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Introduction

Dialogue systems are computer-based systems that enable human-computer interaction through natural language conversations. These systems have various applications, such as virtual assistants, customer service chatbots, and language learning tools. Conversational agents, also known as chatbots, are a specific type of dialogue system designed to simulate human-like conversations.

Background of Study

The field of dialogue systems has seen significant advancements in recent years, with the rise of artificial intelligence and natural language processing technologies. Researchers are continuously striving to improve the capabilities of conversational agents, making them more intelligent, context-aware, and user-friendly.

Problem Statement

Despite the progress in dialogue system research, there are still challenges to be addressed. Some common issues include understanding user intent, maintaining context over long conversations, and generating human-like responses. These challenges highlight the need for further research and development in the field of dialogue systems for conversational agents.

Objectives of Study

The main objective of this thesis is to design, implement, and evaluate a dialogue system for conversational agents that addresses the aforementioned challenges. Specifically, we aim to improve the user experience by enhancing the conversational capabilities of the agent through advanced dialogue management techniques.

Limitations of Study

It is important to acknowledge the limitations of this study, such as the complexity of natural language processing and the variability of user input. These limitations may impact the performance of the dialogue system and should be considered in the interpretation of the results.

Scope of Study

This study focuses on developing a dialogue system for conversational agents within the domain of customer service. The system will be designed to handle common customer queries and provide appropriate responses in a timely manner.

Significance of Study

The findings of this study are expected to contribute to the advancement of dialogue systems for conversational agents, particularly in the context of customer service applications. The proposed system may serve as a benchmark for future research in the field and provide valuable insights for developers and practitioners.

Structure of the Thesis

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 History of dialogue systems
2.2 Types of dialogue systems
2.3 Natural language processing techniques
2.4 Dialogue management strategies
2.5 Evaluation metrics for conversational agents
2.6 User experience in dialogue systems
2.7 Challenges in dialogue system research
2.8 Current trends in conversational agents
2.9 Ethical considerations in dialogue system design
2.10 Future directions in dialogue system research

Chapter 3: System Design and Methodology
3.1 System architecture
3.2 Data preprocessing techniques
3.3 Intent recognition algorithms
3.4 Context management strategies
3.5 Response generation models
3.6 Dialogue state tracking methods
3.7 User interface design
3.8 Evaluation methodology

Chapter 4: System Implementation
4.1 Technology stack
4.2 Data collection process
4.3 Model training and optimization
4.4 System integration and deployment
4.5 Testing and validation
4.6 Performance analysis
4.7 Error analysis

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contribution to the field
5.3 Limitations and future work
5.4 Conclusion

Thesis Overview

Dialogue systems for conversational agents have gained significant attention in recent years due to the increasing demand for intelligent virtual assistants and chatbots. This thesis focuses on designing and implementing a dialogue system for conversational agents in the context of customer service applications. The study aims to enhance the conversational capabilities of the agent by addressing key challenges in the field, such as user intent understanding, context management, and response generation.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 reviews the relevant literature on dialogue systems, natural language processing techniques, dialogue management strategies, evaluation metrics, user experience, challenges, trends, ethical considerations, and future directions in the field.

Chapter 3 details the system design and methodology, including the system architecture, data preprocessing, intent recognition, context management, response generation, dialogue state tracking, user interface design, and evaluation methodology. Chapter 4 focuses on the system implementation, covering the technology stack, data collection, model training, integration, deployment, testing, validation, performance analysis, and error analysis.

Chapter 5 concludes the thesis with a summary of findings, contribution to the field, limitations, and future work. Overall, this study aims to contribute to the advancement of dialogue systems for conversational agents and provide insights for developers, researchers, and practitioners in the field.

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