Above are papers related to the following paper: [Paper_ID] Paper #95435. 	[Title] Incremental methods for computing bounds in partially observable Markov decision processes [Abstract] Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect observabil-ity. The control problem is formulated as a dynamic optimization problem with a value function combining costs or rewards from multiple steps. In this paper we propose, analyse and test various incremental methods for computing bounds on the value function for control problems with infinite discounted horizon criteria. The methods described and tested include novel incremental versions of grid-based linear interpolation method and simple lower bound method with Sondik's updates. Both of these can work with arbitrary points of the belief space and can be enhanced by various heuristic point selection strategies. Also introduced is a new method for computing an initial upper bound the fast informed bound method. This method is able to improve significantly on the standard and commonly used upper bound computed by the MDP-based method. The quality of resulting bounds are tested on a maze navigation problem with 20 states, 6 actions and 8 observations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #36131. 	[Title] Adaptive probabilistic networks [Abstract] Belief networks (or probabilistic networks) and neural networks are two forms of network representations that have been used in the development of intelligent systems in the field of artificial intelligence. Belief networks provide a concise representation of general probability distributions over a set of random variables, and facilitate exact calculation of the impact of evidence on propositions of interest. Neural networks, which represent parameterized algebraic combinations of nonlinear activation functions, have found widespread use as models of real neural systems and as function approximators because of their amenability to simple training algorithms. Furthermore, the simple, local nature of most neural network training algorithms provides a certain biological plausibility and allows for a massively parallel implementation. In this paper, we show that similar local learning algorithms can be derived for belief networks, and that these learning algorithms can operate using only information that is directly available from the normal, inferential processes of the networks. This removes the main obstacle preventing belief networks from competing with neural networks on the above-mentioned tasks. The precise, local, probabilistic interpretation of belief networks also allows them to be partially or wholly constructed by humans; allows the results of learning to be easily understood; and allows them to contribute to rational decision-making in a well-defined way. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107140. 	[Title] A Formal Framework for Speedup Learning from Problems and Solutions [Abstract] Speedup learning seeks to improve the computational efficiency of problem solving with experience. In this paper, we develop a formal framework for learning efficient problem solving from random problems and their solutions. We apply this framework to two different representations of learned knowledge, namely control rules and macro-operators, and prove theorems that identify sufficient conditions for learning in each representation. Our proofs are constructive in that they are accompanied with learning algorithms. Our framework captures both empirical and explanation-based speedup learning in a unified fashion. We illustrate our framework with implementations in two domains: symbolic integration and Eight Puzzle. This work integrates many strands of experimental and theoretical work in machine learning, including empirical learning of control rules, macro-operator learning, 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 #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 #32872. 	[Title] Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation [Abstract] Discrete Bayesian models have been used to model uncertainty for mobile-robot navigation, but the question of how actions should be chosen remains largely unexplored. This paper presents the optimal solution to the problem, formulated as a partially observable Markov decision process. Since solving for the optimal control policy is intractable, in general, it goes on to explore a variety of heuristic control strategies. The control strategies are compared experimentally, both in simulation and in runs on a robot. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13193. 	[Title] Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems [Abstract] Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due to successes in the theoretical analysis of their behavior in Markov environments. If the Markov assumption is removed, however, neither generally the algorithms nor the analyses continue to be usable. We propose and analyze a new learning algorithm to solve a certain class of non-Markov decision problems. Our algorithm applies to problems in which the environment is Markov, but the learner has restricted access to state information. The algorithm involves a Monte-Carlo policy evaluation combined with a policy improvement method that is similar to that of Markov decision problems and is guaranteed to converge to a local maximum. The algorithm operates in the space of stochastic policies, a space which can yield a policy that performs considerably better than any deterministic policy. Although the space of stochastic policies is continuous|even for a discrete action space|our algorithm is computationally tractable. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153262. 	[Title] Rationality and Intelligence [Abstract] The long-term goal of our field is the creation and understanding of intelligence. Productive research in AI, both practical and theoretical, benefits from a notion of intelligence that is precise enough to allow the cumulative development of robust systems and general results. The concept of rational agency has long been considered a leading candidate to fulfill this role. This paper outlines a gradual evolution in the formal conception of rationality that brings it closer to our informal conception of intelligence and simultaneously reduces the gap between theory and practice. Some directions for future research are indicated. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17201. 	[Title] Learning policies for partially observable environments: Scaling up [Abstract] Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor feedback. While the study of pomdp's is motivated by a need to address realistic problems, existing techniques for finding optimal behavior do not appear to scale well and have been unable to find satisfactory policies for problems with more than a dozen states. After a brief review of pomdp's, this paper discusses several simple solution methods and shows that all are capable of finding near-optimal policies for a selection of extremely small pomdp's taken from the learning literature. In contrast, we show that none are able to solve a slightly larger and noisier problem based on robot navigation. We find that a combination of two novel approaches performs well on these problems and suggest methods for scaling to even larger and more complicated domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17208. 	[Title] Approximating Optimal Policies for Partially Observable Stochastic Domains [Abstract] The problem of making optimal decisions in uncertain conditions is central to Artificial Intelligence. If the state of the world is known at all times, the world can be modeled as a Markov Decision Process (MDP). MDPs have been studied extensively and many methods are known for determining optimal courses of action, or policies. The more realistic case where state information is only partially observable, Partially Observable Markov Decision Processes (POMDPs), have received much less attention. The best exact algorithms for these problems can be very inefficient in both space and time. We introduce Smooth Partially Observable Value Approximation (SPOVA), a new approximation method that can quickly yield good approximations which can improve over time. This method can be combined with reinforcement learning methods, a combination that was very effective in our test cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152633. 	[Title]  [Abstract] MOU 130: Feasibility study of fully autonomous vehicles using decision-theoretic control Final Report Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1126012. 	[Title] Some Experiments with Real-time Decision Algorithms [Abstract] Real-time Decision algorithms are a class of incremental resource-bounded [Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence diagrams. We present a test domain for real-time decision algorithms, and the results of experiments with several Real-time Decision Algorithms in this domain. The results demonstrate high performance for two algorithms, a decision-evaluation variant of Incremental Probabilisitic Inference [DAmbrosio, 93] and a variant of an algorithm suggested by Goldszmidt, [Goldszmidt, 95], PK-reduced. We discuss the implications of these experimental results and explore the broader applicability of these algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #184157. 	[Title] Efficient dynamic-programming updates in partially observable Markov decision processes [Abstract] We examine the problem of performing exact dynamic-programming updates in partially observable Markov decision processes (pomdps) from a computational complexity viewpoint. Dynamic-programming updates are a crucial operation in a wide range of pomdp solution methods and we find that it is intractable to perform these updates on piecewise-linear convex value functions for general pomdps. We offer a new algorithm, called the witness algorithm, which can compute updated value functions efficiently on a restricted class of pomdps in which the number of linear facets is not too great. We compare the witness algorithm to existing algorithms analytically and empirically and find that it is the fastest algorithm over a wide range of pomdp sizes. Please summarise all the given information.

Paper #95435 has the title `Incremental methods for computing bounds in partially observable Markov decision processes  
` and the abstract `Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect observabil-ity. The control problem is formulated as a dynamic optimization problem with a value function combining costs or rewards from multiple steps. In this paper we propose, analyse and test various incremental methods for computing bounds on the value function for control problems with infinite discounted horizon criteria. The methods described and tested include novel incremental versions of grid-based linear interpolation method and simple lower bound method with Sondik's updates. Both of these can work with arbitrary points of the belief space and can be enhanced by various heuristic point selection strategies. Also introduced is a new method for computing an initial upper bound the fast informed bound method. This method is able to improve significantly on the standard and commonly used upper bound computed by the MDP-based method. The quality of resulting bounds are tested on a maze navigation problem with 20 states, 6 actions and 8 observations. 
`.
Question: Which category should Paper #95435 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #95435 has the title `Incremental methods for computing bounds in partially observable Markov decision processes  
` and the abstract `Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect observabil-ity. The control problem is formulated as a dynamic optimization problem with a value function combining costs or rewards from multiple steps. In this paper we propose, analyse and test various incremental methods for computing bounds on the value function for control problems with infinite discounted horizon criteria. The methods described and tested include novel incremental versions of grid-based linear interpolation method and simple lower bound method with Sondik's updates. Both of these can work with arbitrary points of the belief space and can be enhanced by various heuristic point selection strategies. Also introduced is a new method for computing an initial upper bound the fast informed bound method. This method is able to improve significantly on the standard and commonly used upper bound computed by the MDP-based method. The quality of resulting bounds are tested on a maze navigation problem with 20 states, 6 actions and 8 observations. 
`.
Question: Which category should Paper #95435 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