Above are papers related to the following paper: [Paper_ID] Paper #51879. 	[Title] Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22883. 	[Title] On the Informativeness of the DNA Promoter Sequences Domain Theory [Abstract] The DNA promoter sequences domain theory and database have become popular for testing systems that integrate empirical and analytical learning. This note reports a simple change and reinterpretation of the domain theory in terms of M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on the 106 items of the database. Moreover, an exhaustive search of the space of M-of-N domain theory interpretations indicates that the expected accuracy of a randomly chosen interpretation is 76.5%, and that a maximum accuracy of 97.2% is achieved in 12 cases. This demonstrates the informativeness of the domain theory, without the complications of understanding the interactions between various learning algorithms and the theory. In addition, our results help characterize the difficulty of learning using the DNA promoters theory. Please summarise all the given information.

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

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

Above are papers related to the following paper: [Paper_ID] Paper #1114664. 	[Title] Knowledge Based Systems [Abstract] Technical Report No. 95/2 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #173884. 	[Title] Revising Bayesian Network Parameters Using Backpropagation [Abstract] The problem of learning Bayesian networks with hidden variables is known to be a hard problem. Even the simpler task of learning just the conditional probabilities on a Bayesian network with hidden variables is hard. In this paper, we present an approach that learns the conditional probabilities on a Bayesian network with hidden variables by transforming it into a multi-layer feedforward neural network (ANN). The conditional probabilities are mapped onto weights in the ANN, which are then learned using standard backpropagation techniques. To avoid the problem of exponentially large ANNs, we focus on Bayesian networks with noisy-or and noisy-and nodes. Experiments on real world classification problems demonstrate the effectiveness of our technique. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #502574. 	[Title] References Automatic student modeling and bug library construction using theory refinement. Ph.D. ml/ Symbolic revision [Abstract] ASSERT demonstrates how theory refinement techniques developed in machine learning can be used to ef fec-tively build student models for intelligent tutoring systems. This application is unique since it inverts the normal goal of theory refinement from correcting errors in a knowledge base to introducing them. A comprehensive experiment involving a lar ge number of students interacting with an automated tutor for teaching concepts in C ++ programming was used to evaluate the approach. This experiment demonstrated the ability of theory refinement to generate more accurate student models than raw induction, as well as the ability of the resulting models to support individualized feedback that actually improves students subsequent performance. Carr, B. and Goldstein, I. (1977). Overlays: a theory of modeling for computer aided instruction. T echnical Report A. I. Memo 406, Cambridge, MA: MIT. Sandberg, J. and Barnard, Y . (1993). Education and technology: What do we know? And where is AI? Artificial Intelligence Communications, 6(1):47-58. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27535. 	[Title] Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases [Abstract] This paper describes Rapture | a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. Rapture uses a modified version of backpropagation to refine the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on refining three actual expert knowledge bases demonstrate that this combined approach generally performs better than previous methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #34961. 	[Title] Comparing Methods for Refining Certainty-Factor Rule-Bases [Abstract] This paper compares two methods for refining uncertain knowledge bases using propositional certainty-factor rules. The first method, implemented in the Rapture system, employs neural-network training to refine the certainties of existing rules but uses a symbolic technique to add new rules. The second method, based on the one used in the Kbann system, initially adds a complete set of potential new rules with very low certainty and allows neural-network training to filter and adjust these rules. Experimental results indicate that the former method results in significantly faster training and produces much simpler refined rule bases with slightly greater accuracy. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4274. 	[Title] Multi-Strategy Learning and Theory Revision [Abstract] This paper presents the system WHY, which learns and updates a diagnostic knowledge base using domain knowledge and a set of examples. The a-priori knowledge consists of a causal model of the domain, stating the relationships among basic phenomena, and a body of phenomenological theory, describing the links between abstract concepts and their possible manifestations in the world. The phenomenological knowledge is used deductively, the causal model is used abductively and the examples are used inductively. The problems of imperfection and intractability of the theory are handled by allowing the system to make assumptions during its reasoning. In this way, robust knowledge can be learned with limited complexity and limited number of examples. The system works in a first order logic environment and has been applied in a real domain. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #6741. 	[Title] LEARNING CONCEPTS BY ASKING QUESTIONS [Abstract] Tw o important issues in machine learning are explored: the role that memory plays in acquiring new concepts; and the extent to which the learner can take an active part in acquiring these concepts. This chapter describes a program, called Marvin, which uses concepts it has learned previously to learn new concepts. The program forms hypotheses about the concept being learned and tests the hypotheses by asking the trainer questions. Learning begins when the trainer shows Marvin an example of the concept to be learned. The program determines which objects in the example belong to concepts stored in the memory. A description of the new concept is formed by using the information obtained from the memory to generalize the description of the training example. The generalized description is tested when the program constructs new examples and shows these to the trainer, asking if they belong to the target concept. Please summarise all the given information.

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

Above are papers related to the following paper: [Paper_ID] Paper #22876. 	[Title] A Case-Based Reasoning Approach [Abstract] The AAAI Fall Symposium; Flexible Computation in Intelligent Systems: Results, Issues, and Opportunities. Nov. 9-11, 1996, Cambridge, MA Abstract This paper presents a case-based reasoning system TA3. We address the flexibility of the case-based reasoning process, namely flexible retrieval of relevant experiences, by using a novel similarity assessment theory. To exemplify the advantages of such an approach, we have experimentally evaluated the system and compared its performance to the performance of non-flexible version of TA3 and to other machine learning algorithms on several domains. 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 #51879 has the title `Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases  
` and the abstract `nan`.
Question: Which category should Paper #51879 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #51879 has the title `Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases  
` and the abstract `nan`.
Question: Which category should Paper #51879 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: Probabilistic_Methods