Above are papers related to the following paper: [Paper_ID] Paper #10798. 	[Title] Learning physical descriptions from functional definitions, examples, Learning from examples: The effect of different conceptual [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #308232. 	[Title] Extending Theory Refinement to M-of-N Rules [Abstract] In recent years, machine learning research has started addressing a problem known as theory refinement. The goal of a theory refinement learner is to modify an incomplete or incorrect rule base, representing a domain theory, to make it consistent with a set of input training examples. This paper presents a major revision of the Either propositional theory refinement system. Two issues are discussed. First, we show how run time efficiency can be greatly improved by changing from a exhaustive scheme for computing repairs to an iterative greedy method. Second, we show how to extend Either to refine M-of-N rules. The resulting algorithm, Neither (New Either), is more than an order of magnitude faster and produces significantly more accurate results with theories that fit the M-of-N format. To demonstrate the advantages of Neither, we present experimental results from two real-world domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3085. 	[Title] MAC/FAC: A Model of Similarity-based Retrieval [Abstract] We present a model of similarity-based retrieval which attempts to capture three psychological phenomena: (1) people are extremely good at judging similarity and analogy when given items to compare. (2) Superficial remindings are much more frequent than structural remindings. (3) People sometimes experience and use purely structural analogical re-mindings. Our model, called MAC/FAC (for "many are called but few are chosen") consists of two stages. The first stage (MAC) uses a computationally cheap, non-structural matcher to filter candidates from a pool of memory items. That is, we redundantly encode structured representations as content vectors, whose dot product yields an estimate of how well the corresponding structural representations will match. The second stage (FAC) uses SME to compute a true structural match between the probe and output from the first stage. MAC/FAC has been fully implemented, and we show that it is capable of modeling patterns of access found in psychological data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105428. 	[Title] From Theory Refinement to KB Maintenance: a Position Statement [Abstract] Since we consider theory refinement (TR) as a possible key concept for a methodologically clear view of knowledge-base maintenance, we try to give a structured overview about the actual state-of-the-art in TR. This overview is arranged along the description of TR as a search problem. We explain the basic approach, show the variety of existing systems and try to give some hints about the direction future research should go. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4649. 	[Title] Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning [Abstract] An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational learning system. We compare the accuracy of concepts learned with this analytic strategy to concepts learned with an analytic strategy that operationalizes the domain theory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10796. 	[Title] An Improved Algorithm for Incremental Induction of Decision Trees [Abstract] Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called `slewing' is introduced. Finally, a non-incremental method is given for finding a decision tree based on a direct metric of a candidate tree. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4660. 	[Title] Context-sensitive learning methods for text categorization [Abstract] Two recently implemented machine learning algorithms, RIPPER and sleeping experts for phrases, are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the "context" of a word w to affect how (or even whether) the presence or absence of w will contribute to a classification. However, RIPPER and sleeping experts differ radically in many other respects: differences include different notions as to what constitutes a context, different ways of combining contexts to construct a classifier, different methods to search for a combination of contexts, and different criteria as to what contexts should be included in such a combination. In spite of these differences, both RIPPER and sleeping experts perform extremely well across a wide variety of categorization problems, generally outperforming previously applied learning methods. We view this result as a confirmation of the usefulness of classifiers that represent contextual information. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153091. 	[Title] More Efficient Windowing [Abstract] Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, previous work has shown that windowing may often lead to a decrease in performance. In this work, we try to argue that separate-and-conquer rule learning algorithms are more appropriate for windowing than divide-and-conquer algorithms, because they learn rules independently and are less susceptible to changes in class distributions. In particular, we will present a new windowing algorithm that achieves additional gains in efficiency by exploiting this property of separate-and-conquer algorithms. While the presented algorithm is only suitable for redundant, noise-free data sets, we will also briefly discuss the problem of noisy data in windowing and present some preliminary ideas how it might be solved with an extension of the algorithm introduced in this paper. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6213. 	[Title] Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction, [Abstract] This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #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 #6217. 	[Title] Online Learning with Random Representations [Abstract] We consider the requirements of online learning|learning which must be done incrementally and in realtime, with the results of learning available soon after each new example is acquired. Despite the abundance of methods for learning from examples, there are few that can be used effectively for online learning, e.g., as components of reinforcement learning systems. Most of these few, including radial basis functions, CMACs, Ko-honen's self-organizing maps, and those developed in this paper, share the same structure. All expand the original input representation into a higher dimensional representation in an unsupervised way, and then map that representation to the final answer using a relatively simple supervised learner, such as a perceptron or LMS rule. Such structures learn very rapidly and reliably, but have been thought either to scale poorly or to require extensive domain knowledge. To the contrary, some researchers (Rosenblatt, 1962; Gallant & Smith, 1987; Kanerva, 1988; Prager & Fallside, 1988) have argued that the expanded representation can be chosen largely at random with good results. The main contribution of this paper is to develop and test this hypothesis. We show that simple random-representation methods can perform as well as nearest-neighbor methods (while being more suited to online learning), and significantly better than backpropagation. We find that the size of the random representation does increase with the dimensionality of the problem, but not unreasonably so, and that the required size can be reduced substantially using unsupervised-learning techniques. Our results suggest that randomness has a useful role to play in online supervised learning and constructive induction. 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 #114. 	[Title] Learning to Act using Real-Time Dynamic Programming [Abstract] fl The authors thank Rich Yee, Vijay Gullapalli, Brian Pinette, and Jonathan Bachrach for helping to clarify the relationships between heuristic search and control. We thank Rich Sutton, Chris Watkins, Paul Werbos, and Ron Williams for sharing their fundamental insights into this subject through numerous discussions, and we further thank Rich Sutton for first making us aware of Korf's research and for his very thoughtful comments on the manuscript. We are very grateful to Dimitri Bertsekas and Steven Sullivan for independently pointing out an error in an earlier version of this article. Finally, we thank Harry Klopf, whose insight and persistence encouraged our interest in this class of learning problems. This research was supported by grants to A.G. Barto from the National Science Foundation (ECS-8912623 and ECS-9214866) and the Air Force Office of Scientific Research, Bolling AFB (AFOSR-89-0526). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108597. 	[Title] Analogical Problem Solving by Adaptation of Schemes [Abstract] We present a computational approach to the acquisition of problem schemes by learning by doing and to their application in analogical problem solving. Our work has its background in automatic program construction and relies on the concept of recursive program schemes. In contrast to the usual approach to cognitive modelling where computational models are designed to fit specific data we propose a framework to describe certain empirically established characteristics of human problem solving and learning in a uniform and formally sound way. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153148. 	[Title] Learning Classification Trees [Abstract] Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule is similar to Quinlan's information gain, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 (Quinlan et al., 1987) and Breiman et al.'s CART (Breiman et al., 1984) show the full Bayesian algorithm can produce Publication: This paper is a final draft submitted for publication to the Statistics and Computing journal; a version with some minor changes appeared in Volume 2, 1992, pages 63-73. more accurate predictions than versions of these other approaches, though pay a computational price. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #93320. 	[Title] Some Experiments with a Hybrid Model for Learning Sequential Decision Making [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #180373. 	[Title] CANCER DIAGNOSIS AND PROGNOSIS VIA LINEAR-PROGRAMMING-BASED MACHINE LEARNING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114777. 	[Title] The Case for Graph-Structured Representations [Abstract] Case-based reasoning involves reasoning from cases: specific pieces of experience, the reasoner's or another's, that can be used to solve problems. We use the term "graph-structured" for representations that (1) are capable of expressing the relations between any two objects in a case, (2) allow the set of relations used to vary from case to case, and (3) allow the set of possible relations to be expanded as necessary to describe new cases. Such representations can be implemented as, for example, semantic networks or lists of concrete propositions in some logic. We believe that graph-structured representations offer significant advantages, and thus we are investigating ways to implement such representations efficiently. We make a "case-based argument" using examples from two systems, chiron and caper, to show how a graph-structured representation supports two different kinds of case-based planning in two different domains. We discuss the costs associated with graph-structured representations and describe an approach to reducing those costs, imple mented in caper. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51866. 	[Title] Bias-Driven Revision of Logical Domain Theories [Abstract] The theory revision problem is the problem of how best to go about revising a deficient domain theory using information contained in examples that expose inaccuracies. In this paper we present our approach to the theory revision problem for propositional domain theories. The approach described here, called PTR, uses probabilities associated with domain theory elements to numerically track the ``ow'' of proof through the theory. This allows us to measure the precise role of a clause or literal in allowing or preventing a (desired or undesired) derivation for a given example. This information is used to efficiently locate and repair awed elements of the theory. PTR is proved to converge to a theory which correctly classifies all examples, and shown experimentally to be fast and accurate even for deep theories. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130653. 	[Title] Functional Programming by Analogy [Abstract] In this paper we describe how the principles of problem solving by analogy can be applied to the domain of functional program synthesis. For this reason, we treat programs as syntactical structures. We discuss two different methods to handle these structures: (a) a graph metric for determining the distance between two program schemes, and (b) the Structure Mapping Engine (an existing system to examine analogical processing). Furthermore we show experimental results and discuss them. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130657. 	[Title] Learning from Examples: Reminding or Heuristic Switching? [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3236. 	[Title] MLC A Machine Learning Library in C [Abstract] We present MLC ++ , a library of C ++ classes and tools for supervised Machine Learning. While MLC ++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and display information visually. More than just a collection of existing algorithms, MLC ++ is an attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible. In this paper we discuss the problems MLC ++ aims to solve, the design of MLC ++ , and the current functionality. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116839. 	[Title] Modeling Analogical Problem Solving in a Production System Architecture [Abstract] This research is supported by a National Science Foundation Fellowship awarded to Dario Salvucci and Office of Naval Research grant N00014-96-1-0491 awarded to John Anderson. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation, the Office of Naval Research, or the United States government. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #112813. 	[Title] REPRO: Supporting Flowsheet Design by Case-Base Retrieval [Abstract] Case-Based Reasoning (CBR) paradigm is very close to the designer behavior during the conceptual design, and seems to be a fruitable computer aided-design approach if a library of design cases is available. The goal of this paper is to presents the general framework of a case-based retrieval system: REPRO, that supports chemical process design. The crucial problems like the case representation and structural similarity measure are widely described. The presented experimental results and the expert evaluation shows usefulness of the described system in real world problems. The papers ends with discussion concerning research problems and future work. 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 #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 #18619. 	[Title] Heterogeneous Uncertainty Sampling for Supervised Learning [Abstract] Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #259772. 	[Title] Reasoning with Portions of Precedents [Abstract] This paper argues that the task of matching in case-based reasoning can often be improved by comparing new cases to portions of precedents. An example is presented that illustrates how combining portions of multiple precedents can permit new cases to be resolved that would be indeterminate if new cases could only be compared to entire precedents. A system that uses of portions of precedents for legal analysis in the domain of Texas worker's compensation law, GREBE, is described, and examples of GREBE's analysis that combine reasoning steps from multiple precedents are presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10430. 	[Title] Rules and Precedents as Complementary Warrants Complementarity of Rules and Precedents for Classification In a [Abstract] This paper describes a model of the complementarity of rules and precedents in the classification task. Under this model, precedents assist rule-based reasoning by operationalizing abstract rule antecedents. Conversely, rules assist case-based reasoning through case elaboration, the process of inferring case facts in order to increase the similarity between cases, and term reformulation, the process of replacing a term whose precedents only weakly match a case with terms whose precedents strongly match the case. Fully exploiting this complementarity requires a control strategy characterized by impartiality, the absence of arbitrary ordering restrictions on the use of rules and precedents. An impartial control strategy was implemented in GREBE in the domain of Texas worker's compensation law. In a preliminary evaluation, GREBE's performance was found to be as good or slightly better than the performance of law students on the same task. A case is classified as belonging to a particular category by relating its description to the criteria for category membership. The justifications, or warrants [Toulmin, 1958], that can relate a case to a category, can vary widely in the generality of their antecedents. For example, consider warrants for classifying a case into the legal category "negligence." A rule, such as "An action is negligent if the actor fails to use reasonable care and the failure is the proximate cause of an injury," has very general antecedent terms (e.g., "breach of reasonable care"). Conversely, a precedent, such as "Dr. Jones was negligent because he failed to count sponges during surgery and as a result left a sponge in Smith," has very specific antecedent terms (e.g., "failure to count sponges"). Both types of warrants have been used by classification systems to relate cases to categories. Classification systems have used precedents to help match the antecedents of rules with cases. Completing this match is difficult when the terms in the antecedent are open-textured, i.e., when there is significant uncertainty whether they match specific facts [Gardner, 1984, McCarty and Sridharan, 1982]. This problem results from the "generality gap" separating abstract terms from specific facts [Porter et al., 1990]. Precedents of an open-textured term, i.e., past cases to which the term applied, can be used to bridge this gap. Unlike rule antecedents, the antecedents of precedents are at the same level of generality as cases, so no generality gap exists between precedents and new cases. Precedents therefore reduce the problem of matching specific case facts with open-textured terms to the problem of matching two sets of specific facts. For example, an injured employee's entitlement to worker's compensation depends on whether he was injured during an activity "in furtherance of employment." Determining whether any particular case should be classified as a compensable injury therefore requires matching the specific facts of the case (e.g., John was injured in an automobile accident while driving to his office) to the open-textured term "activity in furtherance of employment." The gap in generality between the case description and the abstract term makes this match problematical. However, completing this match may be much easier if there are precedents of the term "activity in furtherance of employment" (e.g., Mary's injury was not compensable because it occurred while she was driving to work, which is not an activity in furtherance of employment; Bill's injury was compensable because it occurred while he was driving to a house to deliver a pizza, an activity in furtherance of employment). In this case, John's driving to his office closely matches Mary's driving to work, so Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10435. 	[Title] Improving accuracy by combining rule-based and case-based reasoning [Abstract] An architecture is presented for combining rule-based and case-based reasoning. The architecture is intended for domains that are understood reasonably well, but still imperfectly. It uses a set of rules, which are taken to be only approximately correct, to obtain a preliminary answer for a given problem; it then draws analogies from cases to handle exceptions to the rules. Having rules together with cases not only increases the architecture's domain coverage, it also allows innovative ways of doing case-based reasoning: the same rules that are used for rule-based reasoning are also used by the case-based component to do case indexing and case adaptation. The architecture was applied to the task of name pronunciation, and, with minimal knowledge engineering, was found to perform almost at the level of the best commercial systems. Moreover, its accuracy was found to exceed what it could have achieved with rules or cases alone, thus demonstrating the accuracy improvement afforded by combining rule-based and case-based reasoning. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories of Cambridge, Massachusetts; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories. All rights reserved. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51909. 	[Title] Automated Refinement of First-Order Horn-Clause Domain Theories [Abstract] Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, Forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. Forte uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including propositional theory refinement, first-order induction, and inverse resolution. Forte is demonstrated in several domains, including logic programming and qualitative modelling. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #91853. 	[Title] GIT-CC-92/60 A Model-Based Approach to Analogical Reasoning and Learning in Design [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #566488. 	[Title] REPRESENTING PHYSICAL AND DESIGN KNOWLEDGE IN INNOVATIVE DESIGN [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114336. 	[Title] Four Challenges for a Computational Model of Legal Precedent [Abstract] Identifying the open research issues in a field is a necessary step for progress in that field. This paper describes four open research problems in computational models of precedent-based legal reasoning: relating case representation to precedent use; modeling the selection and construction of both arguments based on pairwise case comparison and multiple-precedent arguments; modeling the process whereby purposes, policies, and principles are used in case similarity assessment; and extending the applicability of precedents to tasks other than classification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20193. 	[Title] The Structure-Mapping Engine: Algorithm and Examples [Abstract] This paper describes the Structure-Mapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the Structure-mapping theory and describe the design of the engine. We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper appeared in Artificial Intelligence, 41, 1989, pp 1-63. For more information, please contact forbus@ils.nwu.edu 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 #1116397. 	[Title] Design, Analogy, and Creativity [Abstract] : Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152244. 	[Title] The Role of Generic Models in Conceptual Change [Abstract] 1 This research was funded in part by NSF Grant No. IRI-92-10925 and in part by ONR Grant No. N00014-92-J-1234. We thank John Clement for the use of his protocol transcript, James Greeno for his contribution to developing our constructive modeling interpretation of it, and Ryan Tweney for his helpful comments Todd W. Griffith, Nancy J. Nersessian, and Ashok Goel Abstract We hypothesize generic models to be central in conceptual change in science. This hypothesis has its origins in two theoretical sources. The first source, constructive modeling, derives from a philosophical theory that synthesizes analyses of historical conceptual changes in science with investigations of reasoning and representation in cognitive psychology. The theory of constructive modeling posits generic mental models as productive in conceptual change. The second source, adaptive modeling, derives from a computational theory of creative design. Both theories posit situation independent domain abstractions, i.e. generic models. Using a constructive modeling interpretation of the reasoning exhibited in protocols collected by John Clement (1989) of a problem solving session involving conceptual change, we employ the resources of the theory of adaptive modeling to develop a new computational model, ToRQUE. Here we describe a piece of our analysis of the protocol to illustrate how our synthesis of the two theories is being used to develop a system for articulating and testing ToRQUE. The results of our research show how generic modeling plays a central role in conceptual change. They also demonstrate how such an interdisciplinary synthesis can provide significant insights into scientific reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #89335. 	[Title] Utilising Explanation to Assist the Refinement of Knowledge-Based Systems [Abstract]  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 #417017. 	[Title] Relating Relational Learning Algorithms [Abstract] Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #267003. 	[Title] Integrating Initialization Bias and Search Bias in Neural Network Learning [Abstract] The use of previously learned knowledge during learning has been shown to reduce the number of examples required for good generalization, and to increase robustness to noise in the examples. In reviewing various means of using learned knowledge from a domain to guide further learning in the same domain, two underlying classes are discerned. Methods which use previous knowledge to initialize a learner (as an initialization bias), and those that use previous knowledge to constrain a learner (as a search bias). We show such methods in fact exploit the same domain knowledge differently, and can complement each other. This is shown by presenting a combined approach which both initializes and constrains a learner. This combined approach is seen to outperform the individual methods under the conditions that accurate previously learned domain knowledge is available, and that there are irrelevant features in the domain representation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #30973. 	[Title] In  Estimating analogical similarity by dot-products of Holographic Reduced Representations. [Abstract] Models of analog retrieval require a computationally cheap method of estimating similarity between a probe and the candidates in a large pool of memory items. The vector dot-product operation would be ideal for this purpose if it were possible to encode complex structures as vector representations in such a way that the superficial similarity of vector representations reflected underlying structural similarity. This paper describes how such an encoding is provided by Holographic Reduced Representations (HRRs), which are a method for encoding nested relational structures as fixed-width distributed representations. The conditions under which structural similarity is reflected in the dot-product rankings of 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.

Above are papers related to the following paper: [Paper_ID] Paper #353541. 	[Title] Building Intelligent Agents for Web-Based Tasks: A Theory-Refinement Approach [Abstract] We present and evaluate an infrastructure with which to rapidly and easily build intelligent software agents for Web-based tasks. Our design is centered around two basic functions: ScoreThis-Link and ScoreThisPage. If given highly accurate such functions, standard heuristic search would lead to efficient retrieval of useful information. Our approach allows users to tailor our system's behavior by providing approximate advice about the above functions. This advice is mapped into neural network implementations of the two functions. Subsequent reinforcements from the Web (e.g., dead links) and any ratings of retrieved pages that the user wishes to provide are, respectively, used to refine the link- and page-scoring functions. Hence, our agent architecture provides an appealing middle ground between nonadaptive "agent" programming languages and systems that solely learn user preferences from the user's ratings of pages. We present a case study where we provide some simple advice and specialize our general-purpose system into a "home-page finder". An empirical study demonstrates that our approach leads to a more effective home-page finder than that of a leading commercial Web search engine. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #54550. 	[Title] Analysis and Empirical Studies of Derivational Analogy [Abstract] Derivational analogy is a technique for reusing problem solving experience to improve problem solving performance. This research addresses an issue common to all problem solvers that use derivational analogy: overcoming the mismatches between past experiences and new problems that impede reuse. First, this research describes the variety of mismatches that can arise and proposes a new approach to derivational analogy that uses appropriate adaptation strategies for each. Second, it compares this approach with seven others in a common domain. This empirical study shows that derivational analogy is almost always more efficient than problem solving from scratch, but the amount it contributes depends on its ability to overcome mismatches Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #101145. 	[Title] Abduction, Experience, and Goals: A Model of Everyday Abductive Explanation* [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #19231. 	[Title] Query by Committee [Abstract] We propose an algorithm called query by committee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptron. As the number of queries goes to infinity, the committee algorithm yields asymptotically finite information gain. This leads to generalization error that decreases exponentially with the number of examples. This in marked contrast to learning from randomly chosen inputs, for which the information gain approaches zero and the generalization error decreases with a relatively slow inverse power law. We suggest that asymptotically finite information gain may be an important characteristic of good query algorithms. 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 #252715. 	[Title] EMERALD: An Integrated System of Machine Learning and Discovery Programs to Support AI Education and [Abstract] With the rapid expansion of machine learning methods and applications, there is a strong need for computer-based interactive tools that support education in this area. The EMERALD system was developed to provide hands-on experience and an interactive demonstration of several machine learning and discovery capabilities for students in AI and cognitive science, and for AI professionals. The current version of EMERALD integrates five programs that exhibit different types of machine learning and discovery: learning rules from examples, determining structural descriptions of object classes, inventing conceptual clusterings of entities, predicting sequences of objects, and discovering equations characterizing collections of quantitative and qualitative data. EMERALD extensively uses color graphic capabilities, voice synthesis, and a natural language representation of the knowledge acquired by the learning programs. Each program is presented as a "learning robot," which has its own "personality," expressed by its icon, its voice, the comments it generates during the learning process, and the results of learning presented as natural language text and/or voice output. Users learn about the capabilities of each "robot" both by being challenged to perform some learning tasks themselves, and by creating their own similar tasks to challenge the "robot." EMERALD is an extension of ILLIAN, an initial, much smaller version that toured eight major US Museums of Science, and was seen by over half a million visitors. EMERALD's architecture allows it to incorporate new programs and new capabilities. The system runs on SUN workstations, and is available to universities and educational institutions. 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 #502574. 	[Title] References Automatic student modeling and bug library construction using theory refinement. Ph.D. ml/ Symbolic revision [Abstract] ASSERT demonstrates how theory refinement techniques developed in machine learning can be used to ef fec-tively build student models for intelligent tutoring systems. This application is unique since it inverts the normal goal of theory refinement from correcting errors in a knowledge base to introducing them. A comprehensive experiment involving a lar ge number of students interacting with an automated tutor for teaching concepts in C ++ programming was used to evaluate the approach. This experiment demonstrated the ability of theory refinement to generate more accurate student models than raw induction, as well as the ability of the resulting models to support individualized feedback that actually improves students subsequent performance. Carr, B. and Goldstein, I. (1977). Overlays: a theory of modeling for computer aided instruction. T echnical Report A. I. Memo 406, Cambridge, MA: MIT. Sandberg, J. and Barnard, Y . (1993). Education and technology: What do we know? And where is AI? Artificial Intelligence Communications, 6(1):47-58. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #173884. 	[Title] Revising Bayesian Network Parameters Using Backpropagation [Abstract] The problem of learning Bayesian networks with hidden variables is known to be a hard problem. Even the simpler task of learning just the conditional probabilities on a Bayesian network with hidden variables is hard. In this paper, we present an approach that learns the conditional probabilities on a Bayesian network with hidden variables by transforming it into a multi-layer feedforward neural network (ANN). The conditional probabilities are mapped onto weights in the ANN, which are then learned using standard backpropagation techniques. To avoid the problem of exponentially large ANNs, we focus on Bayesian networks with noisy-or and noisy-and nodes. Experiments on real world classification problems demonstrate the effectiveness of our technique. 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 #1153877. 	[Title] Adapting Abstract Knowledge [Abstract] For a case-based reasoner to use its knowledge flexibly, it must be equipped with a powerful case adapter. A case-based reasoner can only cope with variation in the form of the problems it is given to the extent that its cases in memory can be efficiently adapted to fit a wide range of new situations. In this paper, we address the task of adapting abstract knowledge about planning to fit specific planning situations. First we show that adapting abstract cases requires reconciling incommensurate representations of planning situations. Next, we describe a representation system, a memory organization, and an adaptation process tailored to this requirement. Our approach is implemented in brainstormer, a planner that takes abstract advice. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153879. 	[Title] Role of Stories 1 [Abstract] PO Box 600 Wellington New Zealand Tel: +64 4 471 5328 Fax: +64 4 495 5232 Internet: Tech.Reports@comp.vuw.ac.nz Technical Report CS-TR-92/4 October 1992 Abstract People often give advice by telling stories. Stories both recommend a course of action and exemplify general conditions in which that recommendation is appropriate. A computational model of advice taking using stories must address two related problems: determining the story's recommendations and appropriateness conditions, and showing that these obtain in the new situation. In this paper, we present an efficient solution to the second problem based on caching the results of the first. Our proposal has been implemented in brainstormer, a planner that takes abstract advice. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153889. 	[Title] Is analogical problem solving always analogical? The case for imitation. Second draft Is analogical problem [Abstract] HCRL Technical Report 97 By Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119078. 	[Title] Tractability of Theory Patching [Abstract] In this paper we consider the problem of theory patching, in which we are given a domain theory, some of whose components are indicated to be possibly flawed, and a set of labeled training examples for the domain concept. The theory patching problem is to revise only the indicated components of the theory, such that the resulting theory correctly classifies all the training examples. Theory patching is thus a type of theory revision in which revisions are made to individual components of the theory. Our concern in this paper is to determine for which classes of logical domain theories the theory patching problem is tractable. We consider both propositional and first-order domain theories, and show that the theory patching problem is equivalent to that of determining what information contained in a theory is stable regardless of what revisions might be performed to the theory. We show that determining stability is tractable if the input theory satisfies two conditions: that revisions to each theory component have monotonic effects on the classification of examples, and that theory components act independently in the classification of examples in the theory. We also show how the concepts introduced can be used to determine the soundness and completeness of particular theory patching algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #59244. 	[Title] Explaining Anomalies as a Basis for Knowledge Base Refinement [Abstract] Explanations play a key role in operationalization-based anomaly detection techniques. In this paper we show that their role is not limited to anomaly detection; they can also be used for guiding automated knowledge base refinement. We introduce a refinement procedure which takes: (i) a small number of refinement rules (rather than test cases), and (ii) explanations constructed in an attempt to reveal the cause (or causes) for inconsistencies detected during the verification process, and returns rule revisions aiming to recover the consistency of the KB-theory. Inconsistencies caused by more than one anomaly are handled at the same time, which improves the efficiency of the refinement process. 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 #31097. 	[Title] What Daimler-Benz has learned as an industrial partner from the Machine Learning Project StatLog [Abstract] Author of this paper was co-ordinator of the Machine Learning project StatLog during 1990-1993. This project was supported financially by the European Community. The main aim of StatLog was to evaluate different learning algorithms using real industrial and commercial applications. As an industrial partner and contributor, Daimler-Benz has introduced different applications to Stat-Log among them fault diagnosis, letter and digit recognition, credit-scoring and prediction of the number of registered trucks. We have learned a lot of lessons from this project which have effected our application oriented research in the field of Machine Learning (ML) in Daimler-Benz. We have distinguished that, especially, more research is necessary to prepare the ML-algorithms to handle the real industrial and commercial applications. In this paper we describe, shortly, the Daimler-Benz applications in StatLog, we discuss shortcomings of the applied ML-algorithms and finally we outline the fields where we think further research is necessary. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #566653. 	[Title] Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts? [Abstract] Judgments of similarity and soundness are important aspects of human analogical processing. This paper explores how these judgments can be modeled using SME, a simulation of Gentner's structure-mapping theory. We focus on structural evaluation, explicating several principles which psychologically plausible algorithms should follow. We introduce the Specificity Conjecture, which claims that naturalistic representations include a preponderance of appearance and low-order information. We demonstrate via computational experiments that this conjecture affects how structural evaluation should be performed, including the choice of normalization technique and how the systematicity preference is implemented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #566664. 	[Title] Making SME greedy and pragmatic [Abstract] The Structure-Mapping Engine (SME) has successfully modeled several aspects of human consistent interpretations of an analogy. While useful for theoretical explorations, this aspect of the algorithm is both psychologically implausible and computationally inefficient. (2) SME contains no mechanism for focusing on interpretations relevant to an analogizer's goals. This paper describes modifications to SME which overcome these flaws. We describe a greedy merge algorithm which efficiently computes an approximate "best" interpretation, and can generate alternate interpretations when necessary. We describe pragmatic marking, a technique which focuses the mapping to produce relevant, yet novel, inferences. We illustrate these techniques via example and evaluate their performance using empirical data and theoretical analysis. analogical processing. However, it has two significant drawbacks: (1) SME constructs all structurally Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #104840. 	[Title] An Optimized Theory Revision Module [Abstract] Theory revision systems typically use a set of theory-to-theory transformations f k g to hill-climb from a given initial theory to a new theory whose empirical accuracy, over a given set of labeled training instances fc j g, is a local maximum. At the heart of each such process is an "evaluator", which compares the accuracy of the current theory KB with that of each of its "neighbors" f k (KB)g, with the goal of determining which neighbor has the highest accuracy. The obvious "wrapper" evaluator simply evaluates each individual neighbor theory KB k = k (KB) on each instance c j . As it can be very expensive to evaluate a single theory on a single instance, and there can be a great many training instances and a huge number of neighbors, this approach can be prohibitively slow. We present an alternative system which employs a smarter evaluator that quickly computes the accuracy of a transformed theory k (KB) by "looking inside" KB and reasoning about the effects of the k transformation. We compare the performance of with the naive wrapper system on real-world theories obtained from a fielded expert system, and find that runs over 35 times faster than , while attaining the same accuracy. This paper also discusses 's source of power. Keywords: theory revision, efficient algorithm, hill-climbing system Multiple Submissions: We have submited a related version of this paper to AAAI96. fl We gratefully acknowledge the many helpful comments on this report from George Drastal, Chandra Mouleeswaran and Geoff Towell. 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 #27535. 	[Title] Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases [Abstract] This paper describes Rapture | a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. Rapture uses a modified version of backpropagation to refine the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on refining three actual expert knowledge bases demonstrate that this combined approach generally performs better than previous methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102751. 	[Title] The Challenge of Revising an Impure Theory [Abstract] A pure rule-based program will return a set of answers to each query; and will return the same answer set even if its rules are re-ordered. However, an impure program, which includes the Prolog cut "!" and not() operators, can return different answers if the rules are re-ordered. There are also many reasoning systems that return only the first answer found for each query; these first answers, too, depend on the rule order, even in pure rule-based systems. A theory revision algorithm, seeking a revised rule-base whose expected accuracy, over the distribution of queries, is optimal, should therefore consider modifying the order of the rules. This paper first shows that a polynomial number of training "labeled queries" (each a query coupled with its correct answer) provides the distribution information necessary to identify the optimal ordering. It then proves, however, that the task of determining which ordering is optimal, once given this information, is intractable even in trivial situations; e.g., even if each query is an atomic literal, we are seeking only a "perfect" theory, and the rule base is propositional. We also prove that this task is not even approximable: Unless P = N P , no polynomial time algorithm can produce an ordering of an n-rule theory whose accuracy is within n fl of optimal, for some fl &gt; 0. We also prove similar hardness, and non-approximatability, results for the related tasks of determining, in these impure contexts, (1) the optimal ordering of the antecedents; (2) the optimal set of rules to add or (3) to delete; and (4) the optimal priority values for a set of defaults. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110947. 	[Title] Lazy Induction Triggered by CBR [Abstract] In recent years, case-based reasoning has been demonstrated to be highly useful for problem solving in complex domains. Also, mixed paradigm approaches emerged for combining CBR and induction techniques aiming at verifying the knowledge and/or building an efficient case memory. However, in complex domains induction over the whole problem space is often not possible or too time consuming. In this paper, an approach is presented which (owing to a close interaction with the CBR part) attempts to induce rules only for a particular context, i.e. for a problem just being solved by a CBR-oriented system. These rules may then be used for indexing purposes or similarity assessment in order to support the CBR process in the future. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1138091. 	[Title] Use of Analogy in Automated Theorem Proving [Abstract] Technical Report ATP-90, Artificial Intelligence Laboratory, University of Texas at Austin. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #434. 	[Title] Learning Analytically and Inductively [Abstract] Learning is a fundamental component of intelligence, and a key consideration in designing cognitive architectures such as Soar [ Laird et al., 1986 ] . This chapter considers the question of what constitutes an appropriate general-purpose learning mechanism. We are interested in mechanisms that might explain and reproduce the rich variety of learning capabilities of humans, ranging from learning perceptual-motor skills such as how to ride a bicycle, to learning highly cognitive tasks such as how to play chess. Research on learning in fields such as cognitive science, artificial intelligence, neurobiology, and statistics has led to the identification of two distinct classes of learning methods: inductive and analytic. Inductive methods, such as neural network Backpropagation, learn general laws by finding statistical correlations and regularities among a large set of training examples. In contrast, analytical methods, such as Explanation-Based Learning, acquire general laws from many fewer training examples. They rely instead on prior knowledge to analyze individual training examples in detail, then use this analysis to distinguish relevant example features from the irrelevant. The question considered in this chapter is how to best combine inductive and analytical learning in an architecture that seeks to cover the range of learning exhibited by intelligent systems such as humans. We present a specific learning mechanism, Explanation Based Neural Network learning (EBNN), that blends these two types of learning, and present experimental results demonstrating its ability to learn control strategies for a mobile robot using Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154500. 	[Title]  [Abstract] Empirical Learning Results in POLLYANNA The value of empirical learning is demonstrated by results of testing the theory space search (TSS) component of POLLYANNA. Empirical data shows approximations generated from generic simplifying assumptions to have widely varying levels of accuracy and efficiency. The candidate theory space includes some theories with Pareto optimal combinations of accuracy and efficiency, as well as others that are non-optimal. Empirical learning is thus needed to separate the optimal theories from the non-optimal ones. It works as a filter on the process of generating approximations from generic simplifying assumptions. Empirical tests serve an additional purpose as well. Theory space search collects data that precisely characterizes the tradeoff between accuracy and efficiency among the candidate approximate theories. The tradeoff data can be used to select a theory that best balances the competing objectives of accuracy and efficiency in a manner appropriate to the intended performance context. The feasibility of empirical learning is also addressed by results of testing the theory space search component of POLLYANNA. In order for empirical testing to be feasible, candidate approximate theories must be operationally usable. Candidate hearts theories generated by POLLYANNA are shown to be operationally usable by experimental results from the theory space search (TSS) phase of learning. They run on a real machine producing results that can be compared with training examples. Feasibility also depends on the information and computation costs of empirical testing. Information costs result from the need to supply the system with training examples. Computation costs result from the need to execute candidate theories. Both types of costs grow with the numbers of candidate theories to be tested. Experimental results show that empirical testing in POLLYANNA is limited more by the computation costs of executing candidate theories than by the information costs of obtaining many training examples. POLLYANNA contrasts in this respect with traditional inductive learning systems. The feasibility of empirical learning depends also on the intended performance context, and on the resources available in the context of learning. Measurements from the theory space search phase indicate that TSS algorithms performing exhaustive search would not be feasible for the hearts domain, although they may be feasible for other applications. TSS algorithms that avoid exhaustive search hold considerably more promise. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5062. 	[Title] A Memory Model for Case Retrieval by Activation Passing [Abstract]  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 #46536. 	[Title] Linear Machine Decision Trees [Abstract] COINS Technical Report 91-10 January 1991 Abstract This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlled manner. Empirical results demonstrate that the algorithm builds small accurate trees across a variety of tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120713. 	[Title] A Computational Model of Ratio Decidendi [Abstract] This paper proposes a model of ratio decidendi as a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. This model satisfies an important set of characteristics of ratio decidendi identified from the jurisprudential literature. In particular, the model shows how the theory under which a case is decided controls its precedential effect. By contrast, a purely exemplar-based model of ratio decidendi fails to account for the dependency of prece-dential effect on the theory of decision. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10186. 	[Title] Learning Default Concepts [Abstract] Classical concepts, based on necessary and sufficient defining conditions, cannot classify logically insufficient object descriptions. Many reasoning systems avoid this limitation by using "default concepts" to classify incompletely described objects. We address the task of learning such default concepts from observational data. We model the underlying performance task | classifying incomplete examples | by a probabilistic process where random test examples are passed through a "blocker" that can hide object attributes from the classifier. We then address the task of learning accurate default concepts from random training examples. We survey the learning techniques that have been proposed for this task in the machine learning and knowledge representation literatures, investigate the relative merits of each, and show that a superior learning technique can be developed from well known statistical principles. Finally, we extend Valiant's pac-learning framework to this context and obtain a number of useful learnability results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144330. 	[Title] Utilizing Prior Concepts for Learning [Abstract] The inductive learning problem consists of learning a concept given examples and non-examples of the concept. To perform this learning task, inductive learning algorithms bias their learning method. Here we discuss biasing the learning method to use previously learned concepts from the same domain. These learned concepts highlight useful information for other concepts in the domain. We describe a transference bias and present M-FOCL, a Horn clause relational learning algorithm, that utilizes this bias to learn multiple concepts. We provide preliminary empirical evaluation to show the effects of biasing previous information on noise-free and noisy data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119180. 	[Title] Concept Sharing: A Means to Improve Multi-Concept Learning [Abstract] This paper describes several means for sharing between related concepts to improve learning in the same domain. The sharing comes in the form of substructures or possibly entire structures of previous concepts which may aid in learning other concepts. These substructures highlight useful information in the domain. Using two domains, we evaluate the effectiveness of concept sharing with respect to accuracy, concept size, search complexity, and noise resistance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #395725. 	[Title] Distributed Representations and Nested Compositional Structure [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20942. 	[Title] Decision Tree Induction Based on Efficient Tree Restructuring [Abstract] The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #47570. 	[Title] Justification Structures for Document Reuse [Abstract] Document drafting|an important problem-solving task of professionals in a wide variety of fields|typifies a design task requiring complex adaptation for case reuse. This paper proposes a framework for document reuse based on an explicit representation of the illocutionary and rhetorical structure underlying documents. Explicit representation of this structure facilitates (1) interpretation of previous documents by enabling them to "explain themselves," (2) construction of documents by enabling document drafters to issue goal-based specifications and rapidly retrieve documents with similar intentional structure, and (3) mainte nance of multi-generation documents. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #601561. 	[Title] Applying a Machine Learning Workbench: Experience with Agricultural Databases [Abstract] This paper reviews our experience with the application of machine learning techniques to agricultural databases. We have designed and implemented a machine learning workbench, WEKA, which permits rapid experimentation on a given dataset using a variety of machine learning schemes, and has several facilities for interactive investigation of the data: preprocessing attributes, evaluating and comparing the results of different schemes, and designing comparative experiments to be run offline. We discuss the partnership between agricultural scientist and machine learning researcher that our experience has shown to be vital to success. We review in some detail a particular agricultural application concerned with the culling of dairy herds. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #601567. 	[Title] Geometric Comparison of Classifications and Rule Sets* [Abstract] We present a technique for evaluating classifications by geometric comparison of rule sets. Rules are represented as objects in an n-dimensional hyperspace. The similarity of classes is computed from the overlap of the geometric class descriptions. The system produces a correlation matrix that indicates the degree of similarity between each pair of classes. The technique can be applied to classifications generated by different algorithms, with different numbers of classes and different attribute sets. Experimental results from a case study in a medical domain are included. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #61417. 	[Title] An Efficient Subsumption Algorithm for Inductive Logic Programming [Abstract] In this paper we investigate the efficiency of - subsumption (` ), the basic provability relation in ILP. As D ` C is NP-complete even if we restrict ourselves to linked Horn clauses and fix C to contain only a small constant number of literals, we investigate in several restrictions of D. We first adapt the notion of determinate clauses used in ILP and show that -subsumption is decidable in polynomial time if D is determinate with respect to C. Secondly, we adapt the notion of k-local Horn clauses and show that - subsumption is efficiently computable for some reasonably small k. We then show how these results can be combined, to give an efficient reasoning procedure for determinate k-local Horn clauses, an ILP-problem recently suggested to be polynomial predictable by Cohen (1993) by a simple counting argument. We finally outline how the -reduction algorithm, an essential part of every lgg ILP-learning algorithm, can be im proved by these ideas. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27627. 	[Title] Information-based objective functions for active data selection [Abstract] Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed which measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8699. 	[Title] Theory Revision in Fault Hierarchies [Abstract] The fault hierarchy representation is widely used in expert systems for the diagnosis of complex mechanical devices. On the assumption that an appropriate bias for a knowledge representation language is also an appropriate bias for learning in this domain, we have developed a theory revision method that operates directly on a fault hierarchy. This task presents several challenges: A typical training instance is missing most feature values, and the pattern of missing features is significant, rather than merely an effect of noise. Moreover, the accuracy of a candidate theory is measured by considering both the sequence of tests required to arrive at a diagnosis and its agreement with the diagnostic endpoints provided by an expert. This paper first describes the algorithm for theory revision of fault hierarchies that was designed to address these challenges, then discusses its application in knowledge base maintenance and reports on experiments that use to revise a fielded diagnostic system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8703. 	[Title] Theory Refinement Combining Analytical and Empirical Methods [Abstract] This article describes a comprehensive approach to automatic theory revision. Given an imperfect theory, the approach combines explanation attempts for incorrectly classified examples in order to identify the failing portions of the theory. For each theory fault, correlated subsets of the examples are used to inductively generate a correction. Because the corrections are focused, they tend to preserve the structure of the original theory. Because the system starts with an approximate domain theory, in general fewer training examples are required to attain a given level of performance (classification accuracy) compared to a purely empirical system. The approach applies to classification systems employing a propositional Horn-clause theory. The system has been tested in a variety of application domains, and results are presented for problems in the domains of molecular biology and plant disease diagnosis. Please summarise all the given information.

Paper #10798 has the title `Learning physical descriptions from functional definitions, examples, Learning from examples: The effect of different conceptual
` and the abstract `nan`.
Question: Which category should Paper #10798 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #10798 has the title `Learning physical descriptions from functional definitions, examples, Learning from examples: The effect of different conceptual
` and the abstract `nan`.
Question: Which category should Paper #10798 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: Case_Based