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 #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 #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 #189721. 	[Title] Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks [Abstract] We show an alternative way of representing a Bayesian belief network by sensitivities and probability distributions. This representation is equivalent to the traditional representation by conditional probabilities, but makes dependencies between nodes apparent and intuitively easy to understand. We also propose a QR matrix representation for the sensitivities and/or conditional probabilities which is more efficient, in both memory requirements and computational speed, than the traditional representation for computer-based implementations of probabilistic inference. We use sensitivities to show that for a certain class of binary networks, the computation time for approximate probabilistic inference with any positive upper bound on the error of the result is independent of the size of the network. Finally, as an alternative to traditional algorithms that use conditional probabilities, we describe an exact algorithm for probabilistic inference that uses the QR-representation for sensitivities and updates probability distributions of nodes in a network according to messages from the neigh bors. 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 #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 #1123756. 	[Title] Case-Based Probability Factoring in Bayesian Belief Networks [Abstract] Bayesian network inference can be formulated as a combinatorial optimization problem, concerning in the computation of an optimal factoring for the distribution represented in the net. Since the determination of an optimal factoring is a computationally hard problem, heuristic greedy strategies able to find approximations of the optimal factoring are usually adopted. In the present paper we investigate an alternative approach based on a combination of genetic algorithms (GA) and case-based reasoning (CBR). We show how the use of genetic algorithms can improve the quality of the computed factoring in case a static strategy is used (as for the MPE computation), while the combination of GA and CBR can still provide advantages in the case of dynamic strategies. Some preliminary results on different kinds of nets are then reported. 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 #124952. 	[Title] Efficient Inference in Bayes Networks As A Combinatorial Optimization Problem [Abstract] A number of exact algorithms have been developed to perform probabilistic inference in Bayesian belief networks in recent years. The techniques used in these algorithms are closely related to network structures and some of them are not easy to understand and implement. In this paper, we consider the problem from the combinatorial optimization point of view and state that efficient probabilistic inference in a belief network is a problem of finding an optimal factoring given a set of probability distributions. From this viewpoint, previously developed algorithms can be seen as alternate factoring strategies. In this paper, we define a combinatorial optimization problem, the optimal factoring problem, and discuss application of this problem in belief networks. We show that optimal factoring provides insight into the key elements of efficient probabilistic inference, and demonstrate simple, easily implemented algorithms with excellent performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1126011. 	[Title] Algebraic Techniques for Efficient Inference in Bayesian Networks [Abstract] A number of exact algorithms have been developed to perform probabilistic inference in Bayesian belief networks in recent years. These algorithms use graph-theoretic techniques to analyze and exploit network topology. In this paper, we examine the problem of efficient probabilistic inference in a belief network as a combinatorial optimization problem, that of finding an optimal factoring given an algebraic expression over a set of probability distributions. We define a combinatorial optimization problem, the optimal factoring problem, and discuss application of this problem in belief networks. We show that optimal factoring provides insight into the key elements of efficient probabilistic inference, and present simple, easily implemented algorithms with excellent performance. We also show how use of an algebraic perspective permits significant extension to the belief net representation. 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.

Above are papers related to the following paper: [Paper_ID] Paper #1119742. 	[Title] Interpretation of Complex Scenes Using Bayesian Networks [Abstract] In most object recognition systems, interactions between objects in a scene are ignored and the best interpretation is considered to be the set of hypothesized objects that matches the greatest number of image features. We show how image interpretation can be cast as the problem of finding the most probable explanation (MPE) in a Bayesian network that models both visual and physical object interactions. The problem of how to determine exact conditional probabilities for the network is shown to be unimportant, since the goal is to find the most probable configuration of objects, not to calculate absolute probabilities. We furthermore show that evaluating configurations by feature counting is equivalent to calculating the joint probability of the configuration using a restricted Bayesian network, and derive the assumptions about probabilities necessary to make a Bayesian formulation reasonable. Please summarise all the given information.

Paper #1126012 has the title `Some Experiments with Real-time Decision Algorithms  
` and the 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.
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Question: Which category should Paper #1126012 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1126012 has the title `Some Experiments with Real-time Decision Algorithms  
` and the 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.
`.
Question: Which category should Paper #1126012 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: Probabilistic_Methods