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 #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 #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 #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 #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 #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 #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 #5075. 	[Title] An Investigation of Marker-Passing Algorithms for Analogue Retrieval [Abstract] If analogy and case-based reasoning systems are to scale up to very large case bases, it is important to analyze the various methods used for retrieving analogues to identify the features of the problem for which they are appropriate. This paper reports on one such analysis, a comparison of retrieval by marker passing or spreading activation in a semantic network with Knowledge-Directed Spreading Activation, a method developed to be well-suited for retrieving semantically distant analogues from a large knowledge base. The analysis has two complementary components: (1) a theoretical model of the retrieval time based on a number of problem characteristics, and (2) experiments showing how the retrieval time of the approaches varies with the knowledge base size. These two components, taken together, suggest that KDSA is more likely than SA to be able to scale up to retrieval in large knowledge bases. 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 #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 #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 #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 #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 #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.

Paper #22386 has the title `CASE-BASED CREATIVE DESIGN  
` and the 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. 
`.
Question: Which category should Paper #22386 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #22386 has the title `CASE-BASED CREATIVE DESIGN  
` and the 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. 
`.
Question: Which category should Paper #22386 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