“She is just stupid”—Analyzing user–agent interactions in emotional game situations

A multiplayer dice game was realized which is played by two users and one embodied conversational agent. During the game, the players have to lie to each other to win the game and the longer the game commences the more probable it is that someone is lying, which creates highly emotional situations. We ran a number of evaluation studies with the system. The specific setting allows us to compare user–user interactions directly with user–agent interactions in the same game. So far, the users’ gaze behavior and the users’ verbal behavior towards one another and towards the agent have been analyzed. Gaze and verbal behavior towards the agent partly resembles patterns found in the literature for human–human interactions, partly the behavior deviates from these observations and could be interpreted as rude or impolite like continuous staring, insulting, or talking about the agent. For most of these seemingly abusive behaviors, a more thorough analysis reveals that they are either acceptable or present some interesting insights for improving the interaction design between users and embodied conversational agents.     

Personal assistants: Direct manipulation vs. mixed initiative interfaces

Interface agents are computer programmes that provide assistance to users dealing with computer-based applications. The introduction of agents to user interfaces caused the exploration of new metaphors to enhance user ability to directly manipulate interfaces. In this regard, mixed-initiative interaction refers to a flexible interaction strategy in which agents contribute with users by providing suitable information at the most appropriate time. Mixed-initiative approaches promise to dramatically enhance human–computer interaction by allowing agents to resemble human assistants. In this paper, we report a study on how the interaction metaphor can affect the user perception of agent capabilities and, in turn, the final success of agents.     

Personalizing user–agent interaction

Interface agents are computer programs that provide personalized assistance to users with their computer-based tasks. The interface agents developed so far have focused their attention on learning a user's preferences in a given application domain and on assisting him according to them. However, in order to personalize the interaction with users, interface agents should also learn how to best interact with each user and how to provide them assistance of the right sort at the right time. To fulfil this goal, an interface agent has to discover when the user wants a suggestion to solve a problem or deal with a given situation, when he requires only a warning about it and when he does not need any assistance at all. In this work, we propose a learning algorithm, named WoS, to tackle this problem. Our algorithm is based on the observation of a user's actions and on a user's reactions to the agent's assistance actions. The WoS algorithm enables an interface agent to adapt its behavior and its interaction with a user to the user's assistance requirements in each particular context.     

An Interface Agent Approach to Personalize Users' Interaction with Databases

Making queries to a database system through a computer application can become a repetitive and time-consuming task for those users who generally make similar queries to get the information they need to work with. We believe that interface agents could help these users by personalizing the query-making and information retrieval tasks. Interface agents are characterized by their ability to learn users' interests in a given domain and to help them by making suggestions or by executing tasks on their behalf. Having this purpose in mind we have developed an agent, named QueryGuesser, to assist users of computer applications in which retrieving information from a database is a key task. This agent observes a user's behavior while he is working with the database and builds the user's profile. Then, QueryGuesser uses this profile to suggest the execution of queries according to the user's habits and interests, and to provide the user information relevant to him by making time-demanding queries in advance or by monitoring the events and operations occurring in the database system. In this way, the interaction between database users and databases becomes personalized while it is enhanced.     

The affective outcomes of using influence tactics in embodied conversational agents

In this study, we highlight the theoretical underpinnings of human impression management tactics and link them to current research in embodied conversational agents. Specifically, we incorporated impression management behaviors into an embodied conversational agent in order to show that different influence strategies affect user perceptions, and how those perceptions might be moderated by user gender. We programmed the agent to use two human impression management techniques (ingratiation and self-promotion) and had the agent interact with 88 users. After the interaction, users reported their perceptions of the system’s power, trustworthiness, expertise, and attractiveness. The impression management techniques altered users’ perceptions and these perceptions were moderated by gender differences.     

A framework for the assessment of synthetic personalities according to user perception

Endowing artificial conversational agents with personality is a very promising way to obtain more believable user interactions with robots and computers. However, although many authors have studied how to create an agent׳s personality and how it affects performance and user satisfaction, less attention has been paid to assess whether the designed agent׳s personality corresponds to the users׳ perception, whether it is easily recognizable, and what is the effect that the user׳s own personality has in the discrimination of the agents׳ personality. In this paper we present an assessment framework to address these issues in an integrated way, which in our opinion offers enough flexibility to consider the diversity of application domains and evaluation approaches that can be found in the literature. The framework is based on numerical measures, which facilitate the interpretation of results and makes it possible to compare and rank different agents with respect to the user׳s perception of the rendered personality. In addition, we have developed a tool that implements the framework, which may be very useful for researchers in order to easily evaluate different agent personalities.     

I-TRUST: investigating trust between users and agents in a multi-agent portfolio management system

There is considerable research investigating trust among agents in multi-agent systems. However, the issue of trust between agents and users has rarely been reported in the literature. In this paper, we describe our experiences with I-TRUST, where trust is introduced as a relationship between clients and broker agents in terms of the amount of money clients are willing to give to these agents to invest on their behalf. The goals of broker agents are not only to maximize total revenue subject to clients’ risk preference, but also to reinforce trust with their clients. To achieve this, broker agents must first elicit user models both explicitly through questionnaires and implicitly through three games. Then based on the initial user models, a broker agent will learn to invest and later update user models when necessary. From the three experiments conducted in this study, we found that the controllability of a client towards the autonomous system plays a significant role for trust building.     

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.     

A Case Study in the Specification and Analysis of Design Alternatives for a User Interface

There is considerable interest within the Human Computer Interaction (HCI) community in the use of media spaces to enhance awareness and interaction between workers in offices or other spatially distributed environments. In addition to the technical challenges of providing reliable and efficient audio-visual communication, there are important social questions, in particular how users are able to control access to their personal environments, and how to advise other users about their level of availability. Within AMODEUS-2¹, an ESPRIT Basic Research Action concerned with the development, transfer and assessment of techniques for modelling human-computer interaction, a prototype media space has been analysed by various user and system oriented modelling techniques. This paper describes how formal specification can be used to express requirements on the interfaces needed to control access and availability in a media space. Beyond its obvious use in clarifying the subtle relationship between these concerns, the paper describes how the specification assists in assessing design options originating from other modelling disciplines. Received August 1996 / Accepted in revised form April 1999     

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     

A Movie Recommendation System – An Application of Voting Theory in User Modeling

Our research agenda focuses on building software agents that can employ user modeling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly beneficial application of intelligent agent technology. A particular kind of assistant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to model and reason with user preferences for items in the application domain. Our primary concern is to develop a reasoning procedure that can meaningfully and systematically tradeoff between user preferences. We have adapted mechanisms from voting theory that have desirable guarantees regarding the recommendations generated from stored preferences. To demonstrate the applicability of our technique, we have developed a movie recommender system that caters to the interests of users. We present issues and initial results based on experimental data of our research that employs voting theory for user modeling, focusing on issues that are especially important in the context of user modeling. We provide multiple query modalities by which the user can pose unconstrained, constrained, or instance-based queries. Our interactive agent learns a user model by gaining feedback aboutits recommended movies from the user. We also provide pro-active information gathering to make user interaction more rewarding. In the paper, we outline the current status of our implementation with particular emphasis on the mechanisms used to provide robust and effective recommendations.     

An extended JADE-S based framework for developing secure Multi-Agent Systems

Agent communities are self-organized virtual spaces consisting of a large number of agents and their dynamic environments. Within a community, agents group together offering special e-services for effective, reliable, and mutual benefits. Usually, an agent community is composed of specialized agents performing one or more tasks in a single domain/sub-domain, or in highly intersecting domains. However, secure Multi-Agent Systems require severe mechanisms in order to prevent malicious attacks. Several limits affect exiting secure agents platform, such as the lack of a strong authentication system, the lack of a flexible distributed mechanism for access control and the lack of a system for storing past behaviors of agent/user. Biometric owner agents authentication, agent/users policies to regulate agent's behavior and actions, and agent/users reputation level to select trusted agents can be used to overcome the above limits and enhance the level of security for these applications. In this paper an extended JADE-S based framework for developing secure Multi-Agent Systems is proposed. The framework functionalities are extended by self-contained FPGA biometric sensors providing secure and fast user authentication service. Each agent owner, by means of biometric authentication, acquires his/her own X.509v3 digital certificate. Policy files and a flexible, fast distributed Access Control Mechanism can regulate behavior and actions of any users/agent inside the platform. In addition, a mechanism based on the agent reputation is used: reputation is an attribute associated to each owner and/or agent on the basis of its past behavior and integrity. In order to prove the feasibility of the proposed framework, we have developed a multi-agent e-Banking system. System goal deals with e-Banking services such as bank account statements, account transactions and so on. In the paper, the experimental features of the biometric self-contained sensors are also outlined.     

An Analysis of the Interaction Between Intelligent Software Agents and Human Users

Interactions between an intelligent software agent (ISA) and a human user are ubiquitous in everyday situations such as access to information, entertainment, and purchases. In such interactions, the ISA mediates the user’s access to the content, or controls some other aspect of the user experience, and is not designed to be neutral about outcomes of user choices. Like human users, ISAs are driven by goals, make autonomous decisions, and can learn from experience. Using ideas from bounded rationality (and deploying concepts from artificial intelligence, behavioural economics, control theory, and game theory), we frame these interactions as instances of an ISA whose reward depends on actions performed by the user. Such agents benefit by steering the user’s behaviour towards outcomes that maximise the ISA’s utility, which may or may not be aligned with that of the user. Video games, news recommendation aggregation engines, and fitness trackers can all be instances of this general case. Our analysis facilitates distinguishing various subcases of interaction (i.e. deception, coercion, trading, and nudging), as well as second-order effects that might include the possibility for adaptive interfaces to induce behavioural addiction, and/or change in user belief. We present these types of interaction within a conceptual framework, and review current examples of persuasive technologies and the issues that arise from their use. We argue that the nature of the feedback commonly used by learning agents to update their models and subsequent decisions could steer the behaviour of human users away from what benefits them, and in a direction that can undermine autonomy and cause further disparity between actions and goals as exemplified by addictive and compulsive behaviour. We discuss some of the ethical, social and legal implications of this technology and argue that it can sometimes exploit and reinforce weaknesses in human beings.     

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.     

A conceptual framework for agent-based agile manufacturing cells

Abstract. Agile manufacturing techniques are perceived as the manufacturing systems of the future. Agile manufacturing cells are dynamic and reconfigurable and the modelling of the manufacturing cells and its interaction mechanism is critical to its successful use. This paper deals with the architecture and cooperation mechanism of web‐based agile manufacturing cells. Based on an analysis of structure and organization requirements of agile manufacturing cells and a comparison of three basic architectures of manufacturing systems, the quasi‐heterarchical architecture is used for the agile manufacturing cell. Functional layers are defined in this architecture to make the cell's control system reconfigurable and reusable. Agent technology is adopted for implementation of each layer's functions to establish an agent‐based model of agile manufacturing cells. Four types of agents including cooperation agent, job management agent, resource broker agent, and resource control agent are defined, and their functions discussed. Finally, a real time interaction mechanism of the agents is presented by considering the activities during the agents’ cooperation in an agile manufacturing cell.     

This computer responds to user frustration:: Theory,design, and results

Use of technology often has unpleasant side effects, which may include strong, negative emotional states that arise during interaction with computers. Frustration, confusion, anger, anxiety and similar emotional states can affect not only the interaction itself, but also productivity, learning, social relationships, and overall well-being. This paper suggests a new solution to this problem: designing human–computer interaction systems to actively support users in their ability to manage and recover from negative emotional states. An interactive affect–support agent was designed and built to test the proposed solution in a situation where users were feeling frustration. The agent, which used only text and buttons in a graphical user interface for its interaction, demonstrated components of active listening, empathy, and sympathy in an effort to support users in their ability to recover from frustration. The agent's effectiveness was evaluated against two control conditions, which were also text-based interactions: (1) users’ emotions were ignored, and (2) users were able to report problems and ‘vent’ their feelings and concerns to the computer. Behavioral results showed that users chose to continue to interact with the system that had caused their frustration significantly longer after interacting with the affect–support agent, in comparison with the two controls. These results support the prediction that the computer can undo some of the negative feelings it causes by helping a user manage his or her emotional state.     

An agent-based approach for integrating user profile into a knowledge management process

This paper presents an approach based on a user agent to permit a number of users connected to distant machines to access different information sources in order to satisfy their requests. This user agent permits the simplification of the information search from distributed sources by making them transparent to the users. The agent considers the specific needs of each user during the search and responds with reference to their profile. It also permits the processing of one or more information requests by one or more users, as well as concurrent responses to each of them. Moreover, the agent provides its users with a measure of interaction, in order to enhance the quality and quantity of the results obtained. As a result, the agent is endowed with the ability to filter and refine the search, thus improving its service to the users.     

User Attitudes Regarding a User-Adaptive eCommerce Web Site

Despite an abundance of recommendations by researchers and more recently by commercial enterprises for adaptive interaction techniques and technologies, there exists little experimental validation of the value of such approaches to users. We have conducted user studies focussed on the perceived value of a variety of personalization features for an eCommerce Web site for computing machinery sales and support. Our study results have implications for the design of user-adaptive applications. Interesting findings include unenthusiastic user attitudes toward system attempts to infer user needs, goals, or interests and to thereby provide user-specific adaptive content. Users also expressed equivocal opinions of collaborative filtering for the specific eCommerce scenarios we studied; thus personalization features popular in one eCommerce environment may not be effective or useful for other eCommerce domains. Users expressed their strong desire to have full and explicit control of data and interaction. Lastly, users want readily to be able to make sense of site behavior, that is, to understand a site’s rationale for displaying particular content.     

Eye movements as indices for the utility of life-like interface agents: A pilot study

We motivate an approach to evaluating the utility of life-like interface agents that is based on human eye movements rather than questionnaires. An eye tracker is employed to obtain quantitative evidence of a user’s focus of attention without distracting from the primary task. The salient feature of our evaluation strategy is that it allows us to measure important properties of a user’s interaction experience on a moment-by-moment basis in addition to a cumulative (spatial) analysis of the user’s areas of interest. We describe a pilot study in which we compare attending behavior of subjects watching the presentation of a computer-generated apartment layout and visualization augmented by three types of media: an animated agent, a text box, and speech only. The investigation of eye movements revealed that deictic gestures performed by the agent are more effective in directing the attentional focus of subjects to relevant interface objects than the media used in the two control conditions, at a slight cost of distracting the user from visual inspection of the object of reference. The results also demonstrate that the presence of an interface agent seemingly triggers natural and social interaction protocols of human users.     

Learning personal preferences on online newspaper articles from user behaviors

This paper discusses methods by which user preferences for WWW-based newspaper articles can be learned from user behaviors. Two modes of inference were compared in an experiment: one using explicit feedback and the other using implicit feedback. In the explicit feedback mode, the users score all articles according to their relevance. In the implicit feedback mode, the user reads articles by performing scrolling and enlarging operations, and the system infers from the operations how much the user was interested in each article. Our newspaper on the WWW, called ANATAGONOMY, has a learning engine and a scoring engine on the server. The system users read daily news articles by using a WWW browser in which there is an interaction agent that monitors the user behaviors. The learning engine on the server infers user preferences from the interaction agent, and the scoring engine scores new articles and creates personalized newspaper pages based on the extracted user profiles. In an experiment, the system was able to personalize the newspaper to some extent when using only implicit feedback when some parameters were properly set, but the personalization was not as precise as it was when explicit feedback was used. By mixing explicit feedback with implicit feedback, the system could personalize newspapers quickly and precisely without requiring too much effort on the part of the users. User preferences can also be used to construct information retrieval agents or even to create cyberspace communities of the users that have similar interests. We think that the proposed technique for learning user preferences greatly enhances the value of the WWW.