An effective conversational agent with user modeling based on Bayesian network

Abstract. Conversational agents interact with users using natural language interface. Especially in Internet space, their role has been recently highlighted as a virtual representative of a web site. However, most of them use simple pattern matching techniques without considering user’s goal. In this paper, we propose a conversational agent that utilizes user model constructed on Bayesian network for the responses consistent with user’s goal. The agent is applied to the active guide of a website, which shows that the user modeling based on Bayesian network helps to respond to user’s queries appropriately with the their goals.

An investigation into a natural language interface for contact centers

Contact centres are the first point of contact between a company and a customer after the purchase of a product or service. These centres make use of contact centre agents to service customer queries. In the past contact centres hired as many agents as they could in order to service customers, which have led to an increase in personnel costs causing contact centres to become costly to run. Automation techniques were introduced to decrease personnel costs and one such technique is the Interactive Voice Response (IVR). The usability of IVR systems is, however, dismal. Customers would rather speak to a contact centre agent than navigate through the menu structure found in these systems. The menu structure has come under scrutiny because it is difficult to use and navigate, is often not aligned to caller usage patterns, and the menu options are long and vague. This research investigated whether a Natural Language Interface (NLI) could alleviate the problems inherent to IVR. NLIs, however, come with their own disadvantages of which the main ones are ambiguity and the loss of context of a conversation. Two prototypes were implemented, one of which resembled an IVR and the other an NLI (using ALICE concepts). An evaluation of two prototypes confirmed the advantages and disadvantages of these concepts in accordance to theory. A Hybrid prototype was proposed with the aid of two models. The model which proposed an NLI using a rule base was chosen for implementation. The Hybrid prototype was then evaluated against the NLI and IVR prototypes to deduce which prototype was the most effective, efficient and satisfying. The evaluation through the aid of descriptive and inferential statistics showed that the Hybrid prototype was the most usable prototype. The evaluation of the Hybrid prototype confirmed that a Hybrid approach could limit the shortcomings of IVR through the elimination of the menu structure found in these systems, thereby allowing users to state their queries in natural language. The incorporated rule base provided the Hybrid system with long term memory, eliminating one of the main disadvantages of NLIs

Autonomous interactive intermediaries : social intelligence for mobile communication agents

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 151-167).Today's cellphones are passive communication portals. They are neither aware of our conversational settings, nor of the relationship between caller and callee, and often interrupt us at inappropriate times. This thesis is about adding elements of human style social intelligence to our mobile communication devices in order to make them more socially acceptable to both user and local others. I suggest the concept of an Autonomous Interactive Intermediary that assumes the role of an actively mediating party between caller, callee, and co-located people. In order to behave in a socially appropriate way, the Intermediary interrupts with non-verbal cues and attempts to harvest 'residual social intelligence' from the calling party, the called person, the people close by, and its current location. For example, the Intermediary obtains the user's conversational status from a decentralized network of autonomous body-worn sensor nodes. These nodes detect conversational groupings in real time, and provide the Intermediary with the user's conversation size and talk-to-listen ratio. The Intermediary can 'poll' all participants of a face-to-face conversation about the appropriateness of a possible interruption by slightly vibrating their wirelessly actuated finger rings.(cont.) Although the alerted people do not know if it is their own cellphone that is about to interrupt, each of them can veto the interruption anonymously by touching his/her ring. If no one vetoes, the Intermediary may interrupt. A user study showed significantly more vetoes during a collaborative group-focused setting than during a less group oriented setting. The Intermediary is implemented as a both a conversational agent and an animatronic device. The animatronics is a small wireless robotic stuffed animal in the form of a squirrel, bunny, or parrot. The purpose of the embodiment is to employ intuitive non-verbal cues such as gaze and gestures to attract attention, instead of ringing or vibration. Evidence suggests that such subtle yet public alerting by animatronics evokes significantly different reactions than ordinary telephones and are seen as less invasive by others present when we receive phone calls. The Intermediary is also a dual conversational agent that can whisper and listen to the user, and converse with a caller, mediating between them in real time.(cont.) The Intermediary modifies its conversational script depending on caller identity, caller and user choices, and the conversational status of the user. It interrupts and communicates with the user when it is socially appropriate, and may break down a synchronous phone call into chunks of voice instant messages.by Stefan Johannes Walter Marti.Ph.D

Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System

This thesis presents research that combines the benefits of intelligent tutoring systems (ITS), conversational agents (CA) and learning styles theory by constructing a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS aims to imitate a human tutor by implicitly predicting individuals’ learning style preferences and adapting its tutoring style to suit them during a tutoring conversation. ITS are computerised learning systems that intelligently personalise tutoring based on learner characteristics such as existing knowledge and learning style. ITS are traditionally student-led, hyperlink-based learning systems that adapt the presentation of learning resources by reordering or hiding links. Research suggests that students learn more effectively when instruction matches their learning style, which is typically modelled explicitly using questionnaires or implicitly based on behaviour. Learning is a social process and natural language interfaces to ITS, such as CAs, allow students to construct knowledge through discussion. Existing CITS adapt tutoring according to student knowledge, emotions and mood, however no CITS adapts to learning styles. Oscar CITS models a human tutor by directing a tutoring conversation and automatically detecting and adapting to an individual’s learning styles. Original methodologies and architectures were developed for constructing an Oscar Predictive CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured from a learning styles model to dynamically predict learning styles from an individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation algorithm to select the best tutoring style for each tutorial question. The Oscar CITS methodologies and architectures are independent of the learning styles model and subject domain. Empirical studies involving real students have validated the prediction and adaptation of learning styles in a real-world teaching/learning environment. The results show that learning styles can be successfully predicted from a natural language tutoring dialogue, and that adapting the tutoring style significantly improves learning performance