Design and Development of Chatbot Based on Reinforcement Learning

Jadhav, Hemlata Makarand; Mulani, Altaf O.; Jadhav, Makarand

doi:10.1002/9781119861850.ch12

Cited by 19 publications

(6 citation statements)

References 15 publications

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“…By using a system of rewards and penalties, chatbots learn to optimize their responses based on user feedback, gradually improving their conversational abilities and decision-making processes [80]. For instance, Jadhav et al [81] introduced a new method for creating a chatbot system using ML for the academic and industrial sectors. The system facilitates human-machine discussions, identifies sentences, and responds to queries.…”

Section: Learning Algorithms Specific To Chatbotsmentioning

confidence: 99%

“…Algorithmic adjustments, mainly through supervised reinforcement, are crucial in the error correction process of chatbots [81]. These techniques enable chatbots to learn from user interactions and feedback, adjust their decision-making algorithms, and progressively improve their ability to engage in accurate and contextually relevant conversations.…”

Section: Algorithmic Adjustmentsmentioning

confidence: 99%

“…For instance, Jadhav et al [81] investigated the use of RL to improve the responsiveness and adaptability of chatbots in academic settings. The proposed chatbot system integrates traditional NLP techniques for query understanding and response generation.…”

Section: Rl In Chatbotsmentioning

confidence: 99%

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Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots

Izadi,

Forouzanfar

2024

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This study explores the progress of chatbot technology, focusing on the aspect of error correction to enhance these smart conversational tools. Chatbots, powered by artificial intelligence (AI), are increasingly prevalent across industries such as customer service, healthcare, e-commerce, and education. Despite their use and increasing complexity, chatbots are prone to errors like misunderstandings, inappropriate responses, and factual inaccuracies. These issues can have an impact on user satisfaction and trust. This research provides an overview of chatbots, conducts an analysis of errors they encounter, and examines different approaches to rectifying these errors. These approaches include using data-driven feedback loops, involving humans in the learning process, and adjusting through learning methods like reinforcement learning, supervised learning, unsupervised learning, semi-supervised learning, and meta-learning. Through real life examples and case studies in different fields, we explore how these strategies are implemented. Looking ahead, we explore the different challenges faced by AI-powered chatbots, including ethical considerations and biases during implementation. Furthermore, we explore the transformative potential of new technological advancements, such as explainable AI models, autonomous content generation algorithms (e.g., generative adversarial networks), and quantum computing to enhance chatbot training. Our research provides information for developers and researchers looking to improve chatbot capabilities, which can be applied in service and support industries to effectively address user requirements.

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Section: Learning Algorithms Specific To Chatbotsmentioning

confidence: 99%

Section: Algorithmic Adjustmentsmentioning

confidence: 99%

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Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots

Izadi,

Forouzanfar

2024

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“…An artwork analysis and display system based on deep learning represents a cutting-edge approach to understanding and presenting visual art [6]. By leveraging deep learning algorithms, such as convolutional neural networks (CNNs), the system can automatically analyze and interpret various aspects of artworks, including style, composition, and content [7]. This enables the system to extract meaningful features and patterns from images, facilitating the categorization, classification, and annotation of artworks with unprecedented accuracy and efficiency [8].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Natural Language Processing Technology Based Display and Analysis of Modern Artwork

Yongjun Li

2024

jes

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The modern artwork analysis display system, empowered by natural language processing (NLP) technology, revolutionizes the way audiences interact with and understand art. By integrating NLP algorithms, this system offers a dynamic and user-friendly platform for analyzing and displaying artwork. Utilizing NLP, visitors can engage in interactive conversations with the system, asking questions or making inquiries about the artwork on display. The system processes these inquiries, extracting relevant information from curated databases and scholarly sources to provide insightful and context-rich responses. Additionally, NLP algorithms can analyze textual descriptions, artist statements, and critical reviews to offer nuanced interpretations and historical context for each artwork. This paper presents the design and implementation of an innovative modern artwork analysis and display system, leveraging deep learning and natural language processing (NLP) technology, integrated with Multi-Feature Extraction Fuzzy Classification (MFEFC). The system offers a comprehensive platform for analyzing and presenting modern artworks, enhancing user engagement and understanding. Deep learning algorithms are employed to extract high-level features from visual artworks, allowing for automatic recognition of artistic styles, genres, and themes. Concurrently, NLP techniques process textual descriptions, artist biographies, and critical reviews to provide contextual information and interpretative insights. The integration of MFEFC enables precise classification of artworks based on multiple features extracted from both visual and textual sources, facilitating accurate analysis and categorization. Simulation of the NLP techniques demonstrated an average precision of 90% in extracting relevant contextual information from textual descriptions and artist biographies. Furthermore, MFEFC achieved a classification accuracy of 88% in categorizing artworks based on combined visual and textual features.

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“…It utilizes unlabeled data to cluster related information and subsequently labels these data to enhance the learning process [16]. (4) Reinforcement learning: This iterative methodology involves training the chatbot through a trial-anderror process [17]. The chatbot interacts with users, receives feedback on the quality of its responses, and adjusts its behavior based on this feedback.…”

Section: Introduction 1definition and Overview Of Chatbotsmentioning

confidence: 99%

Exploring the Potential of Chatbots in Critical Care Nephrology

Suppadungsuk,

Thongprayoon,

Miao

et al. 2023

Medicines

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The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots’ accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.

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Design and Development of Chatbot Based on Reinforcement Learning

Cited by 19 publications

References 15 publications

Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots

Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots

Deep Learning and Natural Language Processing Technology Based Display and Analysis of Modern Artwork

Exploring the Potential of Chatbots in Critical Care Nephrology

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