic class, Dijkstra's shortest-path algorithm; and for the acyclic class, Directed-Acyclic-Graph-Shortest-Paths (DAG-SP) [6]. 1 DAG-SP first topologically sorts the MDP, producing a linear ordering of the states in which every state x precedes all states reachable from x. Then, it runs through that list in reverse, performing one backup per state. Worst-case bounds for VI, Dijkstra, and DAG-SP in deterministic domains with X states and A actions/state are 1 Although [6] presents DAG-SP only for deterministic acyclic problems, it applies straightforwardly to the Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114125. 	[Title] H-learning: A Reinforcement Learning Method to Optimize Undiscounted Average Reward [Abstract] In this paper, we introduce a model-based reinforcement learning method called H-learning, which optimizes undiscounted average reward. We compare it with three other reinforcement learning methods in the domain of scheduling Automatic Guided Vehicles, transportation robots used in modern manufacturing plants and facilities. The four methods differ along two dimensions. They are either model-based or model-free, and optimize discounted total reward or undiscounted average reward. Our experimental results indicate that H-learning is more robust with respect to changes in the domain parameters, and in many cases, converges in fewer steps to better average reward per time step than all the other methods. An added advantage is that unlike the other methods it does not have any parameters to tune. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13966. 	[Title] A Teaching Strategy for Memory-Based Control [Abstract] Combining different machine learning algorithms in the same system can produce benefits above and beyond what either method could achieve alone. This paper demonstrates that genetic algorithms can be used in conjunction with lazy learning to solve examples of a difficult class of delayed reinforcement learning problems better than either method alone. This class, the class of differential games, includes numerous important control problems that arise in robotics, planning, game playing, and other areas, and solutions for differential games suggest solution strategies for the general class of planning and control problems. We conducted a series of experiments applying three learning approaches|lazy Q-learning, k-nearest neighbor (k-NN), and a genetic algorithm|to a particular differential game called a pursuit game. Our experiments demonstrate that k-NN had great difficulty solving the problem, while a lazy version of Q-learning performed moderately well and the genetic algorithm performed even better. These results motivated the next step in the experiments, where we hypothesized k-NN was having difficulty because it did not have good examples-a common source of difficulty for lazy learning. Therefore, we used the genetic algorithm as a bootstrapping method for k-NN to create a system to provide these examples. Our experiments demonstrate that the resulting joint system learned to solve the pursuit games with a high degree of accuracy-outperforming either method alone-and with relatively small memory requirements. 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 #1113739. 	[Title] Decision Tree Function Approximation in Reinforcement Learning [Abstract] We present a decision tree based approach to function approximation in reinforcement learning. We compare our approach with table lookup and a neural network function approximator on three problems: the well known mountain car and pole balance problems as well as a simulated automobile race car. We find that the decision tree can provide better learning performance than the neural network function approximation and can solve large problems that are infeasible using table lookup. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #755217. 	[Title] Active Gesture Recognition using Partially Observable Markov Decision Processes [Abstract] M.I.T Media Laboratory Perceptual Computing Section Technical Report No. 367 Appeared 13th IEEE Intl. Conference on Pattern Recognition (ICPR '96), Vienna, Austria. Abstract We present a foveated gesture recognition system that guides an active camera to foveate salient features based on a reinforcement learning paradigm. Using vision routines previously implemented for an interactive environment, we determine the spatial location of salient body parts of a user and guide an active camera to obtain images of gestures or expressions. A hidden-state reinforcement learning paradigm based on the Partially Observable Markov Decision Process (POMDP) is used to implement this visual attention. The attention module selects targets to foveate based on the goal of successful recognition, and uses a new multiple-model Q-learning formulation. Given a set of target and distractor gestures, our system can learn where to foveate to maximally discriminate a particular gesture. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6163. 	[Title] Generalization in Reinforcement Learning: Safely Approximating the Value Function [Abstract] To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to the curse of dimensionality in reinforcement learning and dynamic programming is to replace the lookup table with a generalizing function approximator such as a neural net. Although this has been successful in the domain of backgammon, there is no guarantee of convergence. In this paper, we show that the combination of dynamic programming and function approximation is not robust, and in even very benign cases, may produce an entirely wrong policy. We then introduce Grow-Support, a new algorithm which is safe from divergence yet can still reap the benefits of successful generalization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #345340. 	[Title] Generalized Prioritized Sweeping [Abstract] Prioritized sweeping is a model-based reinforcement learning method that attempts to focus an agent's limited computational resources to achieve a good estimate of the value of environment states. To choose effectively where to spend a costly planning step, classic prioritized sweeping uses a simple heuristic to focus computation on the states that are likely to have the largest errors. In this paper, we introduce generalized prioritized sweeping, a principled method for generating such estimates in a representation-specific manner. This allows us to extend prioritized sweeping beyond an explicit, state-based representation to deal with compact representations that are necessary for dealing with large state spaces. We apply this method for generalized model approximators (such as Bayesian networks), and describe preliminary experiments that compare our approach with classical prioritized sweeping. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8213. 	[Title] Robust Reinforcement Learning in Motion Planning [Abstract] While exploring to find better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, or even catastrophic, results, often modeled in terms of reaching `failure' states of the agent's environment. This paper presents a method that uses domain knowledge to reduce the number of failures during exploration. This method formulates the set of actions from which the RL agent composes a control policy to ensure that exploration is conducted in a policy space that excludes most of the unacceptable policies. The resulting action set has a more abstract relationship to the task being solved than is common in many applications of RL. Although the cost of this added safety is that learning may result in a suboptimal solution, we argue that this is an appropriate tradeoff in many problems. We illustrate this method in the domain of motion planning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106966. 	[Title] Value Function Approximations and Job-Shop Scheduling [Abstract] We report a successful application of TD() with value function approximation to the task of job-shop scheduling. Our scheduling problems are based on the problem of scheduling payload processing steps for the NASA space shuttle program. The value function is approximated by a 2-layer feedforward network of sigmoid units. A one-step lookahead greedy algorithm using the learned evaluation function outperforms the best existing algorithm for this task, which is an iterative repair method incorporating simulated annealing. To understand the reasons for this performance improvement, this paper introduces several measurements of the learning process and discusses several hypotheses suggested by these measurements. We conclude that the use of value function approximation is not a source of difficulty for our method, and in fact, it may explain the success of the method independent of the use of value iteration. Additional experiments are required to discriminate among our hypotheses. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6170. 	[Title] Improving Policies without Measuring Merits [Abstract] Performing policy iteration in dynamic programming should only require knowledge of relative rather than absolute measures of the utility of actions what Baird (1993) calls the advantages of actions at states. Nevertheless, existing methods in dynamic programming (including Baird's) compute some form of absolute utility function. For smooth problems, advantages satisfy two differential consistency conditions (including the requirement that they be free of curl), and we show that enforcing these can lead to appropriate policy improvement solely in terms of advantages. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #189856. 	[Title] State-Space Abstraction for Anytime Evaluation of Probabilistic Networks [Abstract] One important factor determining the computa - tional complexity of evaluating a probabilistic network is the cardinality of the state spaces of the nodes. By varying the granularity of the state spaces, one can trade off accuracy in the result for computational efficiency. We present an any - time procedure for approximate evaluation of probabilistic networks based on this idea. On application to some simple networks, the proce - dure exhibits a smooth improvement in approxi - mation quality as computation time increases. This suggests that statespace abstraction is one more useful control parameter for designing real-time probabilistic reasoners. 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 #124064. 	[Title] An Upper Bound on the Loss from Approximate Optimal-Value Functions [Abstract] Many reinforcement learning (RL) approaches can be formulated from the theory of Markov decision processes and the associated method of dynamic programming (DP). The value of this theoretical understanding, however, is tempered by many practical concerns. One important question is whether DP-based approaches that use function approximation rather than lookup tables, can avoid catastrophic effects on performance. This note presents a result in Bertsekas (1987) which guarantees that small errors in the approximation of a task's optimal value function cannot produce arbitrarily bad performance when actions are selected greedily. We derive an upper bound on performance loss which is slightly tighter than that in Bertsekas (1987), and we show the extension of the bound to Q-learning (Watkins, 1989). These results provide a theoretical justification for a practice that is common in reinforcement learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108389. 	[Title] Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks [Abstract] We present an algorithm for arc reversal in Bayesian networks with tree-structured conditional probability tables, and consider some of its advantages, especially for the simulation of dynamic probabilistic networks. In particular, the method allows one to produce CPTs for nodes involved in the reversal that exploit regularities in the conditional distributions. We argue that this approach alleviates some of the overhead associated with arc reversal, plays an important role in evidence integration and can be used to restrict sampling of variables in DPNs. We also provide an algorithm that detects the dynamic irrelevance of state variables in forward simulation. This algorithm exploits the structured CPTs in a reversed network to determine, in a time-independent fashion, the conditions under which a variable does or does not need to be sampled. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118245. 	[Title] Learning sorting and decision trees with POMDPs [Abstract] pomdps are general models of sequential decisions in which both actions and observations can be probabilistic. Many problems of interest can be formulated as pomdps, yet the use of pomdps has been limited by the lack of effective algorithms. Recently this has started to change and a number of problems such as robot navigation and planning are beginning to be formulated and solved as pomdps. The advantage of the pomdp approach is its clean semantics and its ability to produce principled solutions that integrate physical and information gathering actions. In this paper we pursue this approach in the context of two learning tasks: learning to sort a vector of numbers and learning decision trees from data. Both problems are formulated as pomdps and solved by a general pomdp algorithm. The main lessons and results are that 1) the use of suitable heuristics and representations allows for the solution of sorting and classification pomdps of non-trivial sizes, 2) the quality of the resulting solutions are competitive with the best algorithms, and 3) problematic aspects in decision tree learning such as test and mis-classification costs, noisy tests, and missing values are naturally accommodated. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28202. 	[Title] MultiPlayer Residual Advantage Learning With General Function Approximation [Abstract] A new algorithm, advantage learning, is presented that improves on advantage updating by requiring that a single function be learned rather than two. Furthermore, advantage learning requires only a single type of update, the learning update, while advantage updating requires two different types of updates, a learning update and a normilization update. The reinforcement learning system uses the residual form of advantage learning. An application of reinforcement learning to a Markov game is presented. The testbed has continuous states and nonlinear dynamics. The game consists of two players, a missile and a plane; the missile pursues the plane and the plane evades the missile. On each time step , each player chooses one of two possible actions; turn left or turn right, resulting in a 90 degree instantaneous change in the aircraft s heading. Reinforcement is given only when the missile hits the plane or the plane reaches an escape distance from the missile. The advantage function is stored in a single-hidden-layer sigmoidal network. Speed of learning is increased by a new algorithm , Incremental Delta-Delta (IDD), which extends Jacobs (1988) Delta-Delta for use in incremental training, and differs from Suttons Incremental Delta-Bar-Delta (1992) in that it does not require the use of a trace and is amenable for use with general function approximation systems. The advantage learning algorithm for optimal control is modified for games in order to find the minimax point, rather than the maximum. Empirical results gathered using the missile/aircraft testbed validate theory that suggests residual forms of reinforcement learning algorithms converge to a local minimum of the mean squared Bellman residual when using general function approximation systems. Also, to our knowledge, this is the first time an approximate second order method has been used with residual algorithms. Empirical results are presented comparing convergence rates with and without the use of IDD for the reinforcement learning testbed described above and for a supervised learning testbed. The results of these experiments demonstrate IDD increased the rate of convergence and resulted in an order of magnitude lower total asymptotic error than when using backpropagation alone. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120170. 	[Title] Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email [Abstract] This paper describes a novel method by which a dialogue agent can learn to choose an optimal dialogue strategy. While it is widely agreed that dialogue strategies should be formulated in terms of communicative intentions, there has been little work on automatically optimizing an agent's choices when there are multiple ways to realize a communicative intention. Our method is based on a combination of learning algorithms and empirical evaluation techniques. The learning component of our method is based on algorithms for reinforcement learning, such as dynamic programming and Q-learning. The empirical component uses the PARADISE evaluation framework (Walker et al., 1997) to identify the important performance factors and to provide the performance function needed by the learning algorithm. We illustrate our method with a dialogue agent named ELVIS (EmaiL Voice Interactive System), that supports access to email over the phone. We show how ELVIS can learn to choose among alternate strategies for agent initiative, for reading messages, and for summarizing email folders. 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 #191404. 	[Title] Exploiting Structure in Policy Construction [Abstract] Markov decision processes (MDPs) have recently been applied to the problem of modeling decision-theoretic planning. While traditional methods for solving MDPs are often practical for small states spaces, their effectiveness for large AI planning problems is questionable. We present an algorithm, called structured policy iteration (SPI), that constructs optimal policies without explicit enumeration of the state space. The algorithm retains the fundamental computational steps of the commonly used modified policy iteration algorithm, but exploits the variable and propositional independencies reflected in a temporal Bayesian network representation of MDPs. The principles behind SPI can be applied to any structured representation of stochastic actions, policies and value functions, and the algorithm itself can be used in conjunction with re cent approximation methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107455. 	[Title] Solving Combinatorial Optimization Tasks by Reinforcement Learning: A General Methodology Applied to Resource-Constrained Scheduling [Abstract] This paper introduces a methodology for solving combinatorial optimization problems through the application of reinforcement learning methods. The approach can be applied in cases where several similar instances of a combinatorial optimization problem must be solved. The key idea is to analyze a set of "training" problem instances and learn a search control policy for solving new problem instances. The search control policy has the twin goals of finding high-quality solutions and finding them quickly. Results of applying this methodology to a NASA scheduling problem show that the learned search control policy is much more effective than the best known non-learning search procedure|a method based on simulated annealing. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #434. 	[Title] Learning Analytically and Inductively [Abstract] Learning is a fundamental component of intelligence, and a key consideration in designing cognitive architectures such as Soar [ Laird et al., 1986 ] . This chapter considers the question of what constitutes an appropriate general-purpose learning mechanism. We are interested in mechanisms that might explain and reproduce the rich variety of learning capabilities of humans, ranging from learning perceptual-motor skills such as how to ride a bicycle, to learning highly cognitive tasks such as how to play chess. Research on learning in fields such as cognitive science, artificial intelligence, neurobiology, and statistics has led to the identification of two distinct classes of learning methods: inductive and analytic. Inductive methods, such as neural network Backpropagation, learn general laws by finding statistical correlations and regularities among a large set of training examples. In contrast, analytical methods, such as Explanation-Based Learning, acquire general laws from many fewer training examples. They rely instead on prior knowledge to analyze individual training examples in detail, then use this analysis to distinguish relevant example features from the irrelevant. The question considered in this chapter is how to best combine inductive and analytical learning in an architecture that seeks to cover the range of learning exhibited by intelligent systems such as humans. We present a specific learning mechanism, Explanation Based Neural Network learning (EBNN), that blends these two types of learning, and present experimental results demonstrating its ability to learn control strategies for a mobile robot using 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 #116790. 	[Title] Sequential Update of Bayesian Network Structure [Abstract] There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new data. While sequential update of parameters for a fixed structure can be accomplished using standard techniques, sequential update of network structure is still an open problem. In this paper, we investigate sequential update of Bayesian networks were both parameters and structure are expected to change. We introduce a new approach that allows for the flexible manipulation of the tradeoff between the quality of the learned networks and the amount of information that is maintained about past observations. We formally describe our approach including the necessary modifications to the scoring functions for learning Bayesian networks, evaluate its effectiveness through and empirical study, and extend it to the case of missing data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20534. 	[Title] A Reinforcement Learning Approach to Job-shop Scheduling [Abstract] We apply reinforcement learning methods to learn domain-specific heuristics for job shop scheduling. A repair-based scheduler starts with a critical-path schedule and incrementally repairs constraint violations with the goal of finding a short conflict-free schedule. The temporal difference algorithm T D() is applied to train a neural network to learn a heuristic evaluation function over states. This evaluation function is used by a one-step looka-head search procedure to find good solutions to new scheduling problems. We evaluate this approach on synthetic problems and on problems from a NASA space shuttle payload processing task. The evaluation function is trained on problems involving a small number of jobs and then tested on larger problems. The TD sched-uler performs better than the best known existing algorithm for this task|Zweben's iterative repair method based on simulated annealing. The results suggest that reinforcement learning can provide a new method for constructing high-performance scheduling systems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28471. 	[Title] Markov Decision Processes in Large State Spaces [Abstract] In this paper we propose a new framework for studying Markov decision processes (MDPs), based on ideas from statistical mechanics. The goal of learning in MDPs is to find a policy that yields the maximum expected return over time. In choosing policies, agents must therefore weigh the prospects of short-term versus long-term gains. We study a simple MDP in which the agent must constantly decide between exploratory jumps and local reward mining in state space. The number of policies to choose from grows exponentially with the size of the state space, N . We view the expected returns as defining an energy landscape over policy space. Methods from statistical mechanics are used to analyze this landscape in the thermodynamic limit N ! 1. We calculate the overall distribution of expected returns, as well as the distribution of returns for policies at a fixed Hamming distance from the optimal one. We briefly discuss the problem of learning optimal policies from empirical estimates of the expected return. As a first step, we relate our findings for the entropy to the limit of high-temperature learning. Numerical simulations support the theoretical results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28350. 	[Title] On the Convergence of Stochastic Iterative Dynamic Programming Algorithms [Abstract] Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD() algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD() and Q-learning belong. This report describes research done at the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for CBCL is provided in part by a grant from the NSF (ASC-9217041). Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense. The authors were supported by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-9013991 from the National Science Foundation, by grant N00014-90-J-1942 from the Office of Naval Research, and by NSF grant ECS-9216531 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is a NSF Presidential Young Investigator. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106112. 	[Title] Parameterized Heuristics for Intelligent Adaptive Network Routing in Large Communication Networks [Abstract] Parameterized heuristics offers an elegant and powerful theoretical framework for design and analysis of autonomous adaptive communication networks. Routing of messages in such networks presents a real-time instance of a multi-criterion optimization problem in a dynamic and uncertain environment. This paper describes a framework for heuristic routing in large networks. The effectiveness of the heuristic routing mechanism upon which Quo Vadis is based is described as part of a simulation study within a network with grid topology. A formal analysis of the underlying principles is presented through the incremental design of a set of heuristic decision functions that can be used to guide messages along a near-optimal (e.g., minimum delay) path in a large network. This paper carefully derives the properties of such heuristics under a set of simplifying assumptions about the network topology and load dynamics and identify the conditions under which they are guaranteed to route messages along an optimal path. The paper concludes with a discussion of the relevance of the theoretical results presented in the paper to the design of intelligent autonomous adaptive communication networks and an outline of some directions of future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #36802. 	[Title] Dynamic Belief Networks for Discrete Monitoring [Abstract] We describe the development of a monitoring system which uses sensor observation data about discrete events to construct dynamically a probabilistic model of the world. This model is a Bayesian network incorporating temporal aspects, which we call a Dynamic Belief Network; it is used to reason under uncertainty about both the causes and consequences of the events being monitored. The basic dynamic construction of the network is data-driven. However the model construction process combines sensor data about events with externally provided information about agents' behaviour, and knowledge already contained within the model, to control the size and complexity of the network. This means that both the network structure within a time interval, and the amount of history and detail maintained, can vary over time. We illustrate the system with the example domain of monitoring robot vehicles and people in a restricted dynamic environment using light-beam sensor data. In addition to presenting a generic network structure for monitoring domains, we describe the use of more complex network structures which address two specific monitoring problems, sensor validation and the Data Association Problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28227. 	[Title] Reinforcement Learning Methods for Continuous-Time Markov Decision Problems [Abstract] Semi-Markov Decision Problems are continuous time generalizations of discrete time Markov Decision Problems. A number of reinforcement learning algorithms have been developed recently for the solution of Markov Decision Problems, based on the ideas of asynchronous dynamic programming and stochastic approximation. Among these are TD(), Q-learning, and Real-time Dynamic Programming. After reviewing semi-Markov Decision Problems and Bellman's optimality equation in that context, we propose algorithms similar to those named above, adapted to the solution of semi-Markov Decision Problems. We demonstrate these algorithms by applying them to the problem of determining the optimal control for a simple queueing system. We conclude with a discussion of circumstances under which these algorithms may be usefully ap plied. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #341188. 	[Title] A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms [Abstract] Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the asynchronous convergence of a complex reinforcement-learning algorithm to be proven by verifying that a simpler synchronous algorithm converges. We illustrate the application of the theorem by analyzing the convergence of Q-learning, model-based reinforcement learning, Q-learning with multi-state updates, Q-learning for Markov games, and risk-sensitive reinforcement learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28485. 	[Title] The Efficient Learning of Multiple Task Sequences [Abstract] I present a modular network architecture and a learning algorithm based on incremental dynamic programming that allows a single learning agent to learn to solve multiple Markovian decision tasks (MDTs) with significant transfer of learning across the tasks. I consider a class of MDTs, called composite tasks, formed by temporally concatenating a number of simpler, elemental MDTs. The architecture is trained on a set of composite and elemental MDTs. The temporal structure of a composite task is assumed to be unknown and the architecture learns to produce a temporal decomposition. It is shown that under certain conditions the solution of a composite MDT can be constructed by computationally inexpensive modifications of the solutions of its constituent elemental MDTs. 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 #91975. 	[Title] Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards [Abstract] Markov decision processes (MDPs) with undis-counted rewards represent an important class of problems in decision and control. The goal of learning in these MDPs is to find a policy that yields the maximum expected return per unit time. In large state spaces, computing these averages directly is not feasible; instead, the agent must estimate them by stochastic exploration of the state space. In this case, longer exploration times enable more accurate estimates and more informed decision-making. The learning curve for an MDP measures how the agent's performance depends on the allowed exploration time, T . In this paper we analyze these learning curves for a simple control problem with undiscounted rewards. In particular, methods from statistical mechanics are used to calculate lower bounds on the agent's performance in the thermodynamic limit T ! 1, N ! 1, ff = T =N (finite), where T is the number of time steps allotted per policy evaluation and N is the size of the state space. In this limit, we provide a lower bound on the return of policies that appear optimal based on imperfect statistics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #590022. 	[Title] Fall Diagnosis using Dynamic Belief Networks [Abstract] The task is to monitor walking patterns and give early warning of falls using foot switch and mercury trigger sensors. We describe a dynamic belief network model for fall diagnosis which, given evidence from sensor observations, outputs beliefs about the current walking status and makes predictions regarding future falls. The model represents possible sensor error and is parametrised to allow customisation to the individual being monitored. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1481. 	[Title] (1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological [Abstract] Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture. 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 #6220. 	[Title] Issues in Using Function Approximation for Reinforcement Learning [Abstract] Reinforcement learning techniques address the problem of learning to select actions in unknown, dynamic environments. It is widely acknowledged that to be of use in complex domains, reinforcement learning techniques must be combined with generalizing function approximation methods such as artificial neural networks. Little, however, is understood about the theoretical properties of such combinations, and many researchers have encountered failures in practice. In this paper we identify a prime source of such failuresnamely, a systematic overestimation of utility values. Using Watkins' Q-Learning [18] as an example, we give a theoretical account of the phenomenon, deriving conditions under which one may expected it to cause learning to fail. Employing some of the most popular function approximators, we present experimental results which support the theoretical findings. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #193742. 	[Title] Q-Learning with Hidden-Unit Restarting [Abstract] Platt's resource-allocation network (RAN) (Platt, 1991a, 1991b) is modified for a reinforcement-learning paradigm and to "restart" existing hidden units rather than adding new units. After restarting, units continue to learn via back-propagation. The resulting restart algorithm is tested in a Q-learning network that learns to solve an inverted pendulum problem. Solutions are found faster on average with the restart algorithm than without it. 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 #6224. 	[Title] Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions [Abstract] Consider a given value function on states of a Markov decision problem, as might result from applying a reinforcement learning algorithm. Unless this value function equals the corresponding optimal value function, at some states there will be a discrepancy, which is natural to call the Bellman residual, between what the value function specifies at that state and what is obtained by a one-step lookahead along the seemingly best action at that state using the given value function to evaluate all succeeding states. This paper derives a tight bound on how far from optimal the discounted return for a greedy policy based on the given value function will be as a function of the maximum norm magnitude of this Bellman residual. A corresponding result is also obtained for value functions defined on state-action pairs, as are used in Q-learning. One significant application of these results is to problems where a function approximator is used to learn a value function, with training of the approxi-mator based on trying to minimize the Bellman residual across states or state-action pairs. When control is based on the use of the resulting value function, this result provides a link between how well the objectives of function approximator training are met and the quality of the resulting control. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #96847. 	[Title] Generality versus Size in Genetic Programming [Abstract] Genetic Programming (GP) uses variable size representations as programs. Size becomes an important and interesting emergent property of the structures evolved by GP. The size of programs can be both a controlling and a controlled factor in GP search. Size influences the efficiency of the search process and is related to the generality of solutions. This paper analyzes the size and generality issues in standard GP and GP using subroutines and addresses the question whether such an analysis can help control the search process. We relate the size, generalization and modularity issues for programs evolved to control an agent in a dynamic and non-deterministic environment, as exemplified by the Pac-Man game. 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 #1103315. 	[Title] Value Function Based Production Scheduling [Abstract] Production scheduling, the problem of sequentially configuring a factory to meet forecasted demands, is a critical problem throughout the manufacturing industry. The requirement of maintaining product inventories in the face of unpredictable demand and stochastic factory output makes standard scheduling models, such as job-shop, inadequate. Currently applied algorithms, such as simulated annealing and constraint propagation, must employ ad-hoc methods such as frequent replanning to cope with uncertainty. In this paper, we describe a Markov Decision Process (MDP) formulation of production scheduling which captures stochasticity in both production and demands. The solution to this MDP is a value function which can be used to generate optimal scheduling decisions online. A simple example illustrates the theoretical superiority of this approach over replanning-based methods. We then describe an industrial application and two reinforcement learning methods for generating an approximate value function on this domain. Our results demonstrate that in both deterministic and noisy scenarios, value function approx imation is an effective technique. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18777. 	[Title] A Tutorial on Learning With Bayesian Networks [Abstract] Technical Report MSR-TR-95-06 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #136665. 	[Title] The Influence of Domain Properties on the Performance of Real-Time Search Algorithms [Abstract] This research is sponsored by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #23258. 	[Title] Exploration and Model Building in Mobile Robot Domains [Abstract] I present first results on COLUMBUS, an autonomous mobile robot. COLUMBUS operates in initially unknown, structured environments. Its task is to explore and model the environment efficiently while avoiding collisions with obstacles. COLUMBUS uses an instance-based learning technique for modeling its environment. Real-world experiences are generalized via two artificial neural networks that encode the characteristics of the robot's sensors, as well as the characteristics of typical environments the robot is assumed to face. Once trained, these networks allow for knowledge transfer across different environments the robot will face over its lifetime. COLUMBUS' models represent both the expected reward and the confidence in these expectations. Exploration is achieved by navigating to low confidence regions. An efficient dynamic programming method is employed in background to find minimal-cost paths that, executed by the robot, maximize exploration. COLUMBUS operates in real-time. It has been operating successfully in an office building environment for periods up to hours. 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 #589923. 	[Title] A case study in dynamic belief networks: monitoring walking, fall prediction and detection [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #64484. 	[Title] Learning from an Automated Training Agent [Abstract] A learning agent employing reinforcement learning is hindered because it only receives the critic's sparse and weakly informative training information. We present an approach in which an automated training agent may also provide occasional instruction to the learner in the form of actions for the learner to perform. The learner has access to both the critic's feedback and the trainer's instruction. In the experiments, we vary the level of the trainer's interaction with the learner, from allowing the trainer to instruct the learner at almost every time step, to not allowing the trainer to respond at all. We also vary a parameter that controls how the learner incorporates the trainer's actions. The results show significant reductions in the average number of training trials necessary to learn to perform the task. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106406. 	[Title] Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in [Abstract] In cellular telephone systems, an important problem is to dynamically allocate the communication resource (channels) so as to maximize service in a stochastic caller environment. This problem is naturally formulated as a dynamic programming problem and we use a reinforcement learning (RL) method to find dynamic channel allocation policies that are better than previous heuristic solutions. The policies obtained perform well for a broad variety of call traffic patterns. We present results on a large cellular system In cellular communication systems, an important problem is to allocate the communication resource (bandwidth) so as to maximize the service provided to a set of mobile callers whose demand for service changes stochastically. A given geographical area is divided into mutually disjoint cells, and each cell serves the calls that are within its boundaries (see Figure 1a). The total system bandwidth is divided into channels, with each channel centered around a frequency. Each channel can be used simultaneously at different cells, provided these cells are sufficiently separated spatially, so that there is no interference between them. The minimum separation distance between simultaneous reuse of the same channel is called the channel reuse constraint . When a call requests service in a given cell either a free channel (one that does not violate the channel reuse constraint) may be assigned to the call, or else the call is blocked from the system; this will happen if no free channel can be found. Also, when a mobile caller crosses from one cell to another, the call is "handed off" to the cell of entry; that is, a new free channel is provided to the call at the new cell. If no such channel is available, the call must be dropped/disconnected from the system. One objective of a channel allocation policy is to allocate the available channels to calls so that the number of blocked calls is minimized. An additional objective is to minimize the number of calls that are dropped when they are handed off to a busy cell. These two objectives must be weighted appropriately to reflect their relative importance, since dropping existing calls is generally more undesirable than blocking new calls. with approximately 70 49 states. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39403. 	[Title] Auto-exploratory Average Reward Reinforcement Learning [Abstract] We introduce a model-based average reward Reinforcement Learning method called H-learning and compare it with its discounted counterpart, Adaptive Real-Time Dynamic Programming, in a simulated robot scheduling task. We also introduce an extension to H-learning, which automatically explores the unexplored parts of the state space, while always choosing greedy actions with respect to the current value function. We show that this "Auto-exploratory H-learning" performs better than the original H-learning under previously studied exploration methods such as random, recency-based, or counter-based exploration. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #134128. 	[Title] Learning To Play the Game of Chess [Abstract] This paper presents NeuroChess, a program which learns to play chess from the final outcome of games. NeuroChess learns chess board evaluation functions, represented by artificial neural networks. It integrates inductive neural network learning, temporal differencing, and a variant of explanation-based learning. Performance results illustrate some of the strengths and weaknesses of this approach. 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 #1105394. 	[Title] An Introspection Approach to Querying a Trainer [Abstract] Technical Report 96-13 January 22, 1996 Abstract This paper introduces the Introspection Approach, a method by which a learning agent employing reinforcement learning can decide when to ask a training agent for instruction. When using our approach, we find that the same number of trainer's responses produced significantly faster learners than by having the learner ask for aid randomly. Guidance received via our approach is more informative than random guidance. Thus, we can reduce the interaction that the training agent has with the learning agent without reducing the speed with which the learner develops its policy. In fact, by being intelligent about when the learner asks for help, we can even increase the learning speed for the same level of trainer interaction. 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.

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 #1117942. 	[Title] Multi-criteria reinforcement learning [Abstract] We consider multi-criteria sequential decision making problems, where the criteria are ordered according to their importance. Structural properties of these problems are touched and reinforcement learning algorithms, which learn asymptotically optimal decisions, are derived. Computer experiments confirm the theoretical results and provide further insight in the learning processes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118332. 	[Title] June 1994 T o app ear in Neural Computation A Coun terexample to T emp [Abstract] Sutton's TD( ) metho d aims to provide a represen tation of the cost function in an absorbing Mark ov chain with transition costs. A simple example is given where the represen tation obtained dep ends on . For = 1 the represen tation is optimal with resp ect to a least squares error criterion, but as decreases towards 0 the represen tation becomes progressiv ely worse and, in some cases, very poor. The example suggests a need to understand better the circumstances under which TD(0) and Q-learning obtain satisfactory neural net work-based compact represen tations of the cost function. A variation of TD(0) is also prop osed, which performs b etter on the example. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #189566. 	[Title] Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions [Abstract] Modeling techniques developed recently in the AI and uncertain reasoning communities permit significantly more flexible specifications of probabilistic knowledge. Specifically, graphical decision-modeling formalisms|belief networks, influence diagrams, and their variants|provide compact representation of probabilistic relationships, and support inference algorithms that automatically exploit the dependence structure in such models [1, 3, 4]. These advances have brought on a resurgence of interest in computational decision systems based on normative theories of belief and preference. However, graphical decision-modeling languages are still quite limited for purposes of knowledge representation because, while they can describe the relationships among particular event instances, they cannot capture general knowledge about probabilistic relationships across classes of events. The inability to capture general knowledge is a serious impediment for those AI tasks in which the relevant factors of a decision problem cannot be enumerated in advance. A graphical decision model encodes a particular set of probabilistic dependencies, a predefined set of decision alternatives, and a specific mathematical form for a utility function. Given a properly specified model, there exist relatively efficient algorithms for calculating posterior probabilities and optimal decision policies. A range of similar cases may be handled by parametric variations of the original model. However, if the structure of dependencies, the set of available alternatives, or the form of utility function changes from situation to situation, then a fixed network representation is no longer adequate. An ideal computational decision system would possess general, broad knowledge of a domain, but would have the ability to reason about the particular circumstances of any given decision problem within the domain. One obvious approach|which we call call knowledge-based model construction (KBMC)|is to generate a decision model dynamically at run-time, based on the problem description and information received thus far. Model construction consists of selection, instantiation, and assembly of causal and associational relationships from a broad knowledge base of general relationships among domain concepts. For example, suppose we wish to develop a system to recommend appropriate actions for maintaining a computer network. The natural graphical decision model would include chance Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28287. 	[Title] Robot Shaping: Developing Situated Agents through Learning [Abstract] Learning plays a vital role in the development of situated agents. In this paper, we explore the use of reinforcement learning to "shape" a robot to perform a predefined target behavior. We connect both simulated and real robots to A LECSYS, a parallel implementation of a learning classifier system with an extended genetic algorithm. After classifying different kinds of Animat-like behaviors, we explore the effects on learning of different types of agent's architecture (monolithic, flat and hierarchical) and of training strategies. In particular, hierarchical architecture requires the agent to learn how to coordinate basic learned responses. We show that the best results are achieved when both the agent's architecture and the training strategy match the structure of the behavior pattern to be learned. We report the results of a number of experiments carried out both in simulated and in real environments, and show that the results of simulations carry smoothly to real robots. While most of our experiments deal with simple reactive behavior, in one of them we demonstrate the use of a simple and general memory mechanism. As a whole, our experimental activity demonstrates that classifier systems with genetic algorithms can be practically employed to develop autonomous agents. Please summarise all the given information.

Paper #130 has the title `Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function  
` and the abstract `Almost all the work in Average-reward Re- inforcement Learning (ARL) so far has focused on table-based methods which do not scale to domains with large state spaces. In this paper, we propose two extensions to a model-based ARL method called H-learning to address the scale-up problem. We extend H-learning to learn action models and reward functions in the form of Bayesian networks, and approximate its value function using local linear regression. We test our algorithms on several scheduling tasks for a simulated Automatic Guided Vehicle (AGV) and show that they are effective in significantly reducing the space requirement of H-learning and making it converge faster. To the best of our knowledge, our results are the first in apply 
`.
Question: Which category should Paper #130 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #130 has the title `Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function  
` and the abstract `Almost all the work in Average-reward Re- inforcement Learning (ARL) so far has focused on table-based methods which do not scale to domains with large state spaces. In this paper, we propose two extensions to a model-based ARL method called H-learning to address the scale-up problem. We extend H-learning to learn action models and reward functions in the form of Bayesian networks, and approximate its value function using local linear regression. We test our algorithms on several scheduling tasks for a simulated Automatic Guided Vehicle (AGV) and show that they are effective in significantly reducing the space requirement of H-learning and making it converge faster. To the best of our knowledge, our results are the first in apply 
`.
Question: Which category should Paper #130 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