Above are papers related to the following paper: [Paper_ID] Paper #1129208. 	[Title] Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and [Abstract] that is based on Angluin's L fl algorithm. The algorithm maintains a model consistent with its past examples. When a new counterexample arrives it tries to extend the model in a minimal fashion. We conducted a set of experiments where random automata that represent different strategies were generated, and the algorithm tried to learn them based on prefix-closed samples of their behavior. The algorithm managed to learn very compact models that agree with the samples. The size of the sample had a small effect on the size of the model. The experimental results suggest that for random prefix-closed samples the algorithm behaves well. However, following Angluin's result on the difficulty of learning almost uniform complete samples [ An-gluin, 1978 ] , it is obvious that our algorithm does not solve the complexity issue of inferring a DFA from a general prefix-closed sample. We are currently looking for classes of prefix-closed samples in which US-L* behaves well. [ Carmel and Markovitch, 1994 ] D. Carmel and S. Markovitch. The M* algorithm: Incorporating opponent models into adversary search. Technical Report CIS report 9402, Technion, March 1994. [ Carmel and Markovitch, 1995 ] D. Carmel and S. Markovitch. Unsupervised learning of finite automata: A practical approach. Technical Report CIS report 9504, Technion, March 1995. [ Shoham and Tennenholtz, 1994 ] Y. Shoham and M. Tennenholtz. Co-Learning and the evolution of social activity. Technical Report STAN-CS-TR-94-1511, Stanford Univrsity, Department of Computer Science, 1994. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1272. 	[Title] Learning a set of primitive actions with an Induction of decision trees. Machine Learning, 1(1):81-106, [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6917. 	[Title] Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs [Abstract] covering has been formalized and used extensively. In this work, the divide-and-conquer technique is formalized as well and compared to the covering technique in a logic programming framework. Covering works by repeatedly specializing an overly general hypothesis, on each iteration focusing on finding a clause with a high coverage of positive examples. Divide-and-conquer works by specializing an overly general hypothesis once, focusing on discriminating positive from negative examples. Experimental results are presented demonstrating that there are cases when more accurate hypotheses can be found by divide-and-conquer than by covering. Moreover, since covering considers the same alternatives repeatedly it tends to be less efficient than divide-and-conquer, which never considers the same alternative twice. On the other hand, covering searches a larger hypothesis space, which may result in that more compact hypotheses are found by this technique than by divide-and-conquer. Furthermore, divide-and-conquer is, in contrast to covering, not applicable to learn ing recursive definitions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105033. 	[Title] Learning to take risks [Abstract] Agents that learn about other agents and can exploit this information possess a distinct advantage in competitive situations. Games provide stylized adversarial environments to study agent learning strategies. Researchers have developed game playing programs that learn to play better from experience. We have developed a learning program that does not learn to play better, but learns to identify and exploit the weaknesses of a particular opponent by repeatedly playing it over several games. We propose a scheme for learning opponent action probabilities and a utility maximization framework that exploits this learned opponent model. We show that the proposed expected utility maximization strategy generalizes the traditional maximin strategy, and allows players to benefit by taking calculated risks that are avoided by the max-imin strategy. Experiments in the popular board game of Connect-4 show that a learning player consistently outperforms a non-learning player when pitted against another automated player using a weaker heuristic. Though our proposed mechanism does not improve the skill level of a computer player, it does improve its ability to play more effectively against a weaker opponent. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6923. 	[Title] Inductive Constraint Logic [Abstract] A novel approach to learning first order logic formulae from positive and negative examples is presented. Whereas present inductive logic programming systems employ examples as true and false ground facts (or clauses), we view examples as interpretations which are true or false for the target theory. This viewpoint allows to reconcile the inductive logic programming paradigm with classical attribute value learning in the sense that the latter is a special case of the former. Because of this property, we are able to adapt AQ and CN2 type algorithms in order to enable learning of full first order formulae. However, whereas classical learning techniques have concentrated on concept representations in disjunctive normal form, we will use a clausal representation, which corresponds to a conjuctive normal form where each conjunct forms a constraint on positive examples. This representation duality reverses also the role of positive and negative examples, both in the heuristics and in the algorithm. The resulting theory is incorporated in a system named ICL (Inductive Constraint Logic). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135125. 	[Title] Constructing Fuzzy Graphs from Examples [Abstract] Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an efficient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs offer quick insights into the structure of the example data, that is, the underlying model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #552469. 	[Title] On Learning More Concepts [Abstract] The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any Boolean concept learning algorithm and describes two algorithms|Multi-Balls and Large-Ball|whose coverage approaches this upper bound. Experimental measurement of the coverage of the ID3 and FRINGE algorithms shows that their coverage is far below this bound. Further analysis of Large-Ball shows that although it learns many concepts, these do not seem to be very interesting concepts. Hence, coverage maximization alone does not appear to yield practically-useful learning algorithms. The paper concludes with a definition of coverage within a bias, which suggests a way that coverage maximization could be applied to strengthen weak preference biases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #520471. 	[Title] The Arguments of Newly Invented Predicates in ILP [Abstract] In this paper we investigate the problem of choosing arguments for a new predicate. We identify the relevant terms to be considered as arguments, and propose methods to choose among them based on propositional minimisation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123867. 	[Title] A Study of Maximal-Coverage Learning Algorithms [Abstract] The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any Boolean concept learning algorithm and describes two algorithms|Multi-Balls and Large-Ball|whose coverage approaches this upper bound. Experimental measurement of the coverage of the ID3 and FRINGE algorithms shows that their coverage is far below this bound. Further analysis of Large-Ball shows that although it learns many concepts, these do not seem to be very interesting concepts. Hence, coverage maximization alone does not appear to yield practically-useful learning algorithms. The paper concludes with a definition of coverage within a bias, which suggests a way that coverage maximization could be applied to strengthen weak preference biases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102625. 	[Title] A systematic description of greedy optimisation algorithms for cost sensitive generalisation [Abstract] This paper defines a class of problems involving combinations of induction and (cost) optimisation. A framework is presented that systematically describes problems that involve construction of decision trees or rules, optimising accuracy as well as measurement- and misclassification costs. It does not present any new algorithms but shows how this framework can be used to configure greedy algorithms for constructing such trees or rules. The framework covers a number of existing algorithms. Moreover, the framework can also be used to define algorithm configurations with new functionalities, as expressed in their evaluation functions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #560936. 	[Title] Monte Carlo Comparison of Non-hierarchical Unsupervised Classifiers [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28456. 	[Title] A Competitive Approach to Game Learning [Abstract] Machine learning of game strategies has often depended on competitive methods that continually develop new strategies capable of defeating previous ones. We use a very inclusive definition of game and consider a framework within which a competitive algorithm makes repeated use of a strategy learning component that can learn strategies which defeat a given set of opponents. We describe game learning in terms of sets H and X of first and second player strategies, and connect the model with more familiar models of concept learning. We show the importance of the ideas of teaching set [20] and specification number [19] k in this new context. The performance of several competitive algorithms is investigated, using both worst-case and randomized strategy learning algorithms. Our central result (Theorem 4) is a competitive algorithm that solves games in a total number of strategies polynomial in lg(jHj), lg(jX j), and k. Its use is demonstrated, including an application in concept learning with a new kind of counterexample oracle. We conclude with a complexity analysis of game learning, and list a number of new questions arising from this work. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #591016. 	[Title] First Order Regression: Applications in Real-World Domains [Abstract] A first order regression algorithm capable of handling real-valued (continuous) variables is introduced and some of its applications are presented. Regressional learning assumes real-valued class and discrete or real-valued variables. The algorithm combines regressional learning with standard ILP concepts, such as first order concept description and background knowledge. A clause is generated by successively refining the initial clause by adding literals of the form A = v for the discrete attributes, A v and A v for the real-valued attributes, and background knowledge literals to the clause body. The algorithm employs a covering approach (beam search), a heuristic impurity function, and stopping criteria based on local improvement, minimum number of examples, maximum clause length, minimum local improvement, minimum description length, allowed error, and variable depth. An outline of the algorithm and the results of the system's application in some artificial and real-world domains are presented. The real-world domains comprise: modelling of the water behavior in a surge tank, modelling of the workpiece roughness in a steel grinding process and modelling of the operator's behavior during the process of electrical discharge machining. Special emphasis is given to the evaluation of obtained models by domain experts and their comments on the aspects of practical use of the induced knowledge. The results obtained during the knowledge acquisition process show several important guidelines for knowledge acquisition, concerning mainly the process of interaction with domain experts, exposing primarily the importance of comprehensibility of the induced knowledge. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #284414. 	[Title] BRACE: A Paradigm For the Discretization of Continuously Valued Data [Abstract] Discretization of continuously valued data is a useful and necessary tool because many learning paradigms assume nominal data. A list of objectives for efficient and effective discretization is presented. A paradigm called BRACE (Boundary Ranking And Classification Evaluation) that attempts to meet the objectives is presented along with an algorithm that follows the paradigm. The paradigm meets many of the objectives, with potential for extension to meet the remainder. Empirical results have been promising. For these reasons BRACE has potential as an effective and efficient method for discretization of continuously valued data. A further advantage of BRACE is that it is general enough to be extended to other types of clustering/unsupervised learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #30895. 	[Title] Induction of Oblique Decision Trees [Abstract] This paper introduces a randomized technique for partitioning examples using oblique hyperplanes. Standard decision tree techniques, such as ID3 and its descendants, partition a set of points with axis-parallel hyper-planes. Our method, by contrast, attempts to find hyperplanes at any orientation. The purpose of this more general technique is to find smaller but equally accurate decision trees than those created by other methods. We have tested our algorithm on both real and simulated data, and found that in some cases it produces surprisingly small trees without losing predictive accuracy. Small trees allow us, in turn, to obtain simple qualitative descriptions of each problem domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #30901. 	[Title] Lookahead and Pathology in Decision Tree Induction [Abstract] The standard approach to decision tree induction is a top-down, greedy algorithm that makes locally optimal, irrevocable decisions at each node of a tree. In this paper, we empirically study an alternative approach, in which the algorithms use one-level lookahead to decide what test to use at a node. We systematically compare, using a very large number of real and artificial data sets, the quality of decision trees induced by the greedy approach to that of trees induced using lookahead. The main observations from our experiments are: (i) the greedy approach consistently produced trees that were just as accurate as trees produced with the much more expensive lookahead step; and (ii) we observed many instances of pathology, i.e., lookahead produced trees that were both larger and less accurate than trees produced without it. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18615. 	[Title] Learning Trees and Rules with Set-valued Features [Abstract] In most learning systems examples are represented as fixed-length "feature vectors", the components of which are either real numbers or nominal values. We propose an extension of the feature-vector representation that allows the value of a feature to be a set of strings; for instance, to represent a small white and black dog with the nominal features size and species and the set-valued feature color, one might use a feature vector with size=small, species=canis-familiaris and color=fwhite,blackg. Since we make no assumptions about the number of possible set elements, this extension of the traditional feature-vector representation is closely connected to Blum's "infinite attribute" representation. We argue that many decision tree and rule learning algorithms can be easily extended to set-valued features. We also show by example that many real-world learning problems can be efficiently and naturally represented with set-valued features; in particular, text categorization problems and problems that arise in propositionalizing first-order representations lend themselves to set-valued features. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #85688. 	[Title] Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings [Abstract] addressed in KBANN (which translates a theory into a neural-net, refines it using backpropagation, and then retranslates the result back into rules) by adding extra hidden units and connections to the initial network; however, this would require predetermining the num In this paper, we have presented constructive induction techniques recently added to the EITHER theory refinement system. Intermediate concept utilization employs existing rules in the theory to derive higher-level features for use in induction. Intermediate concept creation employs inverse resolution to introduce new intermediate concepts in order to fill gaps in a theory than span multiple levels. These revisions allow EITHER to make use of imperfect domain theories in the ways typical of previous work in both constructive induction and theory refinement. As a result, EITHER is able to handle a wider range of theory imperfections than any other existing theory refinement system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28473. 	[Title] Adaptive Load Balancing: A Study in Multi-Agent Learning [Abstract] We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay between basic adaptive behavior parameters and their effect on system efficiency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, we show that naive use of communication may not improve, and might even harm system efficiency. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103162. 	[Title] Team-Partitioned, Opaque-Transition Reinforcement Learning [Abstract] In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of using action-dependent features to generalize the state space. In our work, we use a learned action-dependent feature space. TPOT-RL is an effective technique to allow a team of agents to learn to cooperate towards the achievement of a specific goal. It is an adaptation of traditional RL methods that is applicable in complex, non-Markovian, multi-agent domains with large state spaces and limited training opportunities. Multi-agent scenarios are opaque-transition, as team members are not always in full communication with one another and adversaries may affect the environment. Hence, each learner cannot rely on having knowledge of future state transitions after acting in the world. TPOT-RL enables teams of agents to learn effective policies with very few training examples even in the face of a large state space with large amounts of hidden state. The main responsible features are: dividing the learning task among team members, using a very coarse, action-dependent feature space, and allowing agents to gather reinforcement directly from observation of the environment. TPOT-RL is fully implemented and has been tested in the robotic soccer domain, a complex, multi-agent framework. This paper presents the algorithmic details of TPOT-RL as well as empirical results demonstrating the effectiveness of the developed multi-agent learning approach with learned features. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128256. 	[Title] Where Do SE-trees Perform? (Part I) [Abstract] As a classifier, a Set Enumeration (SE) tree can be viewed as a generalization of decision trees. We empirically characterize domains in which SE-trees are particularly advantageous relative to decision trees. Specifically, we show that: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #636098. 	[Title] NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all [Abstract] In many learning problems, the learning system is presented with values for features that are actually irrelevant to the concept it is trying to learn. The FOCUS algorithm, due to Almuallim and Dietterich, performs an explicit search for the smallest possible input feature set S that permits a consistent mapping from the features in S to the output feature. The FOCUS algorithm can also be seen as an algorithm for learning determinations or functional dependencies, as suggested in [6]. Another algorithm for learning determinations appears in [7]. The FOCUS algorithm has superpolynomial runtime, but Almuallim and Di-etterich leave open the question of tractability of the underlying problem. In this paper, the problem is shown to be NP-complete. We also describe briefly some experiments that demonstrate the benefits of determination learning, and show that finding lowest-cardinality determinations is easier in practice than finding minimal determi Define the MIN-FEATURES problem as follows: given a set X of examples (which are each composed of a a binary value specifying the value of the target feature and a vector of binary values specifying the values of the other features) and a number n, determine whether or not there exists some feature set S such that: We show that MIN-FEATURES is NP-complete by reducing VERTEX-COVER to MIN-FEATURES. 1 The VERTEX-COVER problem may be stated as the question: given a graph G with vertices V and edges E, is there a subset V 0 of V , of size m, such that each edge in E is connected to at least one vertex in V 0 ? We may reduce an instance of VERTEX-COVER to an instance of MIN-FEATURES by mapping each edge in E to an example in X, with one input feature for every vertex in V . 1 In [8], a "proof" is reported for this result by reduction to set covering. The proof therefore fails to show NP-completeness. nations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120962. 	[Title] Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and [Abstract] This chapter describes three studies which address the question of how neural network learning can be improved via the incorporation of information extracted from other networks. This general problem, which we call network transfer, encompasses many types of relationships between source and target networks. Our focus is on the utilization of weights from source networks which solve a subproblem of the target network task, with the goal of speeding up learning on the target task. We demonstrate how the approach described here can improve learning speed by up to ten times over learning starting with random weights. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17476. 	[Title] Markov games as a framework for multi-agent reinforcement learning [Abstract] In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #10183. 	[Title] Concept Learning and Heuristic Classification in Weak-Theory Domains 1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108167. 	[Title] On Learning Read-k-Satisfy-j DNF [Abstract] We study the learnability of Read-k-Satisfy-j (RkSj) DNF formulas. These are boolean formulas in disjunctive normal form (DNF), in which the maximum number of occurrences of a variable is bounded by k, and the number of terms satisfied by any assignment is at most j. After motivating the investigation of this class of DNF formulas, we present an algorithm that with high probability finds a DNF formula that is logically equivalent to any unknown RkSj DNF formula to be learned. The algorithm uses the well-studied protocol of equivalence and membership queries, and runs in polynomial time for k j = O( log n log log n ), where n is the number of input variables. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #254923. 	[Title] A genetic prototype learner [Abstract] Supervised classification problems have received considerable attention from the machine learning community. We propose a novel genetic algorithm based prototype learning system, PLEASE, for this class of problems. Given a set of prototypes for each of the possible classes, the class of an input instance is determined by the prototype nearest to this instance. We assume ordinal attributes and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space as determined by corresponding feature-value pairs. Comparisons with C4.5 on a set of artificial problems of controlled complexity demonstrate the effectiveness of the pro posed system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #85452. 	[Title] Constructive Induction Using a Non-Greedy Strategy for Feature Selection [Abstract] We present a method for feature construction and selection that finds a minimal set of conjunctive features that are appropriate to perform the classification task. For problems where this bias is appropriate, the method outperforms other constructive induction algorithms and is able to achieve higher classification accuracy. The application of the method in the search for minimal multi-level boolean expressions is presented and analyzed with the help of some examples. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #444240. 	[Title] PLEASE: A prototype learning system using genetic algorithms [Abstract] Prototypes have been proposed as representation of concepts that are used effectively by humans. Developing computational schemes for generating prototypes from examples, however, has proved to be a difficult problem. We present a novel genetic algorithm based prototype learning system, PLEASE, for constructing appropriate prototypes from classified training instances. After constructing a set of prototypes for each of the possible classes, the class of a new input instance is determined by the nearest prototype to this instance. Attributes are assumed to be ordinal in nature and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space. We present experimental results on a series of artificial problems of varying complexity. PLEASE performs competitively with several nearest neighbor classification algorithms on the problem set. An analysis of the strengths and weaknesses of the initial version of our system motivates the need for additional operators. The inclusion of these operators substantially improves the performance of the system on particularly difficult problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13269. 	[Title] database [Abstract] MIT Computational Cognitive Science Technical Report 9701 Abstract We describe variational approximation methods for efficient probabilistic reasoning, applying these methods to the problem of diagnostic inference in the QMR-DT database. The QMR-DT database is a large-scale belief network based on statistical and expert knowledge in internal medicine. The size and complexity of this network render exact probabilistic diagnosis infeasible for all but a small set of cases. This has hindered the development of the QMR- DT network as a practical diagnostic tool and has hindered researchers from exploring and critiquing the diagnostic behavior of QMR. In this paper we describe how variational approximation methods can be applied to the QMR network, resulting in fast diagnostic inference. We evaluate the accuracy of our methods on a set of standard diagnostic cases and compare to stochastic sampling methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27230. 	[Title] K unstliche Intelligenz Grdt: Enhancing Model-Based Learning for Its Application in Robot Navigation [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #192734. 	[Title] Preventing "Overfitting" of Cross-Validation Data [Abstract] Suppose that, for a learning task, we have to select one hypothesis out of a set of hypotheses (that may, for example, have been generated by multiple applications of a randomized learning algorithm). A common approach is to evaluate each hypothesis in the set on some previously unseen cross-validation data, and then to select the hypothesis that had the lowest cross-validation error. But when the cross-validation data is partially corrupted such as by noise, and if the set of hypotheses we are selecting from is large, then "folklore" also warns about "overfitting" the cross-validation data [Klockars and Sax, 1986, Tukey, 1949, Tukey, 1953]. In this paper, we explain how this "overfitting" really occurs, and show the surprising result that it can be overcome by selecting a hypothesis with a higher cross-validation error, over others with lower cross-validation errors. We give reasons for not selecting the hypothesis with the lowest cross-validation error, and propose a new algorithm, LOOCVCV, that uses a computa-tionally efficient form of leave-one-out cross-validation to select such a hypothesis. Finally, we present experimental results for one domain, that show LOOCVCV consistently beating picking the hypothesis with the lowest cross-validation error, even when using reasonably large cross-validation sets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #170338. 	[Title] Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study [Abstract] We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy but may behave differently due to position-dependent inputs. All agents making up a team are rewarded or punished collectively in case of goals. We conduct simulations with varying team sizes, and compare two learning algorithms: TD-Q learning with linear neural networks (TD-Q) and Probabilistic Incremental Program Evolution (PIPE). TD-Q is based on evaluation functions (EFs) mapping input/action pairs to expected reward, while PIPE searches policy space directly. PIPE uses adaptive "probabilistic prototype trees" to synthesize programs that calculate action probabilities from current inputs. Our results show that TD-Q encounters several difficulties in learning appropriate shared EFs. PIPE, however, does not depend on EFs and can find good policies faster and more reliably. This suggests that in multiagent learning scenarios direct search through policy space can offer advantages over EF-based approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #52835. 	[Title] On Biases in Estimating Multi-Valued Attributes [Abstract] We analyse the biases of eleven measures for estimating the quality of the multi-valued attributes. The values of information gain, J-measure, gini-index, and relevance tend to linearly increase with the number of values of an attribute. The values of gain-ratio, distance measure, Relief , and the weight of evidence decrease for informative attributes and increase for irrelevant attributes. The bias of the statistic tests based on the chi-square distribution is similar but these functions are not able to discriminate among the attributes of different quality. We also introduce a new function based on the MDL principle whose value slightly decreases with the increasing number of attribute's values. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #97892. 	[Title] Correcting Imperfect Domain Theories: A Knowledge-Level Analysis [Abstract] Explanation-Based Learning [Mitchell et al., 1986; DeJong and Mooney, 1986] has shown promise as a powerful analytical learning technique. However, EBL is severely hampered by the requirement of a complete and correct domain theory for successful learning to occur. Clearly, in non-trivial domains, developing such a domain theory is a nearly impossible task. Therefore, much research has been devoted to understanding how an imperfect domain theory can be corrected and extended during system performance. In this paper, we present a characterization of this problem, and use it to analyze past research in the area. Past characterizations of the problem (e.g, [Mitchell et al., 1986; Rajamoney and DeJong, 1987]) have viewed the types of performance errors caused by a faulty domain theory as primary. In contrast, we focus primarily on the types of knowledge deficiencies present in the theory, and from these derive the types of performance errors that can result. Correcting the theory can be viewed as a search through the space of possible domain theories, with a variety of knowledge sources that can be used to guide the search. We examine the types of knowledge used by a variety of past systems for this purpose. The hope is that this analysis will indicate the need for a "universal weak method" of domain theory correction, in which different sources of knowledge for theory correction can be freely and flexibly combined. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #93923. 	[Title] Inductive Constraint Logic and the Mutagenesis Problem [Abstract] A novel approach to learning first order logic formulae from positive and negative examples is incorporated in a system named ICL (Inductive Constraint Logic). In ICL, examples are viewed as interpretations which are true or false for the target theory, whereas in present inductive logic programming systems, examples are true and false ground facts (or clauses). Furthermore, ICL uses a clausal representation, which corresponds to a conjunctive normal form where each conjunct forms a constraint on positive examples, whereas classical learning techniques have concentrated on concept representations in disjunctive normal form. We present some experiments with this new system on the mutagenesis problem. These experiments illustrate some of the differences with other systems, and indicate that our approach should work at least as well as the more classical approaches. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #6378. 	[Title] Learning to coordinate without sharing information [Abstract] Researchers in the field of Distributed Artificial Intelligence (DAI) have been developing efficient mechanisms to coordinate the activities of multiple autonomous agents. The need for coordination arises because agents have to share resources and expertise required to achieve their goals. Previous work in the area includes using sophisticated information exchange protocols, investigating heuristics for negotiation, and developing formal models of possibilities of conflict and cooperation among agent interests. In order to handle the changing requirements of continuous and dynamic environments, we propose learning as a means to provide additional possibilities for effective coordination. We use reinforcement learning techniques on a block pushing problem to show that agents can learn complimentary policies to follow a desired path without any knowledge about each other. We theoretically analyze and experimentally verify the effects of learning rate on system convergence, and demonstrate benefits of using learned coordination knowledge on similar problems. Reinforcement learning based coordination can be achieved in both cooperative and non-cooperative domains, and in domains with noisy communication channels and other stochastic characteristics that present a formidable challenge to using other coordination schemes. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #157805. 	[Title] Learning from Queries and Examples with Tree-structured Bias [Abstract] Incorporating declarative bias or prior knowledge into learning is an active research topic in machine learning. Tree-structured bias specifies the prior knowledge as a tree of "relevance" relationships between attributes. This paper presents a learning algorithm that implements tree-structured bias, i.e., learns any target function probably approximately correctly from random examples and membership queries if it obeys a given tree-structured bias. The theoretical predictions of the paper are em pirically validated. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #20593. 	[Title] Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract) [Abstract] We investigate the generation of neural networks through the induction of binary trees of threshold logic units (TLUs). Initially, we describe the framework for our tree construction algorithm and how such trees can be transformed into an isomorphic neural network topology. Several methods for learning the linear discriminant functions at each node of the tree structure are examined and shown to produce accuracy results that are comparable to classical information theoretic methods for constructing decision trees (which use single feature tests at each node). Our TLU trees, however, are smaller and thus easier to understand. Moreover, we show that it is possible to simultaneously learn both the topology and weight settings of a neural network simply using the training data set that we are given. 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 #65653. 	[Title] Pessimistic decision tree pruning based on tree size [Abstract] In this work we develop a new criteria to perform pessimistic decision tree pruning. Our method is theoretically sound and is based on theoretical concepts such as uniform convergence and the Vapnik-Chervonenkis dimension. We show that our criteria is very well motivated, from the theory side, and performs very well in practice. The accuracy of the new criteria is comparable to that of the current method used in C4.5. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1122425. 	[Title] Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems [Abstract] The application of decision making and learning algorithms to multi-agent systems presents many interestingresearch challenges and opportunities. Among these is the ability for agents to learn how to act by observing or imitating other agents. We describe an algorithm, the IQ-algorithm, that integrates imitation with Q-learning. Roughly, a Q-learner uses the observations it has made of an expert agent to bias its exploration in promising directions. This algorithm goes beyond previous work in this direction by relaxing the oft-made assumptions that the learner (observer) and the expert (observed agent) share the same objectives and abilities. Our preliminary experiments demonstrate significant transfer between agents using the IQ-model and in many cases reductions in training time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #112378. 	[Title] Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion [Abstract] We describe an approach to grapheme-to-phoneme conversion which is both language-independent and data-oriented. Given a set of examples (spelling words with their associated phonetic representation) in a language, a grapheme-to-phoneme conversion system is automatically produced for that language which takes as its input the spelling of words, and produces as its output the phonetic transcription according to the rules implicit in the training data. We describe the design of the system, and compare its performance to knowledge-based and alternative data-oriented approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33013. 	[Title] Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates [Abstract] Fully cooperative multiagent systemsthose in which agents share a joint utility modelis of special interest in AI. A key problem is that of ensuring that the actions of individual agents are coordinated, especially in settings where the agents are autonomous decision makers. We investigate approaches to learning coordinated strategies in stochastic domains where an agent's actions are not directly observable by others. Much recent work in game theory has adopted a Bayesian learning perspective to the more general problem of equilibrium selection, but tends to assume that actions can be observed. We discuss the special problems that arise when actions are not observable, including effects on rates of convergence, and the effect of action failure probabilities and asymmetries. We also use likelihood estimates as a means of generalizing fictitious play learning models in our setting. Finally, we propose the use of maximum likelihood as a means of removing strategies from consideration, with the aim of convergence to a conventional equilibrium, at which point learning and deliberation can cease. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13686. 	[Title] Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm [Abstract] Technical Report 1123, Computer Sciences Department, University of Wisconsin - Madison, Nov. 1992 ABSTRACT This article describes an approach to combining symbolic and connectionist approaches to machine learning. A three-stage framework is presented and the research of several groups is reviewed with respect to this framework. The first stage involves the insertion of symbolic knowledge into neural networks, the second addresses the refinement of this prior knowledge in its neural representation, while the third concerns the extraction of the refined symbolic knowledge. Experimental results and open research issues are discussed. A shorter version of this paper will appear in Machine Learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135358. 	[Title] 0 Inductive learning of compact rule sets by using efficient hypotheses reduction [Abstract] A method is described which reduces the hypotheses space with an efficient and easily interpretable reduction criteria called a - reduction. A learning algorithm is described based on a - reduction and analyzed by using probability approximate correct learning results. The results are obtained by reducing a rule set to an equivalent set of kDNF formulas. The goal of the learning algorithm is to induce a compact rule set describing the basic dependencies within a set of data. The reduction is based on criterion which is very exible and gives a semantic interpretation of the rules which fulfill the criteria. Comparison with syntactical hypotheses reduction show that the a reduction improves search and has a smaller probability of missclassification. Please summarise all the given information.

Paper #1129208 has the title `Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and
` and the abstract `that is based on Angluin's L fl algorithm. The algorithm maintains a model consistent with its past examples. When a new counterexample arrives it tries to extend the model in a minimal fashion. We conducted a set of experiments where random automata that represent different strategies were generated, and the algorithm tried to learn them based on prefix-closed samples of their behavior. The algorithm managed to learn very compact models that agree with the samples. The size of the sample had a small effect on the size of the model. The experimental results suggest that for random prefix-closed samples the algorithm behaves well. However, following Angluin's result on the difficulty of learning almost uniform complete samples [ An-gluin, 1978 ] , it is obvious that our algorithm does not solve the complexity issue of inferring a DFA from a general prefix-closed sample. We are currently looking for classes of prefix-closed samples in which US-L* behaves well. [ Carmel and Markovitch, 1994 ] D. Carmel and S. Markovitch. The M* algorithm: Incorporating opponent models into adversary search. Technical Report CIS report 9402, Technion, March 1994. [ Carmel and Markovitch, 1995 ] D. Carmel and S. Markovitch. Unsupervised learning of finite automata: A practical approach. Technical Report CIS report 9504, Technion, March 1995. [ Shoham and Tennenholtz, 1994 ] Y. Shoham and M. Tennenholtz. Co-Learning and the evolution of social activity. Technical Report STAN-CS-TR-94-1511, Stanford Univrsity, Department of Computer Science, 1994. 
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Question: Which category should Paper #1129208 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1129208 has the title `Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and
` and the abstract `that is based on Angluin's L fl algorithm. The algorithm maintains a model consistent with its past examples. When a new counterexample arrives it tries to extend the model in a minimal fashion. We conducted a set of experiments where random automata that represent different strategies were generated, and the algorithm tried to learn them based on prefix-closed samples of their behavior. The algorithm managed to learn very compact models that agree with the samples. The size of the sample had a small effect on the size of the model. The experimental results suggest that for random prefix-closed samples the algorithm behaves well. However, following Angluin's result on the difficulty of learning almost uniform complete samples [ An-gluin, 1978 ] , it is obvious that our algorithm does not solve the complexity issue of inferring a DFA from a general prefix-closed sample. We are currently looking for classes of prefix-closed samples in which US-L* behaves well. [ Carmel and Markovitch, 1994 ] D. Carmel and S. Markovitch. The M* algorithm: Incorporating opponent models into adversary search. Technical Report CIS report 9402, Technion, March 1994. [ Carmel and Markovitch, 1995 ] D. Carmel and S. Markovitch. Unsupervised learning of finite automata: A practical approach. Technical Report CIS report 9504, Technion, March 1995. [ Shoham and Tennenholtz, 1994 ] Y. Shoham and M. Tennenholtz. Co-Learning and the evolution of social activity. Technical Report STAN-CS-TR-94-1511, Stanford Univrsity, Department of Computer Science, 1994. 
`.
Question: Which category should Paper #1129208 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: Theory