Case-based Reasoning System Research Articles

OCI-CBR: A hybrid model for decision support in preference-aware investment scenarios

This article proposes an adaptable hybrid model for recommending effective investments in different scenarios. Currently, a wide variety of methodologies are used for company valuation, especially those that take into account financial statements. However, for private held companies, there is no method that would be capable of predicting, with full certainty, the future success of an investment. The Optimal Capital Investment Case-Base Reasoning (OCI-CBR) consists of a case-based reasoning system that uses a classification algorithm to prune the case base according to a projected increase in certain company attributes. Once the cases have been pruned and the case is fed with the most profitable investment opportunities, the case-based reasoning system recommends optimal investments to potential investors. The complete model is conceived as an intelligent hybrid model that optimizes the case base by employing different algorithms for data retrieval and reuse. The system makes recommendations based on the investor’s preferences and the investment decisions of other investors with similar profiles or interests.

Case-Based Reasoning System for Aeroengine Fault Diagnosis Enhanced with Attitudinal Choquet Integral

As the core process of case-based reasoning (CBR), case retrieval is the foundation for CBR success, and the quality of case retrieval depends on the case similarity measure. We improved the CBR system for aeroengine fault diagnosis by embedding the attitudinal Choquet integral (ACI) and 2-order additive measure to consider attribute interactions and decision makers’ attitudes. The enhanced case retrieval method can not only integrate the local similarity, attribute importance, and interaction between attributes, but also incorporate the attitude of the decision maker, thus producing more comprehensive and reasonable global similarity and high-quality recommendations. An experimental study of aeroengine fault diagnosis and comparisons with other similarity aggregation methods were performed to demonstrate the effectiveness of the proposed method.

A dynamic case-based reasoning system for responding to infectious disease outbreaks

Infectious diseases are a global public health problem, which requires timely and effective responses. This study proposes a novel model that contributes to the development of such responses. First, the problem scenario features of infectious disease emergency scenarios are extracted, and the problem scenario is structurally described. A Markov model is adopted to analyze the scenario evolution of the infectious disease outbreaks. Then, a dynamic case-based reasoning model is built. Different matching algorithms are designed for crisp symbols, crisp numbers, interval numbers, and fuzzy linguistic variables. The similarity between the target scenario and various historical scenarios is calculated. Finally, an optimized dynamic emergency decision guide is provided. An experiment is conducted to test the validity and feasibility of the proposed method. The results suggest that the model can realistically simulate the process of infectious disease outbreaks and quickly match the recorded scenarios to generate effective and real-time responses.

A case-based reasoning system to recommend solutions for source water protection: knowledge acquisition and modelling

ABSTRACT “How can drinking water sources be protected, and how can one learn from others who have faced similar problems?” These questions are becoming increasingly relevant in a context where anthropogenic activities threaten natural resources. We describe in this paper a knowledge acquisition and modelling process to support complex decision-making related to source water protection. We conducted online surveys, interviews and applied thematic analysis to gather and structure knowledge from documented and undocumented sources. Our process resulted in structured descriptions of cases that consist of past problems and their implemented solutions. Case design was based on know-how and knowledge needs expressed by water stakeholders. The case base forms the backbone of a knowledge-based recommender system prototype that implements case-based reasoning. It is meant to guide decision-makers in developing solutions based on past experiences. It is a successful application of knowledge management and sharing of lessons learned for decision-making in practice.

An Improved Random Forest Based on Feature Selection and Feature Weighting for Case Retrieval in CBR Systems

: The medical diagnostic process works very similarly to the Case Based Reasoning (CBR) cycle scheme. CBR is a problem solving approach based on the reuse of past experiences called cases. To improve the performance of the retrieval phase, a Random Forest (RF) model is proposed, in this respect we used this algorithm in three different ways (three different algorithms): Classic Random Forest (CRF) algorithm, Random Forest with Feature Selection (RF_FS) algorithm where we selected the most important attributes and deleted the less important ones and Weighted Random Forest (WRF) algorithm where we weighted the most important attributes by giving them more weight. We did this by multiplying the entropy with the weight corresponding to each attribute.We tested our three algorithms CRF, RF_FS and WRF with CBR on data from 11 medical databases and compared the results they produced. We found that WRF and RF_FS give better results than CRF. The experiemental results show the performance and robustess of the proposed approach.

Case-based reasoning system for fault diagnosis of aero-engines

Fault diagnosis in aero-engines typically requires an experienced expert for understanding and detecting the cause of faults. However, accurate and quick identification of fault parts is difficult for maintenance crews owing to the complexity of aero-engines. In this study, we developed a case-based reasoning (CBR) system with a highly accurate novel similarity measure for fault diagnosis of aero-engines by retrieving similar fault cases. The proposed CBR system is established based on 143 cases with the knowledge of correctly diagnosed and successfully resolved aero-engine faults, which constitutes the first tentative case base in the field of aero-engine fault diagnosis. The proposed case similarity measure for fault diagnosis of aero-engines integrates three local similarity measures associated with different attributes, especially among which a tree-based semantic similarity measure is proposed to define the relationship between the fault part and fault mode based on a semantic graph incorporated into the aero-engine tree structure. The proposed CBR system is evaluated using the k-nearest neighbors algorithm and 5-fold cross-validation. The system exhibited high retrieval accuracy with all cases collected from real-world scenarios of aero-engine fault diagnosis. Our study shows great promise that the experience-based decisions yielded from the results can aid in aero-engine maintenance and support services.

Using Soft Sets for a Parametric Assessment of Problem Solving Skills

Volumes of research have been written about problem solving, which is one of the most important components of the human cognition that affects our lives for ages. In this work soft sets are used as means for obtaining a qualitative assessment of problem solving skills and of Case-Based Reasoning systems’ performance. The concept of soft set is a generalization of Zadeh’s fuzzy sets introduced by Molodstov in 1999 as a new mathematical tool for dealing with the existing in real world uncertainty in a parametric manner.

Accelerated quality improvement of 3D printed objects based on a case-based reasoning system

Accelerated quality improvement of 3D printed objects based on a case-based reasoning system

A new case-deletion strategy for case-base maintenance based on K-means Clustering Algorithm applied to medical data

Case-Based Reasoning (CBR) system maintenance is an important issue for current medical systems research. Large-scale CBR systems are becoming more omnipresent, with immense case libraries consisting of millions of cases. Case-Base Maintenance (CBM) is the implementation of the following policies allowing to revise the organization and/or the content (information content, representation field of application, or the implementation) of the Case Base (CB) to improve future thinking. Diverse case-base deletion and addition policies have been proposed which claim to preserve case-base competence. This paper presents a novel clustering-based deletion policy for CBM that exploits the K-means clustering algorithm. Thus, CBM becomes a central subject whose objective is to guarantee the quality of the CB and improve the performance of CBM. The proposed approach exploited clustering, which groups similar cases using the K-means algorithm. We rely on the characterization made of the different cases in the CB, and we find this characterization by a method based on a criterion of competence and performance. From this categorization, case deletion becomes obvious. This quality depends on the competence and performance of the CB. Test results show that the proposed deletion strategy improved the efficiency of the CB while preserving competence.Furthermore, its performance was 13% more reliable. The effectiveness of the proposed approach examined on the medical databases and its performance has been compared with the existing approaches on deletion policy. Experimental results are very encouraging.

F-CBR: An Architecture for Federated Case-Based Reasoning

Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attempts to solve new problems using past experiences known as cases. Experiences collected in a single case base from an institution or geographical region are seldom sufficient to solve diverse problems, especially in rare situations. Additionally, many institutions do not promote peer-to-peer (p2p) communication or encourage data sharing through such networks to retain autonomy. The paper proposes a federated CBR (F-CBR) architecture to address these challenges. F-CBR enables solving new problems based on similar cases from multiple autonomous CBR systems without p2p communication. We also designed an algorithm to minimize (irrelevant or unsolicited) data sharing in an F-CBR system. We extend the F-CBR design to support institutions with organizational or geographical hierarchies. The F-CBR architecture was implemented and evaluated on two public datasets and a private real-world (non-specific musculoskeletal disorder patient) dataset. The findings demonstrate that the retrieval quality of F-CBR systems is comparable to or better than a single CBR system that persists all the cases on a centralized case base. F-CBR systems address data privacy by incorporating the data minimization principle. We foresee F-CBR as a viable real-world design that can aid in federating legacy CBR systems with minimal or no changes. The CBR systems used in this study are shared on GitHub to support reproducibility.

A novel hybrid recommender system approach for student academic advising named COHRS, supported by case-based reasoning and ontology

The recent development of theWorldWideWeb, information, and communications technology have transformed the world and moved us into the data era resulting in an overload of data analysis. Students at high school use, most of the time, the internet as a tool to search for universities/colleges, university?s majors, and career paths that match their interests. However, selecting higher education choices such as a university major is a massive decision for students leading them, to surf the internet for long periods in search of needed information. Therefore, the purpose of this study is to assist high school students through a hybrid recommender system (RS) that provides personalized recommendations related to their interests. To reach this purpose we proposed a novel hybrid RS approach named (COHRS) that incorporates the Knowledge base (KB) and Collaborative Filtering (CF) recommender techniques. This hybrid RS approach is supported by the Casebased Reasoning (CBR) system and Ontology. Hundreds of queries were processed by our hybrid RS approach. The experiments show the high accuracy of COHRS based on two criteria namely the ?accuracy of retrieving the most similar cases? and the ?accuracy of generating personalized recommendations?. The evaluation results show the percentage of accuracy of COHRS based on many experiments as follows: 98 percent accuracy for ?retrieving the most similar cases? and 95 percent accuracy for ?generating personalized recommendations?.

An Automatic Adaptive Case-based Reasoning System for Depression Remedy Recommendation

Social media data represents the fuel for advanced analytics concerning people’s behaviors, physiological and health status. These analytics include identifying users’ depression levels via Twitter and then recommend remedies. Remedies come in the form of suggesting some accounts to follow, displaying motivational quotes, or even recommending a visit to a psychiatrist. This paper proposes a remedy recommendation system which exploits case-based reasoning (CBR) with random forest. The system recommends the appropriate remedy for a person. The main contribution of this work is the creation of an automated, data-driven, and scalable adaptation module without human interference. The results of every stage of the system were verified by certified psychiatrist. Another contribution of this work is setting the weights in case similarity measurement by the features’ importance, extracted from the depression identification system. CBR retrieval accuracy (exact hit) reached 82% while the automatic adaptation accuracy (exact remedy) reached 88%. The adaptation presented an error-tolerance advantage which enhances the overall accuracy.

Knowledge-based Implementation of Deep Reinforcement Learning Agents in Assembly

Robotic systems based on Deep Reinforcement Learning have shown great potential to enable assembly systems with higher flexibility and robustness. This paper presents a concept of a Case-Based Reasoning system to automate the implementation process, based on the assumption that similar assembly tasks have similar solutions as used as heuristics in the current manual procedure. For retrieving similar cases a digital description of the assembly task and a method to measure the similarity is introduced. The retrieved cases are then used to warmstart a Bayesian Hyperparameter Optimization. The approach is evaluated on two simulated robot task.

Identification of Coronary Artery Disease using Artificial Neural Network and Case-Based Reasoning

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

Dynamic Expansion of Behaviour Trees

Artificial intelligence in games is typically used for creating player's opponents. Manual edition of intelligent behaviors for Non-Player Characters (NPCs) of games is a cumbersome task that needs experienced designers. Our research aims to assist designers in this task. Behaviours typically use recurring patterns, so that experience and reuse are crucial aspects for behavior design. The use of hierarchical state machines allows working on different abstraction levels, sharing transitions and reusing pieces from the more detailed levels. However, the static nature of the design process does not release the designer from the burden to completely specify each behaviour. Our approach applies Case-Based Reasoning (CBR) techniques to retrieve and reuse stored behaviors represented as hierarchical state machines (actually, behaviour trees). In this paper we focus on dynamic retrieval of behaviours taking into account the world state and the underlying goals to select the most appropriate state machine to guide the NPC behaviour. The global behaviour of the NPC is dynamically built in run time querying the CBR system. We exemplify our approach through a serious game, developed by our research group, with gameplay elements from First-Person Shooter (FPS) games.

Explaining Deep Learning using examples: Optimal feature weighting methods for twin systems using post-hoc, explanation-by-example in XAI

In this paper, the twin-systems approach is reviewed, implemented, and competitively tested as a post-hoc explanation-by-example solution to the eXplainable Artificial Intelligence (XAI) problem. In twin-systems, an opaque artificial neural network (ANN) is explained by “twinning” it with a more interpretable case-based reasoning (CBR) system, by mapping the feature weights from the former to the latter. Extensive comparative tests are performed, over four experiments, to determine the optimal feature-weighting method for such twin-systems. Twin-systems for traditional multilayer perceptron (MLP) networks (MLP–CBR twins), convolutional neural networks (CNNs; CNN–CBR twins), and transformers for NLP (BERT–CBR twins) are examined. In addition, Feature Activation Maps (FAMs) are explored to enhance explainability by providing an additional layer of explanatory insight. The wider implications of this research on XAI is discussed, and a code library is provided to ease replicability.

Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability

Background: Case-Based Reasoning (CBR) is a problem-solving paradigm that uses knowledge of relevant past experiences (cases) to interpret or solve new problems. CBR systems allow generating explanations easily, as they typically organize and represent knowledge in a way that makes it possible to reason about and thereby generate explanations. An improvement of this paradigm is ontology-based CBR, an approach that combines, in the form of formal ontologies, case-specific knowledge with domain one in order to improve the effectiveness and explanation capability of the system. Intelligent systems make daily activities more easily, efficiently, and represent a real support for sustainable economic development. On the one hand, they improve efficiency, productivity, and quality, and, on the other hand, can reduce costs and cut waste. In this way, intelligent systems facilitate sustainable development, economic growth, societal progress, and improve efficiency. Aim: In this vision, the purpose of this paper is to propose a new generation of intelligent decision support systems for Business Model having the ability to provide explanations to increase confidence in proposed solutions. Findings/result: The performance results obtained show the benefits of the proposed solution with different requirements of an explanatory decision support system. Consequently, applying this paradigm for software tools of business model development will make a great promise for supporting business model design, sustainability, and innovation.

Ontology Engineering for Gastric Dystemperament in Persian Medicine.

Developing an ontology can help collecting and sharing information in traditional medicine including Persian medicine in a well-defined format. The present study aimed to develop an ontology for gastric dystemperament in the Persian medicine. This was a mixed-methods study conducted in 2019. The first stage was related to providing an ontology requirements specification document. In the second stage, important terms, concepts, and their relationships were identified via literature review and expert panels. Then, the results derived from the second stage were refined and validated using the Delphi method in three rounds. Finally, in the fourth stage, the ontology was evaluated in terms of consistency and coherence. In this study, 241 concepts related to different types of gastric dystemperament, diagnostic criteria, and treatments in the Persian medicine were identified through literature review and expert panels, and 12 new concepts were suggested during the Delphi study. In total, after performing three rounds of the Delphi study, 233 concepts were identified. Finally, an ontology was developed with 71 classes, and the results of the evaluation study revealed that the ontology was consistent and coherent. In this study, an ontology was created for gastric dystemperament in the Persian medicine. This ontology can be used for designing future systems, such as case-based reasoning and expert systems. Moreover, the use of other evaluation methods is suggested to construct a more complete and precise ontology.

Case-based reasoning system for prediction of fuel consumption by haulage trucks in open-pit mines

The shovel-truck system is commonly used in open-pit mining operations. Truck haulage cost constitutes about 26% of open-pit mining costs as the trucks are mostly powered by diesel whose cost is escalating annually. Therefore, reducing fuel consumption could lead to a significant decrease in overall mining costs. Various methods have been proposed to improve fuel efficiency in open-pit mines. Case-based reasoning (CBR) can be used to estimate fuel consumption by haulage trucks. In this work, CBR methods namely case-based reasoning using forward sequential selection (CBR-FSS), traditional CBR, and Naïve techniques were used to predict fuel consumption by trucks operating at Orapa Mine. The results show that the CBR method can be used to predict fuel consumption by trucks in open-pit mines; the predicted values of fuel consumption using the CBR-FSS technique gave much lower absolute residual values, higher standardised accuracy values, and effect sizes than those of other prediction techniques on all the datasets used. The system will enable mine planners to know the fuel consumed per trip and allow them to take mitigation measures on trucks with high fuel consumption.

Emergency Management Case-Based Reasoning Systems: A Survey of Recent Developments

ABSTRACT With the frequent occurrence of natural and man-made disasters, emergency management has become an active research field aiming at saving lives and reducing environmental and economic losses. Due to the complexity of crisis situations, emergency managers need to be assisted in making critical and effective decisions. Case-based reasoning (CBR) methodology has been widely adopted to support emergency decision makers in their tasks. This paper presents a comprehensive literature review of recent emergency management CBR systems reported in peer-reviewed journals and ICCBR conference proceedings between 2000 and 2020. Recent development trends of emergency management CBR systems are identified in terms of their purposes, application contexts and techniques used for their development. Finally, opportunities to improve emergency management CBR systems are outlined.