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

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

Above are papers related to the following paper: [Paper_ID] Paper #1135115. 	[Title] Rapid learning of binding-match and binding-error detector circuits via long-term potentiation [Abstract] It is argued that the memorization of events and situations (episodic memory) requires the rapid formation of neural circuits responsive to binding errors and binding matches. While the formation of circuits responsive to binding matches can be modeled by associative learning mechanisms, the rapid formation of circuits responsive to binding errors is difficult to explain given their seemingly paradoxical behavior; such a circuit must be formed in response to the occurrence of a binding (i.e., a particular pattern in the input), but subsequent to its formation, it must not fire anymore in response to the occurrence of the very binding (i.e., pattern) that led to its formation. A plausible account of the formation of such circuits has not been offered. A computational model is described that demonstrates how a transient pattern of activity representing an event can lead to the rapid formation of circuits for detecting bindings and binding errors as a result of long-term potentiation within structures whose architecture and circuitry are similar to those of the hippocampal formation, a neural structure known to be critical to episodic memory. The model exhibits a high memory capacity and is robust against limited amounts of diffuse cell loss. The model also offers an alternate interpretation of the functional role of region CA3 in the formation of episodic memories, and predicts the nature of memory impairment that would result from damage to various regions of the hippocampal formation. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #1153933. 	[Title] Scaling-up RAAMs [Abstract] Modifications to Recursive Auto-Associative Memory are presented, which allow it to store deeper and more complex data structures than previously reported. These modifications include adding extra layers to the compressor and reconstructor networks, employing integer rather than real-valued representations, pre-conditioning the weights and pre-setting the representations to be compatible with them. The resulting system is tested on a data set of syntactic trees extracted from the Penn Treebank. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #64271. 	[Title] Generic Teleological Mechanisms and their Use in Case Adaptation [Abstract] In experience-based (or case-based) reasoning, new problems are solved by retrieving and adapting the solutions to similar problems encountered in the past. An important issue in experience-based reasoning is to identify different types of knowledge and reasoning useful for different classes of case-adaptation tasks. In this paper, we examine a class of non-routine case-adaptation tasks that involve patterned insertions of new elements in old solutions. We describe a model-based method for solving this task in the context of the design of physical devices. The method uses knowledge of generic teleological mechanisms (GTMs) such as cascading. Old designs are adapted to meet new functional specifications by accessing and instantiating the appropriate GTM. The Kritik2 system evaluates the computational feasibility and sufficiency of this method for design adaptation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #15889. 	[Title] Structural Similarity as Guidance in Case-Based Design [Abstract] This paper presents a novel approach to determine structural similarity as guidance for adaptation in case-based reasoning (Cbr). We advance structural similarity assessment which provides not only a single numeric value but the most specific structure two cases have in common, inclusive of the modification rules needed to obtain this structure from the two cases. Our approach treats retrieval, matching and adaptation as a group of dependent processes. This guarantees the retrieval and matching of not only similar but adaptable cases. Both together enlarge the overall problem solving performance of Cbr and the explainability of case selection and adaptation considerably. Although our approach is more theoretical in nature and not restricted to a specific domain, we will give an example taken from the domain of industrial building design. Additionally, we will sketch two prototypical implementations of this approach. Please summarise all the given information.

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

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

Above are papers related to the following paper: [Paper_ID] Paper #22431. 	[Title] An investigation of noise-tolerant relational concept learning algorithms [Abstract] We discuss the types of noise that may occur in relational learning systems and describe two approaches to addressing noise in a relational concept learning algorithm. We then evaluate each approach experimentally. Please summarise all the given information.

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

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

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

Above are papers related to the following paper: [Paper_ID] Paper #252715. 	[Title] EMERALD: An Integrated System of Machine Learning and Discovery Programs to Support AI Education and [Abstract] With the rapid expansion of machine learning methods and applications, there is a strong need for computer-based interactive tools that support education in this area. The EMERALD system was developed to provide hands-on experience and an interactive demonstration of several machine learning and discovery capabilities for students in AI and cognitive science, and for AI professionals. The current version of EMERALD integrates five programs that exhibit different types of machine learning and discovery: learning rules from examples, determining structural descriptions of object classes, inventing conceptual clusterings of entities, predicting sequences of objects, and discovering equations characterizing collections of quantitative and qualitative data. EMERALD extensively uses color graphic capabilities, voice synthesis, and a natural language representation of the knowledge acquired by the learning programs. Each program is presented as a "learning robot," which has its own "personality," expressed by its icon, its voice, the comments it generates during the learning process, and the results of learning presented as natural language text and/or voice output. Users learn about the capabilities of each "robot" both by being challenged to perform some learning tasks themselves, and by creating their own similar tasks to challenge the "robot." EMERALD is an extension of ILLIAN, an initial, much smaller version that toured eight major US Museums of Science, and was seen by over half a million visitors. EMERALD's architecture allows it to incorporate new programs and new capabilities. The system runs on SUN workstations, and is available to universities and educational institutions. Please summarise all the given information.

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

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

Above are papers related to the following paper: [Paper_ID] Paper #112813. 	[Title] REPRO: Supporting Flowsheet Design by Case-Base Retrieval [Abstract] Case-Based Reasoning (CBR) paradigm is very close to the designer behavior during the conceptual design, and seems to be a fruitable computer aided-design approach if a library of design cases is available. The goal of this paper is to presents the general framework of a case-based retrieval system: REPRO, that supports chemical process design. The crucial problems like the case representation and structural similarity measure are widely described. The presented experimental results and the expert evaluation shows usefulness of the described system in real world problems. The papers ends with discussion concerning research problems and future work. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #67633. 	[Title] Massively Parallel Support for Case-based Planning [Abstract] In case-based planning (CBP), previously generated plans are stored as cases in memory and can be reused to solve similar planning problems in the future. CBP can save considerable time over planning from scratch (generative planning), thus offering a potential (heuristic) mechanism for handling intractable problems. One drawback of CBP systems has been the need for a highly structured memory that requires significant domain engineering and complex memory indexing schemes to enable efficient case retrieval. In contrast, our CBP system, CaPER, is based on a massively parallel frame-based AI language and can do extremely fast retrieval of complex cases from a large, unindexed memory. The ability to do fast, frequent retrievals has many advantages: indexing is unnecessary; very large casebases can be used; and memory can be probed in numerous alternate ways, allowing more specific retrieval of stored plans that better fit a target problem with less adaptation. fl Preliminary version of an article appearing in IEEE Expert, February 1994, pp. 8-14. This paper is an extended version of [1]. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #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 #1120563. 	[Title] Preparing Case Retrieval Nets for Distributed Processing [Abstract] In this paper, we discuss two approaches of applying the memory model of Case Retrieval Nets to applications where a distributed processing of information is required. For this, we distinguish two types of such applications, namely (a) the case of distributed case libraries and (b) the case of distributed cases. While a solution to the former is straightforward, the latter requires an extension to Case Retrieval Nets which provides a kind of partitioning of the entire net structure. This extended model even allows for a concurrent implementation of the retrieval process or for the use of collaborative agents for retrieval. Keywords: Case-based reasoning, case retrieval, memory structures, distributed processing. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #16437. 	[Title] Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory [Abstract] This work describes an approach for inferring Deterministic Context-free (DCF) Grammars in a Connectionist paradigm using a Recurrent Neural Network Pushdown Automaton (NNPDA). The NNPDA consists of a recurrent neural network connected to an external stack memory through a common error function. We show that the NNPDA is able to learn the dynamics of an underlying pushdown automaton from examples of grammatical and non-grammatical strings. Not only does the network learn the state transitions in the automaton, it also learns the actions required to control the stack. In order to use continuous optimization methods, we develop an analog stack which reverts to a discrete stack by quantization of all activations, after the network has learned the transition rules and stack actions. We further show an enhancement of the network's learning capabilities by providing hints. In addition, an initial comparative study of simulations with first, second and third order recurrent networks has shown that the increased degree of freedom in a higher order networks improve generalization but not necessarily learning speed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12337. 	[Title] A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction [Abstract] Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. dolce consists of a standard recurrent neural net trained by gradient descent and an adaptive clustering technique that quantizes the state space. dolce is based on the assumption that a finite set of discrete internal states is required for the task, and that the actual network state belongs to this set but has been corrupted by noise due to inaccuracy in the weights. dolce learns to recover the discrete state with maximum a posteriori probability from the noisy state. Simulations show that dolce leads to a significant improvement in generalization performance over earlier neural net approaches to FSM induction. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #18619. 	[Title] Heterogeneous Uncertainty Sampling for Supervised Learning [Abstract] Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #5062. 	[Title] A Memory Model for Case Retrieval by Activation Passing [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10183. 	[Title] Concept Learning and Heuristic Classification in Weak-Theory Domains 1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5064. 	[Title] Case Retrieval Nets: Basic Ideas and Extensions [Abstract] An efficient retrieval of a relatively small number of relevant cases from a huge case base is a crucial subtask of Case-Based Reasoning. In this article, we present Case Retrieval Nets (CRNs), a memory model that has recently been developed for this task. The main idea is to apply a spreading activation process to a net-like case memory in order to retrieve cases being similar to a posed query case. We summarize the basic ideas of CRNs, suggest some useful extensions, and present some initial experimental results which suggest that CRNs can successfully handle case bases larger than considered usually in the CBR community. Please summarise all the given information.

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

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

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

Above are papers related to the following paper: [Paper_ID] Paper #5069. 	[Title] Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains [Abstract] This paper presents Objectdirected Case Retrieval Nets, a memory model developed for an application of Case-Based Reasoning to the task of technical diagnosis. The key idea is to store cases, i.e. observed symptoms and diagnoses, in a network and to enhance this network with an object model encoding knowledge about the devices in the application domain. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #28026. 	[Title] Case Retrieval Nets Applied to Large Case Bases [Abstract] This article presents some experimental results obtained from applying the Case Retrieval Net approach to large case bases. The obtained results suggest that CRNs can successfully handle case bases larger than considered in other reports. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20178. 	[Title] Discovery of Physical Principles from Design Experiences [Abstract] One method for making analogies is to access and instantiate abstract domain principles, and one method for acquiring knowledge of abstract principles is to discover them from experience. We view generalization over experiences in the absence of any prior knowledge of the target principle as the task of hypothesis formation, a subtask of discovery. Also, we view the use of the hypothesized principles for analogical design as the task of hypothesis testing, another subtask of discovery. In this paper, we focus on discovery of physical principles by generalization over design experiences in the domain of physical devices. Some important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. We represent a reasoner's comprehension of specific designs in the form of structure-behavior-function (SBF) models. An SBF model provides a functional and causal explanation of the working of a device. We represent domain principles as device-independent behavior-function (BF) models. We show that (i) the function of a device determines what to generalize from its SBF model, (ii) the SBF model itself suggests how far to generalize, and (iii) the typology of functions indicates what method to use. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20179. 	[Title] Use of Mental Models for Constraining Index Learning in Experience-Based Design [Abstract] The power of the case-based method comes from the ability to retrieve the "right" case when a new problem is specified. This implies that learning the "right" indices to a case before storing it for potential reuse is crucial for the success of the method. A hierarchical organization of the case memory raises two distinct but related issues in index learning: learning the indexing vocabulary, and learning the right level of generalization. In this paper we show how the use of structure-behavior-function (SBF) models constrains index learning in the context of experience-based design of physical devices. The SBF model of a design provides the functional and causal explanation of how the structure of the design delivers its function. We describe how the SBF model of a design, together with a specification of the task for which the design case might be reused, provides the vocabulary for indexing the design case in memory. We also discuss how the prior design experiences stored in case-memory help to determine the level of index generalization. The KRITIK2 system implements and evaluates the model-based method for learning indices to design cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20180. 	[Title] Learning Indices for Schema Selection [Abstract] In addition to learning new knowledge, a system must be able to learn when the knowledge is likely to be applicable. An index is a piece of information which, when identified in a given situation, triggers the relevant piece of knowledge (or schema) in the system's memory. We discuss the issue of how indices may be learned automatically in the context of a story understanding task, and present a program that can learn new indices for existing explanatory schemas. We discuss two methods using which the system can identify the relevant schema even if the input does not directly match an existing index, and learn a new index to allow it to retrieve this schema more efficiently in the future. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153877. 	[Title] Adapting Abstract Knowledge [Abstract] For a case-based reasoner to use its knowledge flexibly, it must be equipped with a powerful case adapter. A case-based reasoner can only cope with variation in the form of the problems it is given to the extent that its cases in memory can be efficiently adapted to fit a wide range of new situations. In this paper, we address the task of adapting abstract knowledge about planning to fit specific planning situations. First we show that adapting abstract cases requires reconciling incommensurate representations of planning situations. Next, we describe a representation system, a memory organization, and an adaptation process tailored to this requirement. Our approach is implemented in brainstormer, a planner that takes abstract advice. Please summarise all the given information.

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

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

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

Above are papers related to the following paper: [Paper_ID] Paper #208345. 	[Title] CHIRON: Planning in an Open-Textured Domain [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #55770. 	[Title] The case for cases: a call for purity in case-based reasoning inherently more difficult than [Abstract] A basic premise of case-based reasoning (CBR) is that it involves reasoning from cases, which are representations of real episodes, rather than from rules, which are facts and if then structures with no stated connection to any real episodes. In fact, most CBR systems do not reason directly from cases | rather they reason from abstractions or simplifications of cases. In this paper, we argue for "pure" case-based reasoning, i.e., reasoning from representations that are both concrete and reasonably complete. We claim that working from representations that satisfy these criteria We illustrate our argument with examples from three previous systems, chef, swale, and hypo, as well as from cookie, a CBR system being developed by the first author. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #112099. 	[Title] Massively Parallel Matching of Knowledge Structures [Abstract] As knowledge bases used for AI systems increase in size, access to relevant information is the dominant factor in the cost of inference. This is especially true for analogical (or case-based) reasoning, in which the ability of the system to perform inference is dependent on efficient and flexible access to a large base of exemplars (cases) judged likely to be relevant to solving a problem at hand. In this chapter we discuss a novel algorithm for efficient associative matching of relational structures in large semantic networks. The structure matching algorithm uses massively parallel hardware to search memory for knowledge structures matching a given probe structure. The algorithm is built on top of PARKA, a massively parallel knowledge representation system which runs on the Connection Machine. We are currently exploring the utility of this algorithm in CaPER, a case-based planning system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #97892. 	[Title] Correcting Imperfect Domain Theories: A Knowledge-Level Analysis [Abstract] Explanation-Based Learning [Mitchell et al., 1986; DeJong and Mooney, 1986] has shown promise as a powerful analytical learning technique. However, EBL is severely hampered by the requirement of a complete and correct domain theory for successful learning to occur. Clearly, in non-trivial domains, developing such a domain theory is a nearly impossible task. Therefore, much research has been devoted to understanding how an imperfect domain theory can be corrected and extended during system performance. In this paper, we present a characterization of this problem, and use it to analyze past research in the area. Past characterizations of the problem (e.g, [Mitchell et al., 1986; Rajamoney and DeJong, 1987]) have viewed the types of performance errors caused by a faulty domain theory as primary. In contrast, we focus primarily on the types of knowledge deficiencies present in the theory, and from these derive the types of performance errors that can result. Correcting the theory can be viewed as a search through the space of possible domain theories, with a variety of knowledge sources that can be used to guide the search. We examine the types of knowledge used by a variety of past systems for this purpose. The hope is that this analysis will indicate the need for a "universal weak method" of domain theory correction, in which different sources of knowledge for theory correction can be freely and flexibly combined. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116397. 	[Title] Design, Analogy, and Creativity [Abstract] : Please summarise all the given information.

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

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

Above are papers related to the following paper: [Paper_ID] Paper #221302. 	[Title] Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems [Abstract] We present a framework for task-driven knowledge acquisition in the development of design support systems. Different types of knowledge that enter the knowledge base of a design support system are defined and illustrated both from a formal and from a knowledge acquisition vantage point. Special emphasis is placed on the task-structure, which is used to guide both acquisition and application of knowledge. Starting with knowledge for planning steps in design and augmenting this with problem-solving knowledge that supports design, a formal integrated model of knowledge for design is constructed. Based on the notion of knowledge acquisition as an incremental process we give an account of possibilities for problem solving depending on the knowledge that is at the disposal of the system. Finally, we depict how different kinds of knowledge interact in a design support system. ? This research was supported by the German Ministry for Research and Technology (BMFT) within the joint project FABEL under contract no. 413-4001-01IW104. Project partners in FABEL are German National Research Center of Computer Science (GMD), Sankt Augustin, BSR Consulting GmbH, Munchen, Technical University of Dresden, HTWK Leipzig, University of Freiburg, and University of Karlsruhe. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #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 #1116410. 	[Title] METHOD-SPECIFIC KNOWLEDGE COMPILATION: TOWARDS PRACTICAL DESIGN SUPPORT SYSTEMS [Abstract] Modern knowledge systems for design typically employ multiple problem-solving methods which in turn use different kinds of knowledge. The construction of a heterogeneous knowledge system that can support practical design thus raises two fundamental questions: how to accumulate huge volumes of design information, and how to support heterogeneous design processing? Fortunately, partial answers to both questions exist separately. Legacy databases already contain huge amounts of general-purpose design information. In addition, modern knowledge systems typically characterize the kinds of knowledge needed by specific problem-solving methods quite precisely. This leads us to hypothesize method-specific data-to-knowledge compilation as a potential mechanism for integrating heterogeneous knowledge systems and legacy databases for design. In this paper, first we outline a general computational architecture called HIPED for this integration. Then, we focus on the specific issue of how to convert data accessed from a legacy database into a form appropriate to the problem-solving method used in a heterogeneous knowledge system. We describe an experiment in which a legacy knowledge system called Interactive Kritik is integrated with an ORACLE database using IDI as the communication tool. The limited experiment indicates the computational feasibility of method-specific data-to-knowledge compilation, but also raises additional research issues. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #63486. 	[Title] Evaluation and Selection of Biases in Machine Learning [Abstract] In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias selection as search in bias and meta-bias spaces. Recent research in the field of machine learning bias is summarized. Please summarise all the given information.

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

Paper #20193 has the title `The Structure-Mapping Engine: Algorithm and Examples  
` and the abstract `This paper describes the Structure-Mapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the Structure-mapping theory and describe the design of the engine. We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper appeared in Artificial Intelligence, 41, 1989, pp 1-63. For more information, please contact forbus@ils.nwu.edu 
`.
Question: Which category should Paper #20193 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


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