Above are papers related to the following paper: [Paper_ID] Paper #1119295. 	[Title] Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on [Abstract] This paper tackles the supervised induction of a distance from examples described as Horn clauses or constrained clauses. In opposition to syntax-driven approaches, this approach is discrimination-driven: it proceeds by defining a small set of complex discriminant hypotheses. These hypotheses serve as new concepts, used to redescribe the initial examples. Further, this redescription can be embedded into the space of natural integers, and a distance between examples thus naturally follows. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1117184. 	[Title] NACODAE: Navy Conversational Decision Aids Environment [Abstract] This report documents NACODAE, the Navy Conversational Decision Aids Environment being developed at the Navy Center for Applied Research in Artificial Intelligence (NCARAI), which is a branch of the Naval Research Laboratory. NA-CODAE is a software prototype that is being developed under the Practical Advances in Case-Based Reasoning project, which is funded by the Office for Naval Research, for the purpose of assisting Navy and other DoD personnel in decision aids tasks such as system maintenance, operational training, crisis response planning, logistics, fault diagnosis, target classification, and meteorological nowcasting. Implemented in Java, NACODAE can be used on any machine containing a Java virtual machine (e.g., PCs, Unix). This document describes and exemplifies NACODAE's capabilities. Our goal is to transition this tool to operational personnel, and to continue its enhancement through user feedback and by testing recent research advances in case-based reasoning and related areas. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106052. 	[Title] Lazy Acquisition of Place Knowledge [Abstract] In this paper we define the task of place learning and describe one approach to this problem. Our framework represents distinct places as evidence grids, a probabilistic description of occupancy. Place recognition relies on nearest neighbor classification, augmented by a registration process to correct for translational differences between the two grids. The learning mechanism is lazy in that it involves the simple storage of inferred evidence grids. Experimental studies with physical and simulated robots suggest that this approach improves place recognition with experience, that it can handle significant sensor noise, that it benefits from improved quality in stored cases, and that it scales well to environments with many distinct places. Additional studies suggest that using historical information about the robot's path through the environment can actually reduce recognition accuracy. Previous researchers have studied evidence grids and place learning, but they have not combined these two powerful concepts, nor have they used systematic experimentation to evaluate their methods' abilities. 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 #151430. 	[Title] Pac-Learning Recursive Logic Programs: Efficient Algorithms [Abstract] We present algorithms that learn certain classes of function-free recursive logic programs in polynomial time from equivalence queries. In particular, we show that a single k-ary recursive constant-depth determinate clause is learnable. Two-clause programs consisting of one learnable recursive clause and one constant-depth determinate non-recursive clause are also learnable, if an additional "basecase" oracle is assumed. These results immediately imply the pac-learnability of these classes. Although these classes of learnable recursive programs are very constrained, it is shown in a companion paper that they are maximally general, in that generalizing either class in any natural way leads to a compu-tationally difficult learning problem. Thus, taken together with its companion paper, this paper establishes a boundary of efficient learnability for recursive logic programs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1061127. 	[Title] Applications of machine learning: a medical follow up study [Abstract] This paper describes preliminary work that aims to apply some learning strategies to a medical follow-up study. An investigation of the application of three machine learning algorithms-1R, FOIL and InductH to identify risk factors that govern the colposuspension cure rate has been made. The goal of this study is to induce a generalised description or explanation of the classification attribute, colposuspension cure rate (completely cured, improved, unchanged and worse) from the 767 examples in the questionnaires. We looked for a set of rules that described which risk factors result in differences of cure rate. The results were encouraging, and indicate that machine learning can play a useful role in large scale medical problem solving. 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 #2440. 	[Title] Quinlan, 1990 J.R. Quinlan. Learning logical definitions from relations. Machine Learning, First-order theory revision. In [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120650. 	[Title] Bottom-up induction of logic programs with more than one recursive clause [Abstract] In this paper we present a bottom-up algorithm called MRI to induce logic programs from their examples. This method can induce programs with a base clause and more than one recursive clause from a very small number of examples. MRI is based on the analysis of saturations of examples. It first generates a path structure, which is an expression of a stream of values processed by predicates. The concept of path structure was originally introduced by Identam-Almquist and used in TIM [ Idestam-Almquist, 1996 ] . In this paper, we introduce the concepts of extension and difference of path structure. Recursive clauses can be expressed as a difference between a path structure and its extension. The paper presents the algorithm and shows experimental results obtained by the method. 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 #4878. 	[Title] Cooperative Case-Based Reasoning [Abstract] We are investigating possible modes of cooperation among homogeneous agents with learning capabilities. In this paper we will be focused on agents that learn and solve problems using Case-based Reasoning (CBR), and we will present two modes of cooperation among them: Distributed Case-based Reasoning (DistCBR) and Collective Case-based Reasoning (ColCBR). We illustrate these modes with an application where different CBR agents able to recommend chromatography techniques for protein purification cooperate. The approach taken is to extend Noos, the representation language being used by the CBR agents. Noos is knowledge modeling framework designed to integrate learning methods and based on the task/method decomposition principle. The extension we present, Plural Noos, allows communication and cooperation among agents implemented in Noos by means of three basic constructs: alien references, foreign method evaluation, and mobile methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114512. 	[Title] Integrity Constraints in ILP using a Monte Carlo approach [Abstract] Many state-of-the-art ILP systems require large numbers of negative examples to avoid overgeneralization. This is a considerable disadvantage for many ILP applications, namely indu ctive program synthesis where relativelly small and sparse example sets are a more realistic scenario. Integrity constraints are first order clauses that can play the role of negative examples in an inductive process. One integrity constraint can replace a long list of ground negative examples. However, checking the consistency of a program with a set of integrity constraints usually involves heavy the orem-proving. We propose an efficient constraint satisfaction algorithm that applies to a wide variety of useful integrity constraints and uses a Monte Carlo strategy. It looks for inconsistencies by ra ndom generation of queries to the program. This method allows the use of integrity constraints instead of (or together with) negative examples. As a consequence programs to induce can be specified more rapidly by the user and the ILP system tends to obtain more accurate definitions. Average running times are not greatly affected by the use of integrity constraints compared to ground negative examples. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49811. 	[Title] Estimating the Accuracy of Learned Concepts [Abstract] This paper investigates alternative estimators of the accuracy of concepts learned from examples. In particular, the cross-validation and 632 bootstrap estimators are studied, using synthetic training data and the foil learning algorithm. Our experimental results contradict previous papers in statistics, which advocate the 632 bootstrap method as superior to cross-validation. Nevertheless, our results also suggest that conclusions based on cross-validation in previous machine learning papers are unreliable. Specifically, our observations are that (i) the true error of the concept learned by foil from independently drawn sets of examples of the same concept varies widely, (ii) the estimate of true error provided by cross-validation has high variability but is approximately unbiased, and (iii) the 632 bootstrap estimator has lower variability than cross-validation, but is systematically biased. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107095. 	[Title] ILP with Noise and Fixed Example Size: A Bayesian Approach [Abstract] Current inductive logic programming systems are limited in their handling of noise, as they employ a greedy covering approach to constructing the hypothesis one clause at a time. This approach also causes difficulty in learning recursive predicates. Additionally, many current systems have an implicit expectation that the cardinality of the positive and negative examples reflect the "proportion" of the concept to the instance space. A framework for learning from noisy data and fixed example size is presented. A Bayesian heuristic for finding the most probable hypothesis in this general framework is derived. This approach evaluates a hypothesis as a whole rather than one clause at a time. The heuristic, which has nice theoretical properties, is incorporated in an ILP system, Lime. Experimental results show that Lime handles noise better than FOIL and PROGOL. It is able to learn recursive definitions from noisy data on which other systems do not perform well. Lime is also capable of learning from only positive data and also from only negative data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110426. 	[Title] Incremental Reduced Error Pruning [Abstract] This paper outlines some problems that may occur with Reduced Error Pruning in relational learning algorithms, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of the algorithm cannot be recommended for domains which require a very specific concept description. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #200480. 	[Title] CBET: a Case Base Exploration Tool [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1121057. 	[Title] Inductive CBR for Customer Support [Abstract] Over the past few years, the telecommunications paradigm has been shifting rapidly from hardware to middleware. In particular, the traditional issues of service characteristics and network control are being replaced by the modern, customer-driven issues of network and service management (e.g., electronic commerce, one-stop shops). An area of service management which has extremely high visibility and negative impact when managed badly is that of problem handling. Problem handling is a very knowledge intensive activity, particularly nowadays with the increase in number and complexity of services becoming available. Trials at several BT support centres have already demonstrated the potential of case-based reasoning technology in improving current practice for problem detection and diagnosis. A major cost involved in implementing a case-based system is in the manual building of the initial case base and then in the subsequent maintenance of that case base over time. This paper shows how inductive machine learning can be combined with case-based reasoning to produce an intelligent system capable of both extracting knowledge from raw data automatically and reasoning from that knowledge. In addition to discovering knowledge in existing data repositories, the integrated system may be used to acquire and revise knowledge continually. Experiments with the suggested integrated approach demonstrate promise and justify the next step. 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 #591017. 	[Title] Producing More Comprehensible Models While Retaining Their Performance [Abstract] Rissanen's Minimum Description Length (MDL) principle is adapted to handle continuous attributes in the Inductive Logic Programming setting. Application of the developed coding as a MDL pruning mechanism is devised. The behavior of the MDL pruning is tested in a synthetic domain with artificially added noise of different levels and in two real life problems | modelling of the surface roughness of a grinding workpiece and modelling of the mutagenicity of nitroaromatic compounds. Results indicate that MDL pruning is a successful parameter-free noise fighting tool in real-life domains since it acts as a safeguard against building too complex models while retaining the accuracy of the model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #212777. 	[Title] Inductive Learning of Characteristic Concept Descriptions from Small Sets of Classified Examples [Abstract] This paper presents a novel idea to the problem of learning concept descriptions from examples. Whereas most existing approaches rely on a large number of classified examples, the approach presented in the paper is aimed at being applicable when only a few examples are classified as positive (and negative) instances of a concept. The approach tries to take advantage of the information which can be induced from descriptions of unclassified objects using a conceptual clustering algorithm. The system Cola is described and results of applying Cola in two real-world domains are presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108267. 	[Title] Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks [Abstract] We describe an integrated problem solving architecture named INBANCA in which Bayesian networks and case-based reasoning (CBR) work cooperatively on multiagent planning tasks. This includes two-team dynamic tasks, and this paper concentrates on simulated soccer as an example. Bayesian networks are used to characterize action selection whereas a case-based approach is used to determine how to implement actions. This paper has two contributions. First, we survey integrations of case-based and Bayesian approaches from the perspective of a popular CBR task decomposition framework, thus explaining what types of integrations have been attempted. This allows us to explain the unique aspects of our proposed integration. Second, we demonstrate how Bayesian nets can be used to provide environmental context, and thus feature selection information, for the case-based reasoner. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6318. 	[Title] Rule Induction and Instance-Based Learning: A Unified Approach [Abstract] This paper presents a new approach to inductive learning that combines aspects of instance-based learning and rule induction in a single simple algorithm. The RISE system searches for rules in a specific-to-general fashion, starting with one rule per training example, and avoids some of the difficulties of separate-and-conquer approaches by evaluating each proposed induction step globally, i.e., through an efficient procedure that is equivalent to checking the accuracy of the rule set as a whole on every training example. Classification is performed using a best-match strategy, and reduces to nearest-neighbor if all generalizations of instances were rejected. An extensive empirical study shows that RISE consistently achieves higher accuracies than state-of-the-art representatives of its "parent" paradigms (PEBLS and CN2), and also outperforms a decision-tree learner (C4.5) in 13 out of 15 test domains (in Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108656. 	[Title] Rule Generation and Compaction in the wwtp [Abstract] In this paper we discuss our approach to learning classification rules from data. We sketch out two modules of our architecture, namely LINNEO + and GAR. LINNEO + , which is a knowledge acquisition tool for ill-structured domains automatically generating classes from examples that incrementally works with an unsupervised strategy. LINNEO + 's output, a representation of the conceptual structure of the domain in terms of classes, is the input to GAR that is used to generate a set of classification rules for the original training set. GAR can generate both conjunctive and disjunctive rules. Herein we present an application of these techniques to data obtained from a real wastewater treatment plant in order to help the construction of a rule base. This rule will be used for a knowledge-based system that aims to supervise the whole process. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49843. 	[Title] Fossil: A Robust Relational Learner [Abstract] The research reported in this paper describes Fossil, an ILP system that uses a search heuristic based on statistical correlation. This algorithm implements a new method for learning useful concepts in the presence of noise. In contrast to Foil's stopping criterion, which allows theories to grow in complexity as the size of the training sets increases, we propose a new stopping criterion that is independent of the number of training examples. Instead, Fossil's stopping criterion depends on a search heuristic that estimates the utility of literals on a uniform scale. In addition we outline how this feature can be used for top-down pruning and present some preliminary results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49844. 	[Title] A Comparison of Pruning Methods for Relational Concept Learning [Abstract] Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are more accurate, but much slower, because they have to generate an overly specific concept description first. We have experimented with a variety of pruning methods, including two new methods that try to combine and integrate pre- and post-pruning in order to achieve both accuracy and efficiency. This is verified with test series in a chess position classification task. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521269. 	[Title] Similarity Metrics: A Formal Unification of Cardinal and Non-Cardinal Similarity Measures [Abstract] In [9] we introduced a formal framework for constructing ordinal similarity measures, and suggested how this might also be applied to cardinal measures. In this paper we will place this approach in a more general framework, called similarity metrics. In this framework, ordinal similarity metrics (where comparison returns a boolean value) can be combined with cardinal metrics (returning a numeric value) and, indeed, with metrics returning values of other types, to produce new metrics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120563. 	[Title] Preparing Case Retrieval Nets for Distributed Processing [Abstract] In this paper, we discuss two approaches of applying the memory model of Case Retrieval Nets to applications where a distributed processing of information is required. For this, we distinguish two types of such applications, namely (a) the case of distributed case libraries and (b) the case of distributed cases. While a solution to the former is straightforward, the latter requires an extension to Case Retrieval Nets which provides a kind of partitioning of the entire net structure. This extended model even allows for a concurrent implementation of the retrieval process or for the use of collaborative agents for retrieval. Keywords: Case-based reasoning, case retrieval, memory structures, distributed processing. 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 #49847. 	[Title] Top-Down Pruning in Relational Learning [Abstract] Pruning is an effective method for dealing with noise in Machine Learning. Recently pruning algorithms, in particular Reduced Error Pruning, have also attracted interest in the field of Inductive Logic Programming. However, it has been shown that these methods can be very inefficient, because most of the time is wasted for generating clauses that explain noisy examples and subsequently pruning these clauses. We introduce a new method which searches for good theories in a top-down fashion to get a better starting point for the pruning algorithm. Experiments show that this approach can significantly lower the complexity of the task without losing predictive accuracy. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63931. 	[Title] Combining FOIL and EBG to Speed-up Logic Programs [Abstract] This paper presents an algorithm that combines traditional EBL techniques and recent developments in inductive logic programming to learn effective clause selection rules for Prolog programs. When these control rules are incorporated into the original program, significant speed-up may be achieved. The algorithm is shown to be an improvement over competing EBL approaches in several domains. Additionally, the algorithm is capable of automatically transforming some intractable algorithms into ones that run in polynomial time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1111614. 	[Title] SaxEx a case-based reasoning system for generating expressive musical performances [Abstract] We have studied the problem of generating expressive musical performances in the context of tenor saxophone interpretations. We have done several recordings of a tenor sax playing different Jazz ballads with different degrees of expressiveness including an inexpressive interpretation of each ballad. These recordings are analyzed, using SMS spectral modeling techniques, to extract information related to several expressive parameters. This set of parameters and the scores constitute the set of cases (examples) of a case-based system. From this set of cases, the system infers a set of possible expressive transformations for a given new phrase applying similarity criteria, based on background musical knowledge, between this new phrase and the set of cases. Finally, SaxEx applies the inferred expressive transformations to the new phrase using the synthesis capabilities of SMS. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #136766. 	[Title] Learning Goal-Decomposition Rules using Exercises [Abstract] Exercises are problems ordered in increasing order of difficulty. Teaching problem-solving through exercises is a widely used pedagogic technique. A computational reason for this is that the knowledge gained by solving simple problems is useful in efficiently solving more difficult problems. We adopt this approach of learning from exercises to acquire search-control knowledge in the form of goal-decomposition rules (d-rules). D-rules are first order, and are learned using a new "generalize-and-test" algorithm which is based on inductive logic programming techniques. We demonstrate the feasibility of the approach by applying it in two planning do mains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #136768. 	[Title] Theory-guided Empirical Speedup Learning of Goal Decomposition Rules [Abstract] Speedup learning is the study of improving the problem-solving performance with experience and from outside guidance. We describe here a system that successfully combines the best features of Explanation-based learning and empirical learning to learn goal decomposition rules from examples of successful problem solving and membership queries. We demonstrate that our system can efficiently learn effective decomposition rules in three different domains. Our results suggest that theory-guided empirical learning can overcome the problems of purely explanation-based learning and purely empirical learning, and be an effective speedup learning method. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1127619. 	[Title] Cooperation of Data-driven and Model-based Induction Methods for Relational Learning [Abstract] Inductive learning in relational domains has been shown to be intractable in general. Many approaches to this task have been suggested nevertheless; all in some way restrict the hypothesis space searched. They can be roughly divided into two groups: data-driven, where the restriction is encoded into the algorithm, and model-based, where the restrictions are made more or less explicit with some form of declarative bias. This paper describes Incy, an inductive learner that seeks to combine aspects of both approaches. Incy is initially data-driven, using examples and background knowledge to put forth and specialize hypotheses based on the "connectivity" of the data at hand. It is model-driven in that hypotheses are abstracted into rule models, which are used both for control decisions in the data-driven phase and for model-guided induction. Key Words: Inductive learning in relational domains, cooperation of data-driven and model-guided methods, implicit and declarative bias. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #399173. 	[Title] Corporate Memories as Distributed Case Libraries [Abstract] Rising operating costs and structural transformations such as resizing and globaliza-tion of companies all over the world have brought into focus the emerging discipline of knowledge management that is concerned with making knowledge pay off. Corporate memories form an important part of such knowledge management initiatives in a company. In this paper, we discuss how viewing corporate memories as distributed case libraries can benefit from existing techniques for distributed case-based reasoning for resource discovery and exploitation of previous expertise. We present two techniques developed in the context of multi-agent case-based reasoning for accessing and exploiting past experience from corporate memory resources. The first approach, called Negotiated Retrieval, deals with retrieving and assembling "case pieces" from different resources in a corporate memory to form a good overall case. The second approach, based on Federated Peer Learning, deals with two modes of cooperation called DistCBR and ColCBR that let an agent exploit the experience and expertise of peer agents to achieve a local task. fl The first author would like to acknowledge the support by the National Science Foundation under Grant Nos. IRI-9523419 and EEC-9209623. The second author's research reported in this paper has been developed at the IIIA inside the ANALOG Project funded by Spanish CICYT grant 122/93. The content of this paper does not necessarily reflect the position or the policy of the US Government, the Kingdom of Spain Government, or the Catalonia Government, and no official endorsement should be inferred. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #582343. 	[Title] Transferring and Retraining Learned Information Filters [Abstract] Any system that learns how to filter documents will suffer poor performance during an initial training phase. One way of addressing this problem is to exploit filters learned by other users in a collaborative fashion. We investigate "direct transfer" of learned filters in this setting|a limiting case for any collaborative learning system. We evaluate the stability of several different learning methods under direct transfer, and conclude that symbolic learning methods that use negatively correlated features of the data perform poorly in transfer, even when they perform well in more conventional evaluation settings. This effect is robust: it holds for several learning methods, when a diverse set of users is used in training the classifier, and even when the learned classifiers can be adapted to the new user's distribution. Our experiments give rise to several concrete proposals for improving generalization performance in a collaborative setting, including a beneficial variation on a feature selection method that has been widely used in text categorization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108551. 	[Title] From: Computational Learning Theory and Natural Systems, Chapter 18, "Cross-validation and Modal Theories", Cross-Validation and [Abstract] Cross-validation is a frequently used, intuitively pleasing technique for estimating the accuracy of theories learned by machine learning algorithms. During testing of a machine learning algorithm (foil) on new databases of prokaryotic RNA transcription promoters which we have developed, cross-validation displayed an interesting phenomenon. One theory is found repeatedly and is responsible for very little of the cross-validation error, whereas other theories are found very infrequently which tend to be responsible for the majority of the cross-validation error. It is tempting to believe that the most frequently found theory (the "modal theory") may be more accurate as a classifier of unseen data than the other theories. However, experiments showed that modal theories are not more accurate on unseen data than the other theories found less frequently during cross-validation. Modal theories may be useful in predicting when cross-validation is a poor estimate of true accuracy. We offer explanations 1 For correspondence: Department of Computer Science and Engineering, University of California, San 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 #1000012. 	[Title] Stochastic Propositionalization of Non-Determinate Background Knowledge [Abstract] It is a well-known fact that propositional learning algorithms require "good" features to perform well in practice. So a major step in data engineering for inductive learning is the construction of good features by domain experts. These features often represent properties of structured objects, where a property typically is the occurrence of a certain substructure having certain properties. To partly automate the process of "feature engineering", we devised an algorithm that searches for features which are defined by such substructures. The algorithm stochastically conducts a top-down search for first-order clauses, where each clause represents a binary feature. It differs from existing algorithms in that its search is not class-blind, and that it is capable of considering clauses ("context") of almost arbitrary length (size). Preliminary experiments are favorable, and support the view that this approach is promising. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #72908. 	[Title] Applications of a logical discovery engine [Abstract] The clausal discovery engine claudien is presented. claudien discovers regularities in data and is a representative of the inductive logic programming paradigm. As such, it represents data and regularities by means of first order clausal theories. Because the search space of clausal theories is larger than that of attribute value representation, claudien also accepts as input a declarative specification of the language bias, which determines the set of syntactically well-formed regularities. Whereas other papers on claudien focuss on the semantics or logical problem specification of claudien, on the discovery algorithm, or the PAC-learning aspects, this paper wants to illustrate the power of the resulting technique. In order to achieve this aim, we show how claudien can be used to learn 1) integrity constraints in databases, 2) functional dependencies and determinations, 3) properties of sequences, 4) mixed quantitative and qualitative laws, 5) reverse engineering, and 6) classification rules. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113934. 	[Title] Integration of Case-Based Reasoning and Neural Networks Approaches for Classification [Abstract] Several different approaches have been used to describe concepts for supervised learning tasks. In this paper we describe two approaches which are: prototype-based incremental neural networks and case-based reasoning approaches. We show then how we can improve a prototype-based neural network model by storing some specific instances in a CBR memory system. This leads us to propose a co-processing hybrid model for classification. 1 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106388. 	[Title] Predicting Ordinal Classes in ILP [Abstract] This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes in an ILP setting. We start with a relational regression algorithm named SRT (Structural Regression Trees) and study various ways of transforming it into a first-order learner for ordinal classification tasks. Combinations of these algorithm variants with several data preprocessing methods are compared on two ILP benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression in relational learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1117786. 	[Title] An intelligent search method using Inductive Logic Programming [Abstract] We propose a method to use Inductive Logic Programming to give heuristic functions for searching goals to solve problems. The method takes solutions of a problem or a history of search and a set of background knowledge on the problem. In a large class of problems, a problem is described as a set of states and a set of operators, and is solved by finding a series of operators. A solution, a series of operators that brings an initial state to a final state, is transformed into positive and negative examples of a relation "better-choice", which describes that an operator is better than others in a state. We also give a way to use the "better-choice" relation as a heuristic function. The method can use any logic program as background knowledge to induce heuristics, and induced heuristics has high readability. The paper inspects the method by applying to a puzzle. 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 #20833. 	[Title] Using Partitioning to Speed Up Specific-to-General Rule Induction [Abstract] RISE (Domingos 1995; in press) is a rule induction algorithm that proceeds by gradually generalizing rules, starting with one rule per example. This has several advantages compared to the more common strategy of gradually specializing initially null rules, and has been shown to lead to significant accuracy gains over algorithms like C4.5RULES and CN2 in a large number of application domains. However, RISE's running time (like that of other rule induction algorithms) is quadratic in the number of examples, making it unsuitable for processing very large databases. This paper studies the use of partitioning to speed up RISE, and compares it with the well-known method of windowing. The use of partitioning in a specific-to-general induction setting creates synergies that would not be possible with a general-to-specific system. Partitioning often reduces running time and improves accuracy at the same time. In noisy conditions, the performance of windowing deteriorates rapidly, while that of partitioning remains stable. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153891. 	[Title] Conceptual Analogy [Abstract] Conceptual analogy (CA) is an approach that integrates conceptualization, i.e., memory organization based on prior experiences and analogical reasoning (Borner 1994a). It was implemented prototypically and tested to support the design process in building engineering (Borner and Janetzko 1995, Borner 1995). There are a number of features that distinguish CA from standard approaches to CBR and AR. First of all, CA automatically extracts the knowledge needed to support design tasks (i.e., complex case representations, the relevance of object features and relations, and proper adaptations) from attribute-value representations of prior layouts. Secondly, it effectively determines the similarity of complex case representations in terms of adaptability. Thirdly, implemented and integrated into a highly interactive and adaptive system architecture it allows for incremental knowledge acquisition and user support. This paper surveys the basic assumptions and the psychological results which influenced the development of CA. It sketches the knowledge representation formalisms employed and characterizes the sub-processes needed to integrate memory organization and analogical reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153254. 	[Title]  [Abstract] A Methodology for Evaluating Theory Revision Systems: Results Abstract Theory revision systems are learning systems that have a goal of making small changes to an original theory to account for new data. A measure for the distance between two theories is proposed. This measure corresponds to the minimum number of edit operations at the literal level required to transform one theory into another. By computing the distance between an original theory and a revised theory, the claim that a theory revision system makes few revisions to a theory may be quantitatively evaluated. We present data using both accuracy and the distance metric on Audrey II, with Audrey II fl Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102567. 	[Title] Computer-Supported Argumentation for Cooperative Design on the World-Wide Web [Abstract] This paper describes an argumentation system for cooperative design applications on the Web. The system provides experts involved in such procedures means of expressing and weighing their individual arguments and preferences, in order to argue for or against the selection of a certain choice. It supports defeasible and qualitative reasoning in the presence of ill-structured information. Argumentation is performed through a set of discourse acts which call a variety of procedures for the propagation of information in the corresponding discussion graph. The paper also reports on the integration of Case Based Reasoning techniques, used to resolve current design issues by considering previous similar situations, and the specitcation of similarity measures between the various argumentation items, the aim being to estimate the variations among opinions of the designers involved in cooperative design. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #37483. 	[Title] Learning Approximate Control Rules Of High Utility [Abstract] One of the difficult problems in the area of explanation based learning is the utility problem; learning too many rules of low utility can lead to swamping, or degradation of performance. This paper introduces two new techniques for improving the utility of learned rules. The first technique is to combine EBL with inductive learning techniques to learn a better set of control rules; the second technique is to use these inductive techniques to learn approximate control rules. The two techniques are synthesized in an algorithm called approximating abductive explanation based learning (AxA-EBL). AxA-EBL is shown to improve substantially over standard EBL in several domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114864. 	[Title] Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1) [Abstract] This is a brief annotated bibliography that I wanted to make available to the attendees of my Machine Learning tutorial at the 1995 AI & Statistics Workshop. These slides are available in my WWW pages under slides. Please contact me if you have any questions. Please also note the date (listed above) on which this was most recently updated. While I plan to make occasional updates to this file, it is bound to be outdated quickly. Also, I apologize for the lack of figures, but my time on this project is limited and the slides should compensate. Finally, this bibliography is, by definition, This book is now out of date. Both Pat Langley and Tom Mitchell are in the process of writing textbooks on this subject, but we're still waiting for them. Until then, I suggest looking at both the Readings and the recent ML conference proceedings (both International and European). There are also a few introductory papers on this subject, though I haven't gotten around to putting them in here yet. However, Pat Langley and Dennis Kibler (1988) have written a good paper on ML as an empirical science, and Pat has written several editorials of use to the ML author (Langley 1986; 1987; 1990). incomplete, and I've left out many other references that may be of some use. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521207. 	[Title] A Formal Analysis of Case Base Retrieval [Abstract] Case based systems typically retrieve cases from the case base by applying similarity measures. The measures are usually constructed in an ad hoc manner. This report presents a toolbox for the systematic construction of similarity measures. In addition to paving the way to a design methodology for similarity measures, this systematic approach facilitates the identification of opportunities for parallelisation in case base retrieval. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #23546. 	[Title] Learning Action-oriented Perceptual Features for Robot Navigation [Abstract]  Please summarise all the given information.

Paper #1119295 has the title `Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on
` and the abstract `This paper tackles the supervised induction of a distance from examples described as Horn clauses or constrained clauses. In opposition to syntax-driven approaches, this approach is discrimination-driven: it proceeds by defining a small set of complex discriminant hypotheses. These hypotheses serve as new concepts, used to redescribe the initial examples. Further, this redescription can be embedded into the space of natural integers, and a distance between examples thus naturally follows. 
`.
Question: Which category should Paper #1119295 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1119295 has the title `Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on
` and the abstract `This paper tackles the supervised induction of a distance from examples described as Horn clauses or constrained clauses. In opposition to syntax-driven approaches, this approach is discrimination-driven: it proceeds by defining a small set of complex discriminant hypotheses. These hypotheses serve as new concepts, used to redescribe the initial examples. Further, this redescription can be embedded into the space of natural integers, and a distance between examples thus naturally follows. 
`.
Question: Which category should Paper #1119295 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: Rule_Learning