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1999
Case adaptation is a complex problem for which no general method has been found. We consider the restricted domain of problems which can be formulated as constraint satisfaction, and propose a general method using dimensionality reduction based on constraint solving and interchangeability.
1995
Case adaptation, a central component of case-based reasoning, is often considered to be the most difficult part of a casebased reasoning system. The difficulties arise from the fact that adaptation often does not converge, especially if it is not done in a systematic way. This problem, sometimes termed the assimilation problem, is especially pronounced in the case-based design problem solving domain where a large set of constraints and features are processed. Furthermore, in the design domain, multiple cases must be considered in conjunction in order to solve the new problem, resulting in the difficulty of how to efficiently combine the cases into a global solution for the new problem.
2001
While there are many general methods for case retrieval, case adaptation usually requires problem-specific knowledge and it is still an open problem. In this paper we propose a general method for solving case adaptation problems for the large class of problems which can be formulated as Constraint Satisfaction Problems. This method is based on the concept of interchangeability between values in problem solutions. The method is able to determine how change propagates in a solution set and generate a minimal set of choices which need to be changed to adapt an existing solution to a new problem. The paper presents the proposed method, algorithms and test results for a resource allocation domain.
1998
We address in this paper the adaptation of a case when a complete constraint model of the underlying problem is given. The idea is to apply methods from constraint based reasoning that allows the detection of "similar" solutions, which can be used to adapt a selected case to a new situation. We consider applications like configuration where a complete constraint model is available.
2003
In [1] we propose interchangeability based algorithms as methods for solving the case adaptation for the domain of problems which can be expressed as Constraint Satisfaction Problems. In this paper we extend the domain to Soft Constraint Satisfaction Problems and give generic adaptation methods based on soft interchangeability concepts. Many real-life problems require the use of preferences. This need motivates for the use of soft constrains which allows the use of preferences. We have defined interchangeability for soft CSPs in [2] by introducing two notions: (δ/α)substitutability/interchangeability and their algorithms. This paper presents howto build generic adaptation methods based on soft interchangeability. It gives an example of an application of a sales manager system for a car configuration domain and reports test results regarding number of (δ/α)interchangeability in random generated problems, thus number of adaptation alternatives.
The adaptation process is an important and complex step of case-based reasoning (CBR) and is most of the time designed for a specific application. This article presents a domain-independent algorithm for adaptation in CBR. Cases are mapped to a set of numerical descriptors filled with values and local constraint intervals. The algorithm computes every target solution descriptor by combining a source solution, a matching expressed as intervals of variations and dependencies between the source problem and its solution. It determines for every target solution descriptor an interval of the admissible values. In this interval, actual values satisfying global constraints can be chosen. This generic approach to adaptation is operational and introduces general and domain-independent adaptation operators. Therefore, this study is a contribution to the design of a general algorithm for adaptation in CBR. CBR uses past solved cases, called source cases, stored in a case base, in order to solve a new problem, called the target problem and denoted by ¦ ¨ § ©¦ .
2006
Design is a complex open-ended task and it is unreasonable to expect a case-base to contain representatives of all possible designs. Therefore, adaptation is a desirable capability for case-based design systems, but acquiring adaptation knowledge can involve significant effort. In this paper adaptation knowledge is induced separately for different criteria associated with the retrieved solution, using knowledge sources implicit in the case-base. This provides a committee of learners and their combined advice is better able to satisfy design constraints and compatibility requirements compared to a single learner. The main emphasis of the paper is to evaluate the impact of specific-to-general and general-to-specific learning on adaptation knowledge acquired by committee members. For this purpose we conduct experiments on a real tablet formulation problem which is tackled as a decomposable design task. Evaluation results suggest that adaptation achieves significant gains compared to a retrieve-only CBR system, but shows that both learning biases can be beneficial for different decomposed sub-tasks.
Computational Intelligence, 2001
In this article we propose a case-base maintenance methodology based on the idea of transferring knowledge between knowledge containers in a case-based reasoning (CBR) system. A machine-learning technique, fuzzy decision-tree induction, is used to transform the case knowledge to adaptation knowledge. By learning the more sophisticated fuzzy adaptation knowledge, many of the redundant cases can be removed. This approach is particularly useful when the case base consists of a large number of redundant cases and the retrieval efficiency becomes a real concern of the user. The method of maintaining a case base from scratch, as proposed in this article, consists of four steps. First, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case base. Second, clustering of cases is carried out to identify different concepts in the case base using the acquired feature-weights knowledge. Third, adaptation rules are mined for each concept using fuzzy decision trees. Fourth, a selection strategy based on the concepts of case coverage and reachability is used to select representative cases. In order to demonstrate the effectiveness of this approach as well as to examine the relationship between compactness and performance of a CBR system, experimental testing is carried out using the Traveling and the Rice Taste data sets. The results show that the testing case bases can be reduced by 36 and 39 percent, respectively, if we complement the remaining cases by the adaptation rules discovered using our approach. The overall accuracies of the two smaller case bases are 94 and 90 percent, respectively, of the originals.
ArXiv, 2021
The case difference heuristic (CDH) approach is a knowledge-light method for learning case adaptation knowledge from the case base of a case-based reasoning system. Given a pair of cases, the CDH approach attributes the difference in their solutions to the difference in the problems they solve, and generates adaptation rules to adjust solutions accordingly when a retrieved case and new query have similar problem differences. As an alternative to learning adaptation rules, several researchers have applied neural networks to learn to predict solution differences from problem differences. Previous work on such approaches has assumed that the feature set describing problems is predefined. This paper investigates a two-phase process combining deep learning for feature extraction and neural network based adaptation learning from extracted features. Its performance is demonstrated in a regression task on an image data: predicting age given the image of a face. Results show that the combine...
1996
Treating constrained problems with EAs is a very challenging problem. Whether one considers constrained optimization problems or constraint satisfaction problems, the presence of a fitness function (penalty function) reflecting constraint violation is essential. The definition of such a penalty function has a great impact on the GA performance, and it is therefore very important to choose it properly. We show that ad hoc setting of penalties for constraint violations can be circumvented by using self-adaptivity. We illustrate the matter on a discrete CSP, the Zebra problem, and show that the penalties learned by the GA are to a large extent independent of the applied genetic operators as well as the initial constraint weights
Constraints, 2002
Abstract. We consider the Weighted Constraint Satisfaction Problem which is an important problem in Artificial Intelligence. Given a set of variables, their domains and a set of constraints between variables, our goal is to obtain an assignment of the variables to domain values such that ...
Proc. of Third Workshop on Learning …, 2009
Constraint-based problems are hard combinatorial problems and are usually solved by heuristic search methods. In this paper, we consider applying a machine learning approach to improve the performance of these search-based solvers. We apply reinforcement learning in the context of Constraint Satisfaction Problems (CSP) to learn a value function, which results in a novel solving strategy. The motivation underlying this approach is to solve previously unsolvable instances.
1993
The essence of Case-Based Reasoning (CBR) as a problem solving paradigm is that solutions are generated by adapting the solutions of similar problems rather than solving the problem from first principles. In this paper we present a categorisation of problem solving tasks, arranged according to complexity. In addition we categorise CBR systems according to the complexity of the adaptation process involved.
2004
Combinatorial optimization is a powerful paradigm for representing complex problems. It has a wide range of applications such as planning, scheduling, resource sharing, in many domains such as transportation, production, mass marketing, network management, human resources management. Constraint satisfaction techniques provide efficient algorithms to prune search spaces.
2005
Adaptation is one of the most problematic steps in the design and development of Case Based Reasoning (CBR) systems, as it may require considerable domain knowledge and involve complex knowledge engineering tasks. This paper describes a general framework for substitutional adaptation, which only requires analogical domain knowledge, very similar to the one required to define a similarity function. The approach is formally defined, and its applicability is discussed with reference to case structure and its variability. A case study focused on the adaptation of cases related to truck tyre production processes is also presented.
Artificial Intelligence, 2006
Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, the retrieved solution can be reused directly. But for design tasks it is common for the retrieved solution to be regarded as an initial solution that should be refined to reflect the differences between the new and retrieved problems. The acquisition of adaptation knowledge to achieve this refinement can be demanding, despite the fact that the knowledge source of stored cases captures a substantial part of the problem-solving expertise. This paper describes an introspective learning approach where the case knowledge itself provides a source from which training data for the adaptation task can be assembled. Different learning algorithms are explored and the effect of the learned adaptations is demonstrated for a demanding component-based pharmaceutical design task, tablet formulation. The evaluation highlights the incremental nature of adaptation as a further reasoning step after nearest-neighbour retrieval. A new property-based classification to adapt symbolic values is proposed, and an ensemble of these property-based adaptation classifiers has been particularly successful for the most difficult of the symbolic adaptation tasks in tablet formulation.
2001
One advantage of Case-Based Reasoning (CBR) is the relative ease of constructing and maintaining CBR systems, especially as a number of commercial CBR tools are available. However, there are areas of CBR that current tools have not yet addressed. One of these is easing or automating the acquisition of adaptation knowledge. Since tasks like design or planning typically require a significant amount of adaptation, CBR systems for these tasks still do not fully benefit from CBR's promise of reducing the development effort. To address this, we have developed several "knowledge-light" methods for learning adaptation knowledge from the cases in the case-base. These methods perform substitutional adaptation, for both nominal and numerical values, and are suitable for decomposable design problems, in particular formulation and configuration. Tests performed on a tablet formulation domain show promising results. The automatic adaptation methods we present can easily be incorporated in general-purpose CBR tools, thus further contributing to reducing the cost of CBR systems.
1999
The adaptation step is central in case-based reasoning (CBR), because it conditions the obtaining of a solution to a problem. This step is difficult from the knowledge acquisition and engineering points of view. We propose a knowledge level analysis of the adaptation step in CBR using the reasoning task concept. Our proposal is based on the study of several CBR systems for complex applications which imply the adaptation task. Three of them are presented to illustrate our analysis. We sketch from this study a generic model of the adaptation process using the task concept. This model is in conformity with other CBR formal models.
International Journal on Advanced Science, Engineering and Information Technology, 2017
Designing tasks in case-based reasoning requires the use of case adaptation due to its novelty characteristic. In this paper, constraint satisfaction is used to generate potential solutions for design case adaptation. An ontological approach is proposed to perform the inference process to satisfy the multiple design constraints. Domain application is the dietary menu planning for diabetics. Results show that the dietary menu planning designed by the proposed approach is better than the conventional approach. Both the physical and aesthetic constraints were satisfied by the proposed approach.
IEEE Intelligent Systems, 2008
In this article we analyze the current state of case-based plan adaptation research. We include the traditional distinction between transformational and derivational analogy but add further dimensions to classify the field. We present six dimensions for categorizing various aspects of existing case-based plan adaptation algorithms. These dimensions are: the type of transformation, the role of the case, the case content, the use of case merging, the representation formalism, and the computational complexity of the algorithm. We use these dimensions as a framework to compare various systems. Our analysis clarifies some common misconceptions about plan adaptation.
Workshop on Adaptation in Case-Based …
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