Exploring agent support at the user interface in e-commerce applications

In this paper, we present some of the results from our ongoing research work in the area of ‘agent support’ for electronic commerce, particularly at the user interface level. Our goal is to provide intelligent agents to assist both the consumers and the vendors in an electronic shopping environment. Users with a wide variety of different needs are expected to use the electronic shopping application and their expectations about the interface could vary a lot. Traditional studies of user interface technology have shown the existence of a ‘gap’ between what the user interface actually lets the users do and the users’ expectations. Agent technology, in the form of personalized user interface agents, can help to narrow this gap. Such agents can be used to give a personalized service to the user by knowing the user’s preferences. By doing so, they can assist in the various stages of the users’ shopping process, provide tailored product recommendations by filtering information on behalf of their users and reduce the information overload. From a vendor’s perspective, a software sales agent could be used for price negotiation with the consumer. Such agents would give the flexibility offered by negotiation without the burden of having to provide human presence to an online store to handle such negotiations. Published online: 25 July 2001     

An ontological modelling of user requirements for personalised information provision

The knowledge economy offers opportunity to a broad and diverse community of information systems users to efficiently gain information and know-how for improving qualifications and enhancing productivity in the work place. Such demand will continue and users will frequently require optimised and personalised information content. The advancement of information technology and the wide dissemination of information endorse individual users when constructing new knowledge from their experience in the real-world context. However, a design of personalised information provision is challenging because users’ requirements and information provision specifications are complex in their representation. The existing methods are not able to effectively support this analysis process. This paper presents a mechanism which can holistically facilitate customisation of information provision based on individual users’ goals, level of knowledge and cognitive styles preferences. An ontology model with embedded norms represents the domain knowledge of information provision in a specific context where users’ needs can be articulated and represented in a user profile. These formal requirements can then be transformed onto information provision specifications which are used to discover suitable information content from repositories and pedagogically organise the selected content to meet the users’ needs. The method is provided with adaptability which enables an appropriate response to changes in users’ requirements during the process of acquiring knowledge and skills.     

A social approach to context-aware retrieval

We present a general purpose solution to Web content and services perusal by means of mobile devices, named Social Context-Aware Browser. This is a novel approach for information access based on users’ context, that exploits social and collaborative models to overtake the limits of the existing solutions. Instead of relying on a pool of experts and on a rigid categorization, as it is usually done in the context-aware field, our solution allows the crowd of users to model, control, and manage the contextual knowledge through collaboration and participation. To have a dynamic and user-tailored context representation, and to enhance the process of retrieval based on users’ actual situation, the community of users is encouraged to define the contexts of interest, to share, use, and discuss them, and to associate context to content and resources (Web pages, services, applications, etc.). This paper provides an overall presentation of our solution, describing the idea, the implementation, and the evaluation through a benchmark based methodology.     

Effective agent collaboration through improved communication by means of contextual reasoning

This paper describes an extension to a context‐driven agent representation paradigm that facilitates modeling collaborative tactical behaviors for simulations of team games or military missions. Called collaborative context‐based reasoning, it emphasizes communication among the collaborating agents and carries it out by exchanging their currently active context when feasible. CCxBR is founded on the concepts defined in joint intention theory (JIT). The research described here presents an architecture that incorporates JIT in a contextual framework. The ability to facilitate communication among the collaborating agents by exchanging information about active contexts resembles the ability of humans to agree on a tactic in midstream and predict the behavior of their collaborators. This allows a CCxBR agent to invoke the actions involved in the tactic in the pursuit of a common goal. The paper describes several prototypes built to evaluate the CCxBR approach and the experiments executed to determine its effectiveness. The results of the experiments and the conclusions reached are discussed. © 2010 Wiley Periodicals, Inc.     

A formal model of emotions for an empathic rational dialog agent

Recent research has shown that virtual agents expressing empathic emotions toward users have the potential to enhance human–machine interaction. To provide empathic capabilities to a rational dialog agent, we propose a formal model of emotions based on an empirical and theoretical analysis of the users’ conditions of emotion elicitation. The emotions are represented by particular mental states of the agent, composed of beliefs, uncertainties and intentions. This semantically grounded formal representation enables a rational dialog agent to identify from a dialogical situation the empathic emotion that it should express. An implementation and an evaluation of an empathic rational dialog agent have enabled us to validate the proposed model of empathy.     

Towards an environment interface standard for agent platforms

We introduce an interface for connecting agent platforms to environments. This interface provides generic functionality for executing actions and for perceiving changes in an agent’s environment. It also provides support for managing an environment, e.g., for starting, pausing and terminating it. Among the benefits of such an interface are (1) standard functionality is provided by the interface implementation itself, and (2) agent platforms that support the interface can connect to any environment that implements the interface. This significantly reduces effort required from agent and environment programmers as the environment code needed to implement the interface needs to be written only once. We propose that the interface presented may be used as a standard that enables agents to control entities in environments. Our starting point for designing such a generic interface is based on a careful study of the various interfaces used by different agent programming languages to connect agent programs to environments. We discuss several case studies that use our interface (an elevator simulator, the well-known agent contest, and an implementation of the interface to connect agents to bots in Unreal Tournament 2004).     

Context-dependent awareness support in open collaboration environments

The widespread adoption of online services for performing work, home and leisure tasks enables users to operate in the ubiquitous environment provided by the Internet by managing a possibly high number of parallel (private and shared) activity contexts. The provision of awareness information is a key factor for keeping users up-to-date with what happens around them; e.g., with the operations performed by their collaborators. However, the delivery of notifications describing the occurred events can interrupt the users’ activities, with a possible disruptive effect on their emotional and attentional states. As a possible solution to the trade-off between informing and interrupting users, we defined two context-dependent notification management policies which support the selection of the notifications to be delivered on the basis of the user’s current activities, at different granularity levels: general collaboration context versus task carried out. These policies are offered by the COntext depeNdent awaReness informAtion Delivery (CONRAD) framework. We tested such policies with users by applying them in a collaboration environment that includes a set of largely used Web 2.0 services. The experiments show that our policies reduce the levels of workload on users while supporting an up-to-the-moment understanding of the interaction with their shared contexts. The present paper presents the CONRAD framework and the techniques underlying the proposed notification policies.     

Human-agent service matching using natural language queries: system test and training

Smart environments, ambient intelligence and intelligent agents leave the user lost between large amounts of services. Ad-hoc networks, mobile agents and mobile devices make the set of available services dynamic over time and space, increasing the user’s problems to find the service he needs. Earlier, we presented a ServiceMatcher that can find the agent best fitting to the user’s natural language request. This paper presents performance results of the ServiceMatcher. The test queries come from human users in a realistic scenario (see our other paper in this issue). With a short training of the agent vocabularies, over 80% correct service matches are found.     

Recommenders in a personalized,collaborative digital library environment

We envisage an information source not only as an information resource where users may submit queries to satisfy their daily information need, but also as a collaborative working and meeting space of people sharing common interests. Indeed, we will present a highly personalized environment where not only users may organize (and search into) the information space according to their individual taste and use, but which provides advanced features of collaborative work among the users. It is up to the system to discover interesting properties about the users’ interests, relationships between users and user communities and to make recommendations based on preference patterns of the users, which is the main topic of this paper.     

Negotiated Collusion: Modeling Social Language and its Relationship Effects in Intelligent Agents

Building a collaborative trusting relationship with users is crucial in a wide range of applications, such as advice-giving or financial transactions, and some minimal degree of cooperativeness is required in all applications to even initiate and maintain an interaction with a user. Despite the importance of this aspect of human–human relationships, few intelligent systems have tried to build user models of trust, credibility, or other similar interpersonal variables, or to influence these variables during interaction with users. Humans use a variety of kinds of social language, including small talk, to establish collaborative trusting interpersonal relationships. We argue that such strategies can also be used by intelligent agents, and that embodied conversational agents are ideally suited for this task given the myriad multimodal cues available to them for managing conversation. In this article we describe a model of the relationship between social language and interpersonal relationships, a new kind of discourse planner that is capable of generating social language to achieve interpersonal goals, and an actual implementation in an embodied conversational agent. We discuss an evaluation of our system in which the use of social language was demonstrated to have a significant effect on users’ perceptions of the agent’s knowledgableness and ability to engage users, and on their trust, credibility, and how well they felt the system knew them, for users manifesting particular personality traits.This revised version was published online in July 2005 with corrections to the author name Bickmore.     

An experts approach to strategy selection in multiagent meeting scheduling

In the multiagent meeting scheduling problem, agents negotiate with each other on behalf of their users to schedule meetings. While a number of negotiation approaches have been proposed for scheduling meetings, it is not well understood how agents can negotiate strategically in order to maximize their users’ utility. To negotiate strategically, agents need to learn to pick good strategies for negotiating with other agents. In this paper, we show how agents can learn online to negotiate strategically in order to better satisfy their users’ preferences. We outline the applicability of experts algorithms to the problem of learning to select negotiation strategies. In particular, we show how two different experts approaches, plays [3] and Exploration–Exploitation Experts (EEE) [10] can be adapted to the task. We show experimentally the effectiveness of our approach for learning to negotiate strategically.     

Supporting Ubiquitous IMS-based Teleconferencing Through Discovery and Composition of IMS and Web Components

Natural communication among people happens in flexible ways and is strongly affected by the users’ situation (such as communication tools available, user’s location, and user’s preferences). This situation or context information is seldom used to initiate communication sessions among users. Current communication systems are indifferent about users’ context, often require time consuming manual configurations and often result in conferencing tools not being easily accessible when needed. This leads to lower adoption of innovative communications services. IMS SIP (IP Multimedia Subsystem, Session Initiation Protocol) sessions allow users to access the session from different points of contact (home, office, etc.), however, IMS still requires a prior knowledge of all SIP components that might be used in a SIP session. Furthermore, IMS makes limited use of context information (mainly user-defined availability). To address these issues our research approach combines techniques from pervasive computing with IMS networking principles to facilitate compositions of communication sessions based on users’ context. We propose a platform and APIs for pervasive application development support to allow greater intelligence in IMS applications. We additionally provide mechanisms for IMS applications to apply their intelligence to the configuration of physical devices and web resources used to set up a conference. The innovations proposed in this paper are: (1) A new standard for intelligent IMS-based conferencing applications. (2) Application Development Interfaces (APIs) for a platform for pervasive computing. (3) An architecture for a pervasive IMS platform.

Activity recognition using eye-gaze movements and traditional interactions

The need for intelligent HCI has been reinforced by the increasing numbers of human-centered applications in our daily life. However, in order to respond adequately, intelligent applications must first interpret users’ actions. Identifying the context in which users’ interactions occur is an important step toward automatic interpretation of behavior. In order to address a part of this context-sensing problem, we propose a generic and application-independent framework for activity recognition of users interacting with a computer interface. Our approach uses Layered Hidden Markov Models (LHMM) and is based on eye-gaze movements along with keyboard and mouse interactions. The main contribution of the proposed framework is the ability to relate users’ interactions to a task model in variant applications and for different monitoring purposes. Experimental results from two user studies show that our activity recognition technique is able to achieve good predictive accuracy with a relatively small amount of training data.     

A multi-agent recommender system for supporting device adaptivity in e-Commerce

Traditional recommender systems for e-Commerce support the customers’ activities providing them with useful suggestions about available products in Web stores. To this purpose, in an agent-based context, each customer is often associated with a customer agent that interacts with the site agent associated with the visited e-Commerce Web site. In presence of a high number of interactions between customers and Web sites, the generation of recommendations can be a heavy task for both these agents. Moreover, customers can navigate on the Web by using different devices having different characteristics that may influence customer’s preferences. In this paper we propose a new multi-agent system, called ARSEC, where each device exploited by a customer is associated with a device agent that autonomously monitors his/her behaviour. Furthermore, each customer is associated with a customer agent that collects in a global profile the information provided by his/her device agents and each e-Commerce Web site is associated with a seller agent. Based on the similarity existing among the global profiles the customers are partitioned in clusters, each one managed by a counsellor agent. Recommendations are generated in ARSEC as result of the collaboration between the seller agent and some counsellor agents associated with the customer. The usage of the device agents leads to generating recommendations taking into account the device currently used, while the fully decentralized architecture introduces a strong reduction of the time costs. Some experimental results are presented to show the significant advantages obtained by ARSEC in terms of recommendation effectiveness with respect to other well-known agent-based recommenders.     

Attentive interfaces for users with disabilities: eye gaze for intention and uncertainty estimation

Attentive user interfaces (AUIs) capitalize on the rich information that can be obtained from users’ gaze behavior in order to infer relevant aspects of their cognitive state. Not only is eye gaze an excellent clue to states of interest and intention, but also to preference and confidence in comprehension. AUIs are built with the aim of adapting the interface to the user’s current information need, and thus reduce workload of interaction. Given those characteristics, it is believed that AUIs can have particular benefits for users with severe disabilities, for whom operating a physical device (like a mouse pointer) might be very strenuous or infeasible. This paper presents three studies that attempt to gauge uncertainty and intention on the part of the user from gaze data, and compare the success of each approach. The paper discusses how the application of the approaches adopted in each study to user interfaces can support users with severe disabilities.     

Using ubiquitous computing in interactive mobile marketing

Unique features of handheld devices, including their mobility, personalization and location-awareness engender new types of applications for mobile commerce, such as mobile advertising. Mobile marketing and advertising applications deliver promotional information to consumers based on their preferences and location. In this paper, we present SMMART, a context-aware, adaptive and personalized m-commerce application designed to deliver targeted promotions to the users of mobile devices about the products they like while guarding the users’ identity and protecting them from any unsolicited messages. Promotions distributed by SMMART are personalized by performing intelligent matching of the user’s shopping interests to current promotions available at a retail site. SMMART can adapt to changing preferences of its user by inconspicuously monitoring his or her shopping habits. We describe a fully functional prototype of SMMART built for Pocket PCs running Windows CE with .NET Compact Framework. This paper also presents a study demonstrating end-user usability and economic viability of SMMART.     

Problem-Driven Approach to the Design of Information Technology Systems Supporting Complex Cognitive Tasks

The present paper proposes a methodological framework for the design and evaluation of information technology systems supporting complex cognitive tasks. The aim of the methodological framework is to permit the design of systems which: (1) address the cognitive difficulties met by potential users in performing complex problem-solving tasks; (2) improve their potential users’ problem-solving performance; and (3) achieve compatibility with potential users’ competences and working environment. After a short review of the weaknesses of existing systems supposed to support complex cognitive tasks, the theoretical foundations of the proposed methodology are presented. These are the ergonomic work analysis of French ergonomists, cognitive engineering, cognitive anthropology–ethnomethodology and activity theory. The third section of the paper describes the generic ergonomic model, which constitutes a frame of reference useful for the analyst of the work situation to which the information technology system is addressed. In the fourth section, the proposed methodology is outlined, and in the fifth a case study demonstrating an application of the methodology is summarised. In the epilogue, the differences between the proposed methodological framework and other more conventional approaches are discussed. Finally, directions for future developments of the problem-driven approach are proposed.     

Experience with usability evaluation of e-learning systems

Progress in the field of e-learning has been slow, with related problems mainly associated with the poor design of e-learning systems. Moreover, because of a depreciated importance of usability, usability studies are not very frequent. This paper reports the experience with the usability assessment of intelligent learning and teaching systems which are based on TEx-Sys model and are intended to enhance the process of knowledge acquisition in daily classroom settings. The applied scenario-based usability evaluation, as a combination of behaviour and opinion based measurements, enabled to quantify usability in terms of users’ (teachers’ and students’) performance and satisfaction. According to the achieved results, the main directions for interface redesign are offered. The acquired experience indicates that useful usability assessments with a significant identification of interface limitations can be performed quite easily and quickly. On the other hand, it raised a series of questions which, in order to be clarified, require further comprehensive research, the more so if the employment of universal design within e-learning context is considered.     

Active adaptation in human-agent collaborative interaction

When a human user interacts with an adaptive agent to achieve human-agent collaboration, active adaptation is considered to be one of the critical characteristics of the agent. In order to investigate the principal features of active adaptation, we developed a human-agent collaborative experimental environment called WAITER (waiter agent interactive training experimental restaurant) and conducted two types of experiments, a Wizard of OZ (WOZ) agent experiment and an autonomous agent experiment. The objective of these experiments is to observe how human users perceive the agents and change their instructions when interacting with adaptive agents. The results indicate that humans can recognize changes in the agent’s actions and change their instruction methods accordingly. This implies that active adaptation of the agents may encourage the adaptation of the human users and help to build an adaptation loop between them. The experimental results also suggest that active adaptation may play an important role in a human-agent collaborative task.