This project details the development of a novel and practical framework for the development of conversation agents (CAs), or conversation robots. CAs, are software programs which can be used to provide a natural interface between human and computers. In this study, ‘conversation’ refers to real-time dialogue exchange between human and machine which may range from web chatting to “on-the-go” conversation through mobile devices. In essence, the project proposes a “smart and effective” communication technology where an autonomous agent is able to carry out simulated human conversation via multiple channels. The CA developed in this project is termed “Artificial Intelligence Natural-language Identity” (AINI) and AINI is used to illustrate the implementation and testing carried out in this project. Up to now, most CAs have been developed with a short term objective to serve as tools to convince users that they are talking with real humans as in the case of the Turing Test. The traditional designs have mainly relied on ad-hoc approach and hand-crafted domain knowledge. Such approaches make it difficult for a fully integrated system to be developed and modified for other domain applications and tasks. The proposed framework in this thesis addresses such limitations. Overcoming the weaknesses of previous systems have been the key challenges in this study. The research in this study has provided a better understanding of the system requirements and the development of a systematic approach for the construction of intelligent CAs based on agent architecture using a modular N-tiered approach. This study demonstrates an effective implementation and exploration of the new paradigm of Computer Mediated Conversation (CMC) through CAs. The most significant aspect of the proposed framework is its ability to re-use and encapsulate expertise such as domain knowledge, natural language query and human-computer interface through plug-in components. As a result, the developer does not need to change the framework implementation for different applications. This proposed system provides interoperability among heterogeneous systems and it has the flexibility to be adapted for other languages, interface designs and domain applications. A modular design of knowledge representation facilitates the creation of the CA knowledge bases. This enables easier integration of open-domain and domain-specific knowledge with the ability to provide answers for broader queries. In order to build the knowledge base for the CAs, this study has also proposed a mechanism to gather information from commonsense collaborative knowledge and online web documents. The proposed Automated Knowledge Extraction Agent (AKEA) has been used for the extraction of unstructured knowledge from the Web. On the other hand, it is also realised that it is important to establish the trustworthiness of the sources of information. This thesis introduces a Web Knowledge Trust Model (WKTM) to establish the trustworthiness of the sources. In order to assess the proposed framework, relevant tools and application modules have been developed and an evaluation of their effectiveness has been carried out to validate the performance and accuracy of the system. Both laboratory and public experiments with online users in real-time have been carried out. The results have shown that the proposed system is effective. In addition, it has been demonstrated that the CA could be implemented on the Web, mobile services and Instant Messaging (IM). In the real-time human-machine conversation experiment, it was shown that AINI is able to carry out conversations with human users by providing spontaneous interaction in an unconstrained setting. The study observed that AINI and humans share common properties in linguistic features and paralinguistic cues. These human-computer interactions have been analysed and contributed to the understanding of how the users interact with CAs. Such knowledge is also useful for the development of conversation systems utilising the commonalities found in these interactions. While AINI is found having difficulties in responding to some forms of paralinguistic cues, this could lead to research directions for further work to improve the CA performance in the future.

Yes / Building on the perspectives of the uses & gratification (U&G) theory and stimulus-organism-response (S-O-R) model, this article develops and tests an integrative framework to examine the underlying factors influencing customers’ experiences with chatbots as a form of virtual conversational agent (VCA) in the UK and Vietnam. In addition to utilitarian and hedonic factors, anthropomorphism and social presence are also investigated, which are considered important experiential dimensions in a customer-machine relationship. We also explore how stimuli such as functionality, communication style similarity, and aesthetics indirectly affect outcomes like customer satisfaction and reuse intention, mediated by four types of customer experiences. Data collected from a sample of 417 and 359 participants in the UK and Vietnam respectively revealed that, in general, perceived informativeness, credibility, enjoyment, functionality, and communication style similarity are crucial for customer satisfaction in both countries. Interesting differences in the effects of customer experience between developed and developing countries were observed. For instance, the effects of anthropomorphism and social presence on satisfaction are only effective for customers from developed country, while those from developing country only need information provided by chatbots be transparent. Our findings offer a novel way to understand customer experience with chatbots and provide important theoretical and managerial implications.