Case adaptation

Designing dietary menu planning is a complex problem-solving task. It involves several constraints and extensive common sense. Case-based reasoning (CBR) solves the complexity by storing an expert common sense in the case base. Case adaptation (CA) is important for design task using CBR since old cases are partially similar as a current one. An automatic CA mainly focused on the processing level rather than at the data level. On the other hand, semantic technology (ST) inserts the intelligence features by shifting the focus on the application code to the data. This can leverage the burden on the logical processing of adaptation engine. Ontology is a prerequisite in ST. Thus, this research proposes a computational model of design CA using an ontological approach. This paper discusses the experience we gained during the process of ontology modelling based on the OD101 method. The Malaysian food ontology was successfully developed to make the domain assumptions explicit in supporting the reasoning process of case adaptation for dietary menu planning recommendation.

Taaable is a Case-Based Reasoning (cbr) system that uses a recipe book as a case base to answer cooking queries. Taaable participates in the Computer Cooking Contest since 2008. Its success is due, in particular, to a smart combination of various methods and techniques from knowledge-based systems: cbr, knowledge representation, knowledge acquisition and discovery, knowledge management, and natural language processing. In this chapter, we describe Taaable and its modules. We first present the cbr engine and features such as the retrieval process based on minimal generalization of a query and the different adaptation processes available. Next, we focus on the knowledge containers used by the system. We report on our experiences in building and managing these containers. The Taaable system has been operational for several years and is constantly evolving. To conclude, we discuss the future developments: the lessons that we learned and the possible extensions.

Taaable is a Case-Based Reasoning (cbr) system that uses a recipe book as a case base to answer cooking queries. Taaable participates in the Computer Cooking Contest since 2008. Its success is due, in particular, to a smart combination of various methods and techniques from knowledge-based systems: cbr, knowledge representation, knowledge acquisition and discovery, knowledge management, and natural language processing. In this chapter, we describe Taaable and its modules. We first present the cbr engine and features such as the retrieval process based on minimal generalization of a query and the different adaptation processes available. Next, we focus on the knowledge containers used by the system. We report on our experiences in building and managing these containers. The Taaable system has been operational for several years and is constantly evolving. To conclude, we discuss the future developments: the lessons that we learned and the possible extensions.

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.

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 how to 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.

Abstract. 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. Keywords: case-based reasoning, case adaptation, constraint satisfaction problem, interchangeability

Taaable is a Case-Based Reasoning (cbr) system that uses a recipe book as a case base to answer cooking queries. Taaable participates in the Computer Cooking Contest since 2008. Its success is due, in particular, to a smart combination of various methods and techniques from knowledge-based systems: cbr, knowledge representation, knowledge acquisition and discovery, knowledge management, and natural language processing. In this chapter, we describe Taaable and its modules. We first present the cbr engine and features such as the retrieval process based on minimal generalization of a query and the different adaptation processes available. Next, we focus on the knowledge containers used by the system. We report on our experiences in building and managing these containers. The Taaable system has been operational for several years and is constantly evolving. To conclude, we discuss the future developments: the lessons that we learned and the possible extensions.

This research deals with two important issues in Case-Based Design (CBD): a structure of design cases and a process of design adaptation for handling design failures which is crucial especially in CBD. This paper proposes the structure of design cases consists of problem situation, design specification, design tasks, design solutions, causal explanation, and past design failures as well as design performance. It has been noticed that how to represent a structure of design cases and how it can be used in an actual process of design adaptation process are crucial in CBD paradigm. Also, it suggests an integrative system architecture for design adaptation as well as a model of adaptation process. This paper characterizes various types of design failures for choosing a direction of failure resolution based on causal explanation. Figuring out the strategic approaches of design adaptation process in an analytical way is the major focus of this research adopting CBD paradigm. A model of causal explanation is presented as a useful tool for identifying sources of design failures. Applicability of the suggested adaptation process is demonstrated in an exemplary kitchen layout task.