Biblio

FinBots are chatbots built on automated decision technology, aimed to facilitate accessible banking and to support customers in making financial decisions. Chatbots are increasing in prevalence, sometimes even equipped to mimic human social rules, expectations and norms, decreasing the necessity for human-to-human interaction. As banks and financial advisory platforms move towards creating bots that enhance the current state of consumer trust and adoption rates, we investigated the effects of chatbot vignettes with and without socio-emotional features on intention to use the chatbot for financial support purposes. We conducted a between-subject online experiment with N = 410 participants. Participants in the control group were provided with a vignette describing a secure and reliable chatbot called XRO23, whereas participants in the experimental group were presented with a vignette describing a secure and reliable chatbot that is more human-like and named Emma. We found that Vignette Emma did not increase participants' trust levels nor lowered their privacy concerns even though it increased perception of social presence. However, we found that intention to use the presented chatbot for financial support was positively influenced by perceived humanness and trust in the bot. Participants were also more willing to share financially-sensitive information such as account number, sort code and payments information to XRO23 compared to Emma - revealing a preference for a technical and mechanical FinBot in information sharing. Overall, this research contributes to our understanding of the intention to use chatbots with different features as financial technology, in particular that socio-emotional support may not be favoured when designed independently of financial function.

Conversational systems are computer programs that interact with users using natural language. Considering the complexity and interaction of the different components involved in building intelligent conversational systems that can perform diverse tasks, a promising approach to facilitate their development is by using multiagent systems (MAS). This paper reviews the main concepts and history of conversational systems, and introduces an architecture based on MAS. This architecture was designed to support the development of conversational systems in the domain chosen by the developer while also providing a reusable built-in dialogue control. We present a practical application in the healthcare domain. We observed that it can help developers to create conversational systems in different domains while providing a reusable and centralized dialogue control. We also present derived lessons learned that can be helpful to steer future research on engineering domain-specific conversational systems.

This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. The paper considers the possibility and necessity of using in modern control and training systems with a natural language interface methods and mechanisms, characteristic for knowledge processing systems. This symbiosis assumes the introduction of specialized inference machines into the testing systems. For the effective operation of such an intelligent interpreter, it is necessary to “translate” the user's answers into one of the known forms of the knowledge representation, for example, into the expressions (rules) of the first-order predicate calculus. A lexical processor, performing morphological, syntactic and semantic analysis, solves this task. To simplify further work with the rules, the Skolem-transformation is used, which allows to get rid of quantifiers and to present semantic structures in the form of sequents (clauses, disjuncts). The basic principles of operation of the inference machine are described, which is the main component of the developed intellectual subsystem. To improve the performance of the machine, one of the fastest methods was chosen - a parallel method of deductive inference based on the division of clauses. The parallelism inherent in the method, and the use of the dataflow architecture, allow parallel computations in the output machine to be implemented without additional effort on the part of the programmer. All this makes it possible to reduce the time for comparing the sequences stored in the knowledge base by several times as compared to traditional inference mechanisms that implement various versions of the principle of resolutions. Formulas and features of the technique of numerical estimation of the user's answers are given. In general, the development of the human-computer dialogue capabilities in test systems- through the development of a specialized module for processing knowledge, will increase the intelligence of such systems and allow us to directly consider the semantics of sentences, more accurately determine the relevance of the user's response to standard knowledge and, ultimately, get rid of the skeptical attitude of many managers to machine testing systems.