Above are papers related to the following paper: [Paper_ID] Paper #936. 	[Title] The Use of Explicit Goals for Knowledge to Guide Inference and Learning [Abstract] Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate explicit desires for knowledge. The question of focus of attention is thereby transformed into two related problems: How can explicit desires for knowledge be used to control inference and facilitate resource-constrained goal pursuit in general? and, Where do these desires for knowledge come from? We present a theory of knowledge goals, or desires for knowledge, and their use in the processes of understanding and learning. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel or unusual newspaper stories, and a differential diagnosis program that improves its accuracy with experience. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152896. 	[Title] Abstract [Abstract] Self-selection of input examples on the basis of performance failure is a powerful bias for learning systems. The definition of what constitutes a learning bias, however, has been typically restricted to bias provided by the input language, hypothesis language, and preference criteria between competing concept hypotheses. But if bias is taken in the broader context as any basis that provides a preference for one concept change over another, then the paradigm of failure-driven processing indeed provides a bias. Bias is exhibited by the selection of examples from an input stream that are examples of failure; successful performance is filtered out. We show that the degrees of freedom are less in failure-driven learning than in success-driven learning and that learning is facilitated because of this constraint. We also broaden the definition of failure, provide a novel taxonomy of failure causes, and illustrate the interaction of both in a multistrategy learning system called Meta-AQUA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #255233. 	[Title] An Explanation-Based Approach to Improve Retrieval in Case-Based Planning [Abstract] When a case-based planner is retrieving a previous case in preparation for solving a new similar problem, it is often not aware of the implicit features of the new problem situation which determine if a particular case may be successfully applied. This means that some cases may be retrieved in error in that the case may fail to improve the planner's performance. Retrieval may be incrementally improved by detecting and explaining these failures as they occur. In this paper we provide a definition of case failure for the planner, dersnlp (derivation replay in snlp), which solves new problems by replaying its previous plan derivations. We provide EBL (explanation-based learning) techniques for detecting and constructing the reasons for the failure. We also describe how to organize a case library so as to incorporate this failure information as it is produced. Finally we present an empirical study which demonstrates the effectiveness of this approach in improving the performance of dersnlp. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #310530. 	[Title] Integrating Creativity and Reading: A Functional Approach [Abstract] Reading has been studied for decades by a variety of cognitive disciplines, yet no theories exist which sufficiently describe and explain how people accomplish the complete task of reading real-world texts. In particular, a type of knowledge intensive reading known as creative reading has been largely ignored by the past research. We argue that creative reading is an aspect of practically all reading experiences; as a result, any theory which overlooks this will be insufficient. We have built on results from psychology, artificial intelligence, and education in order to produce a functional theory of the complete reading process. The overall framework describes the set of tasks necessary for reading to be performed. Within this framework, we have developed a theory of creative reading. The theory is implemented in the ISAAC (Integrated Story Analysis And Creativity) system, a reading system which reads science fiction stories. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3084. 	[Title] A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems [Abstract] The utility problem in learning systems occurs when knowledge learned in an attempt to improve a system's performance degrades performance instead. We present a methodology for the analysis of utility problems which uses computational models of problem solving systems to isolate the root causes of a utility problem, to detect the threshold conditions under which the problem will arise, and to design strategies to eliminate it. We present models of case-based reasoning and control-rule learning systems and compare their performance with respect to the swamping utility problem. Our analysis suggests that case-based reasoning systems are more resistant to the utility problem than control-rule learning systems. 1 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #129558. 	[Title] A Goal-Based Approach to Intelligent Information Retrieval [Abstract] Intelligent information retrieval (IIR) requires inference. The number of inferences that can be drawn by even a simple reasoner is very large, and the inferential resources available to any practical computer system are limited. This problem is one long faced by AI researchers. In this paper, we present a method used by two recent machine learning programs for control of inference that is relevant to the design of IIR systems. The key feature of the approach is the use of explicit representations of desired knowledge, which we call knowledge goals. Our theory addresses the representation of knowledge goals, methods for generating and transforming these goals, and heuristics for selecting among potential inferences in order to feasibly satisfy such goals. In this view, IIR becomes a kind of planning: decisions about what to infer, how to infer and when to infer are based on representations of desired knowledge, as well as internal representations of the system's inferential abilities and current state. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel newspaper stories, and a differential diagnosis program that improves its accuracy with experience. We conclude by making several suggestions on how this machine learning framework can be integrated with existing information retrieval methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #101143. 	[Title] Issues in Goal-Driven Explanation [Abstract] When a reasoner explains surprising events for its internal use, a key motivation for explaining is to perform learning that will facilitate the achievement of its goals. Human explainers use a range of strategies to build explanations, including both internal reasoning and external information search, and goal-based considerations have a profound effect on their choices of when and how to pursue explanations. However, standard AI models of explanation rely on goal-neutral use of a single fixed strategy|generally backwards chaining|to build their explanations. This paper argues that explanation should be modeled as a goal-driven learning process for gathering and transforming information, and discusses the issues involved in developing an active multi-strategy process for goal-driven explanation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33303. 	[Title] Design and Implementation of a Replay Framework based on a Partial Order Planner [Abstract] In this paper we describe the design and implementation of the derivation replay framework, dersnlp+ebl (Derivational snlp+ebl), which is based within a partial order planner. dersnlp+ebl replays previous plan derivations by first repeating its earlier decisions in the context of the new problem situation, then extending the replayed path to obtain a complete solution for the new problem. When the replayed path cannot be extended into a new solution, explanation-based learning (ebl) techniques are employed to identify the features of the new problem which prevent this extension. These features are then added as censors on the retrieval of the stored case. To keep retrieval costs low, dersnlp+ebl normally stores plan derivations for individual goals, and replays one or more of these derivations in solving multi-goal problems. Cases covering multiple goals are stored only when subplans for individual goals cannot be successfully merged. The aim in constructing the case library is to predict these goal interactions and to store a multi-goal case for each set of negatively interacting goals. We provide empirical results demonstrating the effectiveness of dersnlp+ebl in improving planning performance on randomly-generated problems drawn from a complex domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1817. 	[Title] Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification [Abstract] This paper introduces a hybrid learning methodology that integrates genetic algorithms (GAs) and decision tree learning (ID3) in order to evolve optimal subsets of discriminatory features for robust pattern classification. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. For a given feature subset, ID3 is invoked to produce a decision tree. The classification performance of the decision tree on unseen data is used as a measure of fitness for the given feature set, which, in turn, is used by the GA to evolve better feature sets. This GA-ID3 process iterates until a feature subset is found with satisfactory classification performance. Experimental results are presented which illustrate the feasibility of our approach on difficult problems involving recognizing visual concepts in satellite and facial image data. The results also show improved classification performance and reduced description complexity when compared against standard methods for feature selection. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1952. 	[Title] Combining Rules and Cases to Learn Case Adaptation [Abstract] Computer models of case-based reasoning (CBR) generally guide case adaptation using a fixed set of adaptation rules. A difficult practical problem is how to identify the knowledge required to guide adaptation for particular tasks. Likewise, an open issue for CBR as a cognitive model is how case adaptation knowledge is learned. We describe a new approach to acquiring case adaptation knowledge. In this approach, adaptation problems are initially solved by reasoning from scratch, using abstract rules about structural transformations and general memory search heuristics. Traces of the processing used for successful rule-based adaptation are stored as cases to enable future adaptation to be done by case-based reasoning. When similar adaptation problems are encountered in the future, these adaptation cases provide task- and domain-specific guidance for the case adaptation process. We present the tenets of the approach concerning the relationship between memory search and case adaptation, the memory search process, and the storage and reuse of cases representing adaptation episodes. These points are discussed in the context of ongoing research on DIAL, a computer model that learns case adaptation knowledge for case-based disaster response planning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1955. 	[Title] In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning [Abstract] Machine learning techniques are perceived to have a great potential as means for the acquisition of knowledge; nevertheless, their use in complex engineering domains is still rare. Most machine learning techniques have been studied in the context of knowledge acquisition for well defined tasks, such as classification. Learning for these tasks can be handled by relatively simple algorithms. Complex domains present difficulties that can be approached by combining the strengths of several complementing learning techniques, and overcoming their weaknesses by providing alternative learning strategies. This study presents two perspectives, the macro and the micro, for viewing the issue of multistrategy learning. The macro perspective deals with the decomposition of an overall complex learning task into relatively well-defined learning tasks, and the micro perspective deals with designing multistrategy learning techniques for supporting the acquisition of knowledge for each task. The two perspectives are discussed in the context of Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #207395. 	[Title] A Functional Theory of Creative Reading [Abstract] Reading is an area of human cognition which has been studied for decades by psychologists, education researchers, and artificial intelligence researchers. Yet, there still does not exist a theory which accurately describes the complete process. We believe that these past attempts fell short due to an incomplete understanding of the overall task of reading; namely, the complete set of mental tasks a reasoner must perform to read and the mechanisms that carry out these tasks. We present a functional theory of the reading process and argue that it represents a coverage of the task. The theory combines experimental results from psychology, artificial intelligence, education, and linguistics, along with the insights we have gained from our own research. This greater understanding of the mental tasks necessary for reading will enable new natural language understanding systems to be more flexible and more capable than earlier ones. Furthermore, we argue that creativity is a necessary component of the reading process and must be considered in any theory or system attempting to describe it. We present a functional theory of creative reading and a novel knowledge organization scheme that supports the creativity mechanisms. The reading theory is currently being implemented in the ISAAC (Integrated Story Analysis And Creativity) system, a computer system which reads science fiction stories. fl This paper is part of the Georgia Institute of Technology, College of Computing, Technical Report series. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1956. 	[Title] INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning [Abstract] The development of multistrategy learning systems should be based on a clear understanding of the roles and the applicability conditions of different learning strategies. To this end, this chapter introduces the Inferential Theory of Learning that provides a conceptual framework for explaining logical capabilities of learning strategies, i.e., their competence. Viewing learning as a process of modifying the learners knowledge by exploring the learners experience, the theory postulates that any such process can be described as a search in a knowledge space, triggered by the learners experience and guided by learning goals. The search operators are instantiations of knowledge transmutations, which are generic patterns of knowledge change. Transmutations may employ any basic type of inferencededuction, induction or analogy. Several fundamental knowledge transmutations are described in a novel and general way, such as generalization, abstraction, explanation and similization, and their counterparts, specialization, concretion, prediction and dissimilization, respectively. Generalization enlarges the reference set of a description (the set of entities that are being described). Abstraction reduces the amount of the detail about the reference set. Explanation generates premises that explain (or imply) the given properties of the reference set. Similization transfers knowledge from one reference set to a similar reference set. Using concepts of the theory, a multistrategy task-adaptive learning (MTL) methodology is outlined, and illustrated b y an example. MTL dynamically adapts strategies to the learning task, defined by the input information, learners background knowledge, and the learning goal. It aims at synergistically integrating a whole range of inferential learning strategies, such as empirical generalization, constructive induction, deductive generalization, explanation, prediction, abstraction, and similization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1959. 	[Title] Introspective reasoning using meta-explanations for multistrategy learning [Abstract] In order to learn effectively, a reasoner must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it performs a given task and what particular pieces of knowledge it needs to improve its performance at the current task. Introspection requires declarative representations of meta-knowledge of the reasoning performed by the system during the performance task, of the system's knowledge, and of the organization of this knowledge. This chapter presents a taxonomy of possible reasoning failures that can occur during a performance task, declarative representations of these failures, and associations between failures and particular learning strategies. The theory is based on Meta-XPs, which are explanation structures that help the system identify failure types, formulate learning goals, and choose appropriate learning strategies in order to avoid similar mistakes in the future. The theory is implemented in a computer model of an introspective reasoner that performs multistrategy learning during a story understanding task. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #940. 	[Title] Decision Models: A Theory of Volitional Explanation [Abstract] This paper presents a theory of motivational analysis, the construction of volitional explanations to describe the planning behavior of agents. We discuss both the content of such explanations, as well as the process by which an understander builds the explanations. Explanations are constructed from decision models, which describe the planning process that an agent goes through when considering whether to perform an action. Decision models are represented as explanation patterns, which are standard patterns of causality based on previous experiences of the understander. We discuss the nature of explanation patterns, their use in representing decision models, and the process by which they are retrieved, used and evaluated. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38829. 	[Title] Learning Generic Mechanisms from Experiences for Analogical Reasoning [Abstract] Humans appear to often solve problems in a new domain by transferring their expertise from a more familiar domain. However, making such cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions used by designers. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from problem-solving experiences in familiar domains by generalization over patterns of regularity. Three important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. In this paper, we show that mental models in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we show how the model-based learning method integrated with similarity-based learning addresses the issues in generalization from experiences. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #941. 	[Title] Incremental Learning of Explanation Patterns and their Indices [Abstract] This paper describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Recent work in AI has dealt with the issue of using past explanations stored in the reasoner's memory to understand novel situations. However, this process assumes that past explanations are well understood and provide good "lessons" to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Instead, it is reasonable to assume that the reasoner would have gaps in its knowledge base. By reasoning about a new situation, the reasoner should be able to fill in these gaps as new information came in, reorganize its explanations in memory, and gradually evolve a better understanding of its domain. We present a story understanding program that retrieves past explanations from situations already in memory, and uses them to build explanations to understand novel stories about terrorism. In doing so, the system refines its understanding of the domain by filling in gaps in these explanations, by elaborating the explanations, or by learning new indices for the explanations. This is a type of incremental learning since the system improves its explanatory knowledge of the domain in an incremental fashion rather than by learning new XPs as a whole. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #943. 	[Title] Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases [Abstract] This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner's memory to understand novel situations or solve novel problems. However, this process assumes that past experiences are well understood and provide good "lessons" to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Furthermore, the reasoner may not even have a case that adequately deals with the new situation, or may not be able to access the case using existing indices. We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of the case by using it to reason about new situations, thus evolving a better understanding of its domain through experience. This research complements work in case-based reasoning by providing mechanisms by which a case library can be automatically built for use by a case-based reasoning program. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110579. 	[Title] Modeling Invention by Analogy in ACT-R [Abstract] We investigate some aspects of cognition involved in invention, more precisely in the invention of the telephone by Alexander Graham Bell. We propose the use of the Structure-Behavior-Function (SBF) language for the representation of invention knowledge; we claim that because SBF has been shown to support a wide range of reasoning about physical devices, it constitutes a plausible account of how an inventor might represent knowledge of an invention. We further propose the use of the ACT-R architecture for the implementation of this model. ACT-R has been shown to very precisely model a wide range of human cognition. We draw upon the architecture for execution of productions and matching of declarative knowledge through spreading activation. Thus we present a model which combines the well-established cognitive validity of ACT-R with the powerful, specialized model-based reasoning methods facilitated by SBF. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38845. 	[Title] Opportunistic Reasoning: A Design Perspective [Abstract] An essential component of opportunistic behavior is opportunity recognition, the recognition of those conditions that facilitate the pursuit of some suspended goal. Opportunity recognition is a special case of situation assessment, the process of sizing up a novel situation. The ability to recognize opportunities for reinstating suspended problem contexts (one way in which goals manifest themselves in design) is crucial to creative design. In order to deal with real world opportunity recognition, we attribute limited inferential power to relevant suspended goals. We propose that goals suspended in the working memory monitor the internal (hidden) representations of the currently recognized objects. A suspended goal is satisfied when the current internal representation and a suspended goal match. We propose a computational model for working memory and we compare it with other relevant theories of opportunistic planning. This working memory model is implemented as part of our IMPROVISER system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #263486. 	[Title] Architecture for Iterative Learning of Recursive Definitions [Abstract] In this paper we are concerned with the problem of inducing recursive Horn clauses from small sets of training examples. The method of iterative bootstrap induction is presented. In the first step, the system generates simple clauses, which can be regarded as properties of the required definition. Properties represent generalizations of the positive examples, simulating the effect of having larger number of examples. Properties are used subsequently to induce the required recursive definitions. This paper describes the method together with a series of experiments. The results support the thesis that iterative bootstrap induction is indeed an effective technique that could be of general use in ILP. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38846. 	[Title] Explaining Serendipitous Recognition in Design [Abstract] Creative designers often see solutions to pending design problems in the everyday objects surrounding them. This can often lead to innovation and insight, sometimes revealing new functions and purposes for common design pieces in the process. We are interested in modeling serendipitous recognition of solutions to pending problems in the context of creative mechanical design. This paper characterizes this ability, analyzing observations we have made of it, and placing it in the context of other forms of recognition. We propose a computational model to capture and explore serendipitous recognition which is based on ideas from reconstructive dynamic memory and situation assessment in case-based reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107010. 	[Title] an Opportunistic Enterprise [Abstract] Tech Report GIT-COGSCI-97/04 Abstract This paper identifies goal handling processes that begin to account for the kind of processes involved in invention. We identify new kinds of goals with special properties and mechanisms for processing such goals, as well as means of integrating opportunism, deliberation, and social interaction into goal/plan processes. We focus on invention goals, which address significant enterprises associated with an inventor. Invention goals represent seed goals of an expert, around which the whole knowledge of an expert gets reorganized and grows more or less opportunistically. Invention goals reflect the idiosyncrasy of thematic goals among experts. They constantly increase the sensitivity of individuals for particular events that might contribute to their satisfaction. Our exploration is based on a well-documented example: the invention of the telephone by Alexander Graham Bell. We propose mechanisms to explain: (1) how Bell's early thematic goals gave rise to the new goals to invent the multiple telegraph and the telephone, and (2) how the new goals interacted opportunistically. Finally, we describe our computational model, ALEC, that accounts for the role of goals in invention. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153101. 	[Title] Machine Learning and Inference [Abstract] Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: onefor the best representation space, and twofor the best hypothesis in that space. In data-driven constructive induction (DCI), a learning system searches for a better representation space by analyzing the input examples (data). The presented data-driven constructive induction method combines an AQ-type learning algorithm with two classes of representation space improvement operators: constructors, and destructors. The implemented system, AQ17-DCI, has been experimentally applied to a GNP prediction problem using a World Bank database. The results show that decision rules learned by AQ17-DCI outperformed the rules learned in the original representation space both in predictive accuracy and rule simplicity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1111899. 	[Title] Case-Based Planning to Learn [Abstract] Learning can be viewed as a problem of planning a series of modifications to memory. We adopt this view of learning and propose the applicability of the case-based planning methodology to the task of planning to learn. We argue that relatively simple, fine-grained primitive inferential operators are needed to support flexible planning. We show that it is possible to obtain the benefits of case-based reasoning within a planning to learn framework. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #143323. 	[Title] AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING [Abstract] The utility problem in speedup learning describes a common behavior of machine learning methods: the eventual degradation of performance due to increasing amounts of learned knowledge. The shape of the learning curve (cost of using a learning method vs. number of training examples) over several domains suggests a parameterized model relating performance to the amount of learned knowledge and a mechanism to limit the amount of learned knowledge for optimal performance. Many recent approaches to avoiding the utility problem in speedup learning rely on sophisticated utility measures and significant numbers of training data to accurately estimate the utility of control knowledge. Empirical results presented here and elsewhere indicate that a simple selection strategy of retaining all control rules derived from a training problem explanation quickly defines an efficient set of control knowledge from few training problems. This simple selection strategy provides a low-cost alternative to example-intensive approaches for improving the speed of a problem solver. Experimentation illustrates the existence of a minimum (representing least cost) in the learning curve which is reached after a few training examples. Stress is placed on controlling the amount of learned knowledge as opposed to which knowledge. An attempt is also made to relate domain characteristics to the shape of the learning curve. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118302. 	[Title] Learning Recursion with Iterative Bootstrap Induction (Extended Abstract) [Abstract] In this paper we are concerned with the problem of inducing recursive Horn clauses from small sets of training examples. The method of iterative bootstrap induction is presented. In the first step, the system generates simple clauses, which can be regarded as properties of the required definition. Properties represent generalizations of the positive examples, simulating the effect of having larger number of examples. Properties are used subsequently to induce the required recursive definitions. This paper describes the method together with a series of experiments. The results support the thesis that iterative bootstrap induction is indeed an effective technique that could be of general use in ILP. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #74975. 	[Title] Towards More Creative Case-Based Design Systems [Abstract] Case-based reasoning (CBR) has a great deal to offer in supporting creative design, particularly processes that rely heavily on previous design experience, such as framing the problem and evaluating design alternatives. However, most existing CBR systems are not living up to their potential. They tend to adapt and reuse old solutions in routine ways, producing robust but uninspired results. Little research effort has been directed towards the kinds of situation assessment, evaluation, and assimilation processes that facilitate the exploration of ideas and the elaboration and redefinition of problems that are crucial to creative design. Also, their typically rigid control structures do not facilitate the kinds of strategic control and opportunism inherent in creative reasoning. In this paper, we describe the types of behavior we would like case-based design systems to support, based on a study of designers working on a mechanical engineering problem. We show how the standard CBR framework should be extended and we describe an architecture we are developing to experiment with these ideas. 1 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107171. 	[Title] A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture [Abstract] Case-based reasoning (CBR) can be used as a form of "caching" solved problems to speedup later problem solving. Using "cached" cases brings additional costs with it due to retrieval time, case adaptation time and also storage space. Simply storing all cases will result in a situation in which retrieving and trying to adapt old cases will take more time (on average) than not caching at all. This means that caching must be applied selectively to build a case memory that is actually useful. This is a form of the utility problem [4, 2]. The approach taken here is to construct a "cost model" of a system that can be used to predict the effect of changes to the system. In this paper we describe the utility problem associated with "caching" cases and the construction of a "cost model". We present experimental results that demonstrate that the model can be used to predict the effect of certain changes to the case memory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22386. 	[Title] CASE-BASED CREATIVE DESIGN [Abstract] Designers across a variety of domains engage in many of the same creative activities. Since much creativity stems from using old solutions in novel ways, we believe that case-based reasoning can be used to explain many creative design processes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3828. 	[Title] A theory of questions and question asking [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #83449. 	[Title] Context-Based Similarity Applied to Retrieval of Relevant Cases [Abstract] Retrieving relevant cases is a crucial component of case-based reasoning systems. The task is to use user-defined query to retrieve useful information, i.e., exact matches or partial matches which are close to query-defined request according to certain measures. The difficulty stems from the fact that it may not be easy (or it may be even impossible) to specify query requests precisely and completely resulting in a situation known as a fuzzy-querying. It is usually not a problem for small domains, but for a large repositories which store various information (multifunctional information bases or a federated databases), a request specification becomes a bottleneck. Thus, a flexible retrieval algorithm is required, allowing for imprecise query specification and for changing the viewpoint. Efficient database techniques exists for locating exact matches. Finding relevant partial matches might be a problem. This document proposes a context-based similarity as a basis for flexible retrieval. Historical bacground on research in similarity assessment is presented and is used as a motivation for formal definition of context-based similarity. We also describe a similarity-based retrieval system for multifunctinal information bases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #198653. 	[Title] Information filtering: Selection mechanisms in learning systems. Machine Learning, 10:113-151, 1993. Generalization as search. Artificial [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153150. 	[Title] Machine Learning and Inference [Abstract] Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: onefor the best representation space, and twofor the best hypothesis in that space. In data-driven constructive induction (DCI), a learning system searches for a better representation space by analyzing the input examples (data). The presented data-driven constructive induction method combines an AQ-type learning algorithm with two classes of representation space improvement operators: constructors, and destructors. The implemented system, AQ17-DCI, has been experimentally applied to a GNP prediction problem using a World Bank database. The results show that decision rules learned by AQ17-DCI outperformed the rules learned in the original representation space both in predictive accuracy and rule simplicity. Please summarise all the given information.

Paper #936 has the title `The Use of Explicit Goals for Knowledge to Guide Inference and Learning  
` and the abstract `Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate explicit desires for knowledge. The question of focus of attention is thereby transformed into two related problems: How can explicit desires for knowledge be used to control inference and facilitate resource-constrained goal pursuit in general? and, Where do these desires for knowledge come from? We present a theory of knowledge goals, or desires for knowledge, and their use in the processes of understanding and learning. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel or unusual newspaper stories, and a differential diagnosis program that improves its accuracy with experience. 
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Question: Which category should Paper #936 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #936 has the title `The Use of Explicit Goals for Knowledge to Guide Inference and Learning  
` and the abstract `Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate explicit desires for knowledge. The question of focus of attention is thereby transformed into two related problems: How can explicit desires for knowledge be used to control inference and facilitate resource-constrained goal pursuit in general? and, Where do these desires for knowledge come from? We present a theory of knowledge goals, or desires for knowledge, and their use in the processes of understanding and learning. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel or unusual newspaper stories, and a differential diagnosis program that improves its accuracy with experience. 
`.
Question: Which category should Paper #936 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