Human-Computer Speech is gaining momentum as a technique of computer interaction. There has been a recent upsurge in speech based search engines and assistants such as Siri, Google Chrome and Cortana. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to analyse speech, and intelligent responses can be found by designing an engine to provide appropriate human like responses. This type of programme is called a Chatbot, which is the focus of this study. This paper presents a survey on the techniques used to design Chatbots and a comparison is made between different design techniques from nine carefully selected papers according to the main methods adopted. These papers are representative of the significant improvements in Chatbots in the last decade. The paper discusses the similarities and differences in the techniques and examines in particular the Loebner prize-winning Chatbots.

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Survey on Chatbot Design Techniques in Speech Conversation Systems

Background: Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care.
Objective: This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application.
Methods: We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms “conversational agents,” “conversational AI,” “chatbots,” and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis.
Results: The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education.
Conclusions: The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence–driven, and smartphone app–delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.

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Conversational Agents in Health Care: Scoping Review and Conceptual Analysis.

Artificial intelligence (AI) has transformed the world and the relationships among humans as the learning capabilities of machines have allowed for a new means of communication between humans and machines. In the field of health, there is much interest in new technologies that help to improve and automate services in hospitals. This article aims to explore the literature related to conversational agents applied to health care, searching for definitions, patterns, methods, architectures, and data types. Furthermore, this work identifies an agent application taxonomy, current challenges, and research gaps. In this work, we use a systematic literature review approach. We guide and refine this study and the research questions by applying Population, Intervention, Comparison, Outcome, and Context (PICOC) criteria. The present study investigated approximately 4145 articles involving conversational agents in health published over the last ten years. In this context, we finally selected 40 articles based on their approaches and objectives as related to our main subject. As a result, we developed a taxonomy, identified the main challenges in the field, and defined the main types of dialog and contexts related to conversational agents in health. These results contributed to discussions regarding conversational health agents, and highlighted some research gaps for future study.

Survey of conversational agents in health

Opinion Paper: "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Introduction: Benefits from mental health early interventions may not be sustained over time, and longer-term intervention programs may be required to maintain early clinical gains However, due to the high intensity of face-to-face early intervention treatments, this may not be feasible Adjunctive internet-based interventions specifically designed for youth may provide a cost-effective and engaging alternative to prevent loss of intervention benefits However, until now online interventions have relied on human moderators to deliver therapeutic content More sophisticated models responsive to user data are critical to inform tailored online therapy Thus, integration of user experience with a sophisticated and cutting-edge technology to deliver content is necessary to redefine online interventions in youth mental health This paper discusses the development of the moderated online social therapy (MOST) web application, which provides an interactive social media-based platform for recovery in mental health We provide an overview of the system's main features and discus our current work regarding the incorporation of advanced computational and artificial intelligence methods to enhance user engagement and improve the discovery and delivery of therapy content Methods: Our case study is the ongoing Horyzons site (5-year randomized controlled trial for youth recovering from early psychosis), which is powered by MOST We outline the motivation underlying the project and the web application's foundational features and interface We discuss system innovations, including the incorporation of pertinent usage patterns as well as identifying certain limitations of the system This leads to our current motivations and focus on using computational and artificial intelligence methods to enhance user engagement, and to further improve the system with novel mechanisms for the delivery of therapy content to users In particular, we cover our usage of natural language analysis and chatbot technologies as strategies to tailor interventions and scale up the system Conclusions: To date, the innovative MOST system has demonstrated viability in a series of clinical research trials Given the data-driven opportunities afforded by the software system, observed usage patterns, and the aim to deploy it on a greater scale, an important next step in its evolution is the incorporation of advanced and automated content delivery mechanisms

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Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health.

Chatbot (Chatting Robot) is a computer system that allows human to interact with computers using Natural Human Language. This paper intents to present a technical review of five modern chatbot systems, namely, DeepProbe [27], AliMe [19], SuperAgent [4], MILABOT [21] and RubyStar [12]. Review elements will be covered in two general sections: (1) Architectural design; and (2) Implementation process. Architectural design section will review topics surrounding chatbot’s knowledge domain, response generation, text processing and machine learning model, while implementation section will review dataset usage and evaluation strategy topics for each chatbot’s case study. A summarized table of all reviewed elements is presented at the end of this paper together with discussion on our insight regarding the whole review. This paper will conclude with our view on the future roadmap for modern chatbot design.

Modern chatbot systems: a technical review

Problem statement: Artificial intelligence chatbot is a technology th at makes interactions between man and machines using natural language possible. From literature, we found out that in general, chatbot are functions like a typical searc h engine. Although chatbot just produced only one output instead of multiple outputs/results, the bas ic process flow is the same where each time an inpu t is entered, the new search will be done. Nothing re lated to previous output. This research is focused on enabling chatbot to become a search engine that can process the next search with the relation to the previous search output. In chatbot context, this fu nctionality will enhance the capability of chatbot' s input processing. Approach: In attempt to augment the traditional mechanism of chatbot processes, we used the relational database model approach to r edesign the architecture of chatbot in a whole as well as incorporated the algorithm of Extension and Prerequisite (our proposed algorithm). By using this design, we had developed and tested Virtual Di abetes physician (ViDi), a web-based chatbot that function in specific domain of Diabetes education. Results: Extension and prerequisite enabled relations between responses that significantly make it easier for user to chat with chatbot using the same approach as chatting with an actual human. Chatbot can give different responses from the same input given by user according to current conversati on issue. Conclusion: Extension and prerequisite makes chatting with chatbot becomes more likely as chatting with an actual human prior to the relations between responses that produce a response related to the current conversation issue.

Extension and Prerequisite: An Algorithm to Enable Relations Between Responses in Chatbot Technology

Problem statement: Artificial intelligence chatbot is a technology that makes interactions between men and machines using natural language possible. From literature of chatbot’s keywords/pattern matching techniques, potential issues for improvement had been discovered. The discovered issues are in the context of keywords arrangement for matching precedence and keywords variety for matching flexibility. Approach: Combining previous techniques/mechanisms with some additional adjustment, new technique to be used for keywords matching process is proposed. Using newly developed chatbot named ViDi (abbreviation for Virtual Diabetes physician which is a chatbot for diabetes education activity) as a testing medium, the proposed technique named One-Match and All-Match Categories (OMAMC) is being used to test the creation of possible keywords surrounding one sample input sentence. The result for possible keywords created by this technique then being compared to possible keywords created by previous chatbot’s techniques surrounding the same sample sentence in matching precedence and matching flexibility context. Results: OMAMC technique is found to be improving previous matching techniques in matching precedence and flexibility context. This improvement is seen to be useful for shortening matching time and widening matching flexibility within the chatbot’s keywords matching process. Conclusion: OMAMC for keywords matching in chatbot is shown to be an improvement over previous techniques in the context of keywords arrangement for matching precedence and keywords variety for matching flexibility.

One-match and all-match categories for keywords matching in chatbot

Artificial Intelligence chatbot is a technology that makes interaction between man and machine using natural language possible. In this paper, we proposed the architectural design of Virtual Dietition (ViDi), a chatbot that will function as virtual dietitian for diabetic patients. A general a history of a chatbot, a brief description of each chatbots is discussed. We proposed the use of new technique that will be implemented in ViDi as the key component to function as virtual dietitian. In architectural design of ViDi, Vpath is used to remember the conversation path. The architectural design will allow chatbot ViDi to response to the whole conversation as it specifically designed to be a Virtual Dietitian.

An architectural design of Virtual Dietitian (ViDi) for diabetic patients

Chatbot is one of a technology that tried to encounter the question that popped into computer science field in 1950 which is “Can machines think?” [6]. Proposed by mathematician Alan Turing, the question later becomes the pinnacle reference for researchers in artificial intelligence discipline. Turing later also introduces “The Imitation Game” that now known as “Turing Test” where the idea of the test is to examine whether machine can fool a judge into thinking that they are having a conversation with an actual human. The technology back then was great but in rapid evolution of computer science, it can become even better. Evolution is computer scripting language, application design model, and so on, clearly have its advantage towards enabling more complex features in developing a computer program. In this paper, we propose an enhanced algorithm of a chatbot by taking advantages of relational database model to design the whole chatbot architecture that enable several features that cannot or difficult to be done in previous state of computer science programming technique. Started with some literature of a previous developed chatbot, then a detailed description of each new enhanced algorithm together with testing and results from the implementation of these new algorithms that can be used in development of a modern chatbot. These several new algorithms will enable features that will extend chatbot capabilities in responding to the conversation. These algorithm is actually implemented in design and development of chatbot that specifically deal with Bahasa Malaysia language, but taking to account that language in chatbot is really about the data in chatbot knowledge-based, the algorithm is seems transferable wherever it fits into another human language.

Abbas Saliimi Lokman

Papers

Modern chatbot systems: a technical review

Extension and Prerequisite: An Algorithm to Enable Relations Between Responses in Chatbot Technology

One-match and all-match categories for keywords matching in chatbot

An architectural design of Virtual Dietitian (ViDi) for diabetic patients

Chatbot Enhanced Algorithms: A Case Study on Implementation in Bahasa Malaysia Human Language