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 #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 #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 #3097. 	[Title] Towards A Computer Model of Memory Search Strategy Learning [Abstract] Much recent research on modeling memory processes has focused on identifying useful indices and retrieval strategies to support particular memory tasks. Another important question concerning memory processes, however, is how retrieval criteria are learned. This paper examines the issues involved in modeling the learning of memory search strategies. It discusses the general requirements for appropriate strategy learning and presents a model of memory search strategy learning applied to the problem of retrieving relevant information for adapting cases in case-based reasoning. It discusses an implementation of that model, and, based on the lessons learned from that implementation, points towards issues and directions in refining the model. 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 #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 #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 #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 #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 #170798. 	[Title] Learning Problem-Solving Concepts by Reflecting on Problem Solving [Abstract] Learning and problem solving are intimately related: problem solving determines the knowledge requirements of the reasoner which learning must fulfill, and learning enables improved problem-solving performance. Different models of problem solving, however, recognize different knowledge needs, and, as a result, set up different learning tasks. Some recent models analyze problem solving in terms of generic tasks, methods, and subtasks. These models require the learning of problem-solving concepts such as new tasks and new task decompositions. We view reflection as a core process for learning these problem-solving concepts. In this paper, we identify the learning issues raised by the task-structure framework of problem solving. We view the problem solver as an abstract device, and represent how it works in terms of a structure-behavior-function model which specifies how the knowledge and reasoning of the problem solver results in the accomplishment of its tasks. We describe how this model enables reflection, and how model-based reflection enables the reasoner to adapt its task structure to produce solutions of better quality. The Autognostic system illustrates this reflection process. 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 #38839. 	[Title] Role of Ontology in Creative Understanding [Abstract] In Proceedings of the 18th Annual Cognitive Science Conference, San Diego, CA, July 1996 This paper can also be found at the Georgia Tech WWW site: http://www.cc.gatech.edu/cogsci/ Abstract Successful creative understanding requires that a reasoner be able to manipulate known concepts in order to understand novel ones. A major problem arises, however, when one considers exactly how these manipulations are to be bounded. If a bound is imposed which is too loose, the reasoner is likely to create bizarre understandings rather than useful creative ones. On the other hand, if the bound is too tight, the reasoner will not have the flexibility needed to deal with a wide range of creative understanding experiences. Our approach is to make use of a principled ontology as one source of reasonable bounding. This allows our creative understanding theory to have good explanatory power about the process while allowing the computer implementation of the theory (the ISAAC system) to be flexible without being bizarre in the task domain of reading science fiction short stories. 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 #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 #1153728. 	[Title] Abstract [Abstract] Metacognition addresses the issues of knowledge about cognition and regulating cognition. We argue that the regulation process should be improved with growing experience. Therefore mental models are needed which facilitate the re-use of previous regulation processes. We will satisfy this requirement by describing a case-based approach to Introspection Planning which utilises previous experience obtained during reasoning at the meta-level and at the object level. The introspection plans used in this approach support various metacognitive tasks which are identified by the generation of self-questions. As an example of introspection planning, the metacognitive behaviour of our system, IULIAN, is described. 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 #1128407. 	[Title] Understanding Creativity: A Case-Based Approach [Abstract] Dissatisfaction with existing standard case-based reasoning (CBR) systems has prompted us to investigate how we can make these systems more creative and, more broadly, what would it mean for them to be more creative. This paper discusses three research goals: understanding creative processes better, investigating the role of cases and CBR in creative problem solving, and understanding the framework that supports this more interesting kind of case-based reasoning. In addition, it discusses methodological issues in the study of creativity and, in particular, the use of CBR as a research paradigm for exploring creativity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3932. 	[Title] Finding Analogues for Innovative Design [Abstract] Knowledge Systems Laboratory March 1995 Report No. KSL 95-32 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #82664. 	[Title] Learning Adaptation Strategies by Introspective Reasoning about Memory Search [Abstract] In case-based reasoning systems, the case adaptation process is traditionally controlled by static libraries of hand-coded adaptation rules. This paper proposes a method for learning adaptation knowledge in the form of adaptation strategies of the type developed and hand-coded by Kass [90] . Adaptation strategies differ from standard adaptation rules in that they encode general memory search procedures for finding the information needed during case adaptation; this paper focuses on the issues involved in learning memory search procedures to form the basis of new adaptation strategies. It proposes a method that starts with a small library of abstract adaptation rules and uses introspective reasoning about the system's memory organization to generate the memory search plans needed to apply those rules. The search plans are then packaged with the original abstract rules to form new adaptation strategies for future use. This process allows a CBR system not only to learn about its domain, by storing the results of case adaptation, but also to learn how to apply the cases in its memory more effectively. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #73327. 	[Title] Modeling Case-based Planning for Repairing Reasoning Failures [Abstract] One application of models of reasoning behavior is to allow a reasoner to introspectively detect and repair failures of its own reasoning process. We address the issues of the transferability of such models versus the specificity of the knowledge in them, the kinds of knowledge needed for self-modeling and how that knowledge is structured, and the evaluation of introspective reasoning systems. We present the ROBBIE system which implements a model of its planning processes to improve the planner in response to reasoning failures. We show how ROBBIE's hierarchical model balances model generality with access to implementation-specific details, and discuss the qualitative and quantitative measures we have used for evaluating its introspective component. 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.

Paper #310530 has the title `Integrating Creativity and Reading: A Functional Approach  
` and the 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. 
`.
Question: Which category should Paper #310530 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #310530 has the title `Integrating Creativity and Reading: A Functional Approach  
` and the 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. 
`.
Question: Which category should Paper #310530 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