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نتیجه جستجو - Case-based reasoning

تعداد مقالات یافته شده: 10
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1 A knowledge-based risk management tool for construction projects using case-based reasoning
یک ابزار مدیریت ریسک مبتنی بر دانش برای پروژه های ساختمانی با استفاده از استدلال مبتنی بر مورد-2021
Construction projects are often deemed as complex and high-risk endeavours, mostly because of their vulnera- bility to external conditions as well as project-related uncertainties. Risk management (RM) is a critical success factor for companies operating in the construction industry. RM is a knowledge-intensive process that requires effective management of risk-related knowledge. Although some research has already been conducted to develop tools to support knowledge-based RM processes, most of these tools ignore some critical features, such as live knowledge capture, web-based platform for knowledge sharing and effective case retrieval for learning from past projects. Moreover, several RM phases, such as risk identification, analysis, response and monitoring are not usually integrated. Thus, this study aims to bridge these gaps by developing a knowledge-based RM tool (namely, CBRisk) via case-based reasoning (CBR). CBRisk has been developed as a web-based tool that supports the cyclic RM process and utilises an effective case retrieval method considering a comprehensive list of project similarity features in the form of fuzzy linguistic variables. Finally, the developed tool was evaluated and validated by conducting black-box testing and expert review meeting. Results demonstrated that CBRisk has a considerable potential to enhance the effectiveness of RM in construction projects and may be used in other project-based industries with minimal modifications.
keywords: هوش مصنوعی | فراگیری ماشین | مدیریت ریسک مبتنی بر دانش | مدیریت ریسک | مدیریت دانش | استدلال مبتنی بر مورد | ابزار مبتنی بر وب | Artificial intelligence | Machine learning | Knowledge-based risk management | Risk management | Knowledge management | Case-based reasoning | Web-based tool
مقاله انگلیسی
2 DECAF: Deep Case-based Policy Inference for knowledge transfer in Reinforcement Learning
DECAF: استنتاج سیاست های مبتنی بر مورد عمیق برای انتقال دانش در یادگیری تقویتی-2020
Having the ability to solve increasingly complex problems using Reinforcement Learning (RL) has prompted researchers to start developing a greater interest in systematic approaches to retain and reuse knowledge over a variety of tasks. With Case-based Reasoning (CBR) there exists a general methodology that provides a framework for knowledge transfer which has been underrepresented in the RL literature so far. We for- mulate a terminology for the CBR framework targeted towards RL researchers with the goal of facilitating communication between the respective research communities. Based on this framework, we propose the Deep Case-based Policy Inference (DECAF) algorithm to accelerate learning by building a library of cases and reusing them if they are similar to a new task when training a new policy. DECAF guides the train- ing by dynamically selecting and blending policies according to their usefulness for the current target task, reusing previously learned policies for a more effective exploration but still enabling the adaptation to particularities of the new task. We show an empirical evaluation in the Atari game playing domain depicting the benefits of our algorithm with regards to sample efficiency, robustness against negative transfer, and performance increase when compared to state-of-the-art methods.
Keywords: Deep Reinforcement Learning | Case-based Reasoning | Transfer Learning | Knowledge discovery | Knowledge management | Neural networks
مقاله انگلیسی
3 Solving the motion planning problem using learning experience through case-based reasoning and machine learning algorithms
حل مسئله برنامه ریزی حرکت با استفاده از تجربه یادگیری از طریق استدلال مبتنی بر مورد و الگوریتم های یادگیری ماشین-2019
This article presents two novel methodologies for solving the motion planning problem through retained experience. Both approaches employ AI’s case-based reasoning (CBR) technique. Case-based reasoning is an expert system development methodology which reuses past solutions to solve new problems. The first approach uses CBR to retain K similar cases to solve the motion planning problem by merging those solutions into a set. Afterwards, it picks from this set based on a heuristic function to assemble a final solution. Regarding the second approach, it employs the retained K similar cases differently. It uses those solution to build a graph which can be queried using traditional graph search algorithms. Results prove the success of such approaches concerning solution quality and success rate compared to different experience-based algorithms. Such utilization for CBR systems develops new research directions for building systems that can solve NP problems based on retained experiences exclusively.
Keywords: Sampling-based algorithms | Experience-based algorithms | Case-based reasoning Artificial intelligence | Motion planning
مقاله انگلیسی
4 Legal ontologies over time: A systematic mapping study
هستی شناسی های قانونی با گذشت زمان: یک مطالعه نگاشت سیستماتیک-2019
Over the last 30 years, AI & Law has provided breakthroughs in studies involving case-based reasoning, rule-based reasoning, information retrieval and, most recently, conceptual models for knowledge repre- sentation and reasoning, known as Legal Ontologies. Ontologies have been widely used by legal prac- titioners, scholars, and lay people in a variety of situations, such as simulating legal actions, semantic search and indexing, and to keep up-to-date with the continual change of laws and regulations. Given the high number of legal ontologies produced, the need to summarize this research realm through a well-defined methodological procedure is urgent need. This study presents the results of a systematic mapping of the literature, aiming at categorizing legal ontologies along certain dimensions, such as pur- pose, level of generality, underlying legal theories, among other aspects. The reasons to carry out a sys- tematic mapping are twofold: in addition to explaining the maturation of the area over recent decades, it helps to avoid the old problem of reinventing the wheel. Through organizing and classifying what has already been produced, it is possible to realize that the development of legal ontologies can rise to the level of reusability where prefabricated models might be coupled with new and more complex ontologies for practical law.
Keywords: Legal ontology| Systematic mapping study | Legal expert system | Legal theory | Semantic web
مقاله انگلیسی
5 Case-base maintenance of a personalised and adaptive CBR bolus insulin recommender system for type 1 diabetes
نگهداری موردی از یک سیستم توصیه گر انسولین بولوس CBR شخصی و سازگار برای دیابت نوع 1-2019
People with type 1 diabetes must control their blood glucose level through insulin infusion either with several daily injections or with an insulin pump. However, estimating the required insulin dose is not easy. Recommender systems, mainly based on Case-Based Reasoning (CBR), are being developed to provide recommendations to users. These systems are designed to keep the experiences or cases of the user in a case-base, which requires maintenance to keep system’s response accurate and efficient. This paper proposes a case-base maintenance methodology that combines case-base redundancy reduction and attribute weight learning. Contrary to previous approaches designed for classification problems, the maintenance methodology presented in this paper deals with numerical recommendations. It can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. The proposed approach has been tested using the UVA/PADOVA type 1 diabetes simulator and the results demonstrate that it can accomplish better levels of accuracy than other insulin recommender systems mentioned in the literature, when a large number of attributes is considered.
Keywords: Case-based reasoning | Insulin recommender system | Case-base maintenance | Attribute weight learning | Patient empowerment | Diabetes
مقاله انگلیسی
6 Legal ontologies over time: A systematic mapping study
هستی شناسی های قانونی با گذشت زمان: یک مطالعه نگاشت منظم-2019
Over the last 30 years, AI & Law has provided breakthroughs in studies involving case-based reasoning, rule-based reasoning, information retrieval and, most recently, conceptual models for knowledge repre- sentation and reasoning, known as Legal Ontologies. Ontologies have been widely used by legal prac- titioners, scholars, and lay people in a variety of situations, such as simulating legal actions, semantic search and indexing, and to keep up-to-date with the continual change of laws and regulations. Given the high number of legal ontologies produced, the need to summarize this research realm through a well-defined methodological procedure is urgent need. This study presents the results of a systematic mapping of the literature, aiming at categorizing legal ontologies along certain dimensions, such as pur- pose, level of generality, underlying legal theories, among other aspects. The reasons to carry out a sys- tematic mapping are twofold: in addition to explaining the maturation of the area over recent decades, it helps to avoid the old problem of reinventing the wheel. Through organizing and classifying what has already been produced, it is possible to realize that the development of legal ontologies can rise to the level of reusability where prefabricated models might be coupled with new and more complex ontologies for practical law.
Keywords: Legal ontology | Systematic mapping study | Legal expert system | Legal theory | Semantic web
مقاله انگلیسی
7 Ontology-Based Web Service Architecture for Retail Supply Chain Management
هستی شناسی مبتنی بر معماری-وب سرویس برای مدیریت زنجیره تامین خرده فروشی-2018
Service-oriented computing (SOC) technologies provide numerous opportunities and value-added service capabilities that global retail business requires to remain competitive in the market. Initiative to semantic web service provision is playing a crucial role to realize the possibility of heterogenous information systems integration in supply chain. The ability to dynamically discover and invoke a web service is an important aspects of semantic web service-based architecture. An essential part of the service discovery process is the ontology-based semantic web service matchmaking algorithm. This paper presents the key features of an improved matchmaking algorithm to calculate the similarity between concepts on ontology for semantic web service. The matchmaking is taking place in the context of OWL-S (Ontology Web Language for Services) based retail sales management. The paper describes the Semantic Web Service Architecture-II (SWSA-II), which uses a hybrid knowledge-based system; and it consists of Structural Case-Based Reasoning (S-CBR), Rule-Based Reasoning (RBR), and an ontological concept similarity assessment algorithm. Finally, a business scenario is used to demonstrate the functionality of the algorithm.
Keywords: Retail Supply Chain; Service Oriented Computing; Semantic Web Services; Case-Based Reasoning; Rule-Based Reasoning; Ontology Matchmaking Algorithm
مقاله انگلیسی
8 Environmental data stream mining through a case-based stochastic learning approach
جریا کاوی داده های زیست محیطی از طریق یک رویکرد یادگیری تصادفی مبتنی بر مورد-2018
Environmental data stream mining is an open challenge for Data Science. Common methods used are static because they analyze a static set of data, and provide static data-driven models. Environmental systems are dynamic and generate a continuous data stream. Dynamic methods coping with the tem poral nature of data must be provided in Data Science. Our proposal is to model each environmental information unit, timely generated, as a new case/experience in a Case-Based Reasoning (CBR) system. This contribution aims to incrementally build and manage a Dynamic Adaptive Case Library (DACL). In this paper, a stochastic method for the learning of new cases and management of prototypes to create and manage the DACL in an incremental way is introduced. This stochastic method works with two main moments. An evaluation of the method has been carried using a data stream of air quality of the city of Obregon, Sonora. Mexico, with good results. In addition, other datasets have been mined to ensure the generality of the approach.
Keywords: Data science ، Data stream mining ، Dynamic case learning ، Stochastic learning ، Case-based reasoning ، Air quality detection ، Environmental modelling
مقاله انگلیسی
9 A hybrid and learning agent architecture for network intrusion detection
عامل یادگیری معماری ترکیبی برای تشخیص نفوذ شبکه -2017
Learning is an effective way for automating the adaptation of systems to their environment. This ability is especially relevant in dynamic environments as computer networks where new intrusions are constantly emerging, most of them having similarities and occurring frequently. Traditional intrusion detection sys tems still have limitations of adaptability because they are just able to detect intrusions previously set in system design. This paper proposes HyLAA a software agent architecture that combines case-based reasoning, reactive behavior and learning. Through its learning mechanism, HyLAA can adapt itself to its environment and identify new intrusions not previously specified in system design. This is done by learn ing new reactive rules by observing recurrent good solutions to the same perception from the case-based reasoning system, which will be stored in the agent knowledge base. The effectiveness of HyLAA to de tect intrusions using case-based reasoning behavior, the accuracy of the classifier learned by the learning component and both the performance and effectiveness of HyLAA to detect intrusions using hybrid be havior with learning and without learning were evaluated, respectively, by conducting four experiments. In the first experiment, HyLAA exhibited good effectiveness to detect intrusions. In the second experi ment the classifiers learned by the learning component presented high accuracy. Both the hybrid agent behavior with learning and without learning (third and fourth experiment, respectively) presented greater effectiveness and a balance between performance and effectiveness, but only the hybrid behavior showed better effectiveness and performance as long as the agent learns.
Keywords: Learning agents | Hybrid agents | Case-based reasoning | Ontologies | Information security | Intrusion detection systems
مقاله انگلیسی
10 A Multi-Agent Case-Based Reasoning Architecture for Phishing Detection
معماری استنتاجی مبتنی بر مورد چند عاملی برای تشخیص سرقت اطلاعات -2017
Security threats are becoming very sophisticated and pervasive everywhere. Phishing threats in particular has a changeable nature and short life cycle that complicates the detection process. In this paper, we introduce a Multi-Agent System (MAS) as an adaptive intelligent technique that acts on top of distributed Case-Based Reasoning (CBR) Phishing Detection Systems (CBR-PDSs) as a Phishing Detection System Architecture (PDSA) that runs on large scale globally to constitute a robust worldwide Phishing Threat Intelligence (PTI) environment. The global collaborations of PTI introduces a proactive phishing detection technique, quarantines phishing threats via global threats sharing, and minimizes users’ susceptibilities to hard-to-detect spear or advanced phishing attacks. Also, combining two intelligent systems in a unified interactive architecture facilitates the prediction process, increases the accuracy rate, easily tackles the dynamic and changeable behaviors of advanced phishing threats, and minimizes the false negative rate as well. The proposed architecture illustrates the consolidated interaction between intelligent agents and distributed CBR-PDSs in a PTI framework.
Keywords: Phishing Detection | Agents Technology | Case-Based Reasoning | Distributed Systems
مقاله انگلیسی
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