Recent years have witnessed the birth of a new paradigm for learning environments: animated pedagogical agents. These lifelike autonomous characters cohabit learning environments with students to create rich, face-to-face learning interactions. This opens up exciting new possibilities; for example, agents can demonstrate complex tasks, employ locomotion and gesture to focus students'attention on the most salient aspect of the task at hand, and convey emotional responses to the tutorial situation. Animated pedagogical agents offer great promise for broadening the bandwidth of tutorial communication and increasing learning environments' ability to engage and motivate students. This article sets forth the motivations behind animated pedagogical agents, describes the key capabilities they offer, and discusses the technical issues they raise. The discussion is illustrated with descriptions of a number of animated agents that represent the current state of the art.

The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without any emotions. – Marvin Minsky, (Minsky, 1986) p. 163 In every art form it is the emotional content that makes the difference between mere technical skill and true art.

What kinds of social relationships can people have with computers? Are there activities that computers can engage in that actively draw people into relationships with them? What are the potential benefits to the people who participate in these human-computer relationships? To address these questions this work introduces a theory of Relational Agents, which are computational artifacts designed to build and maintain long-term, social-emotional relationships with their users. These can be purely software humanoid animated agents--as developed in this work--but they can also be non-humanoid or embodied in various physical forms, from robots, to pets, to jewelry, clothing, hand-helds, and other interactive devices. Central to the notion of relationship is that it is a persistent construct, spanning multiple interactions; thus, Relational Agents are explicitly designed to remember past history and manage future expectations in their interactions with users. Finally, relationships are fundamentally social and emotional, and detailed knowledge of human social psychology--with a particular emphasis on the role of affect--must be incorporated into these agents if they are to effectively leverage the mechanisms of human social cognition in order to build relationships in the most natural manner possible. People build

Building trust with users is crucial in a wide range of applications, such as financial transactions, and some minimal degree of trust is required in all applications to even initiate and maintain an interaction with a user. Humans use a variety of relational conversational strategies, including small talk, to establish trusting relationships with each other. We argue that such strategies can also be used by interface agents, and that embodied conversational agents are ideally suited for this task given the myriad cues available to them for signaling trustworthiness. We describe a model of social dialogue, an implementation in an embodied conversation agent, and an experiment in which social dialogue was demonstrated to have an effect on trust, for users with a disposition to be extroverts.

Abstract. Lifelike animated agents for knowledge-based learning environments can provide timely, customized advice to support learners ’ problem-solving activities. By drawing on a rich repertoire of emotive behaviors to exhibit contextually appropriate facial expressions and emotive gestures, these agents could exploit the visual channel to more effectively communicate with learners. To address these issues, this article proposes the emotive-kinesthetic behavior sequencing framework for dynamically sequencing lifelike pedagogical agents ’ full-body emotive expression. By exploiting a rich behavior space populated with emotive behaviors and structured by pedagogical speech act categories, a behavior sequencing engine operates in realtime to select and assemble contextually appropriate expressive behaviors. This framework has been implemented in a lifelike pedagogical agent, COSMO, who exhibits full-body emotive behaviors in response to learners ' problem-solving activities.

Lifelike animated agents for knowledge-based learning environments can provide timely, cus-tomized advice to support students ' problem solving. Because of their strong visual presence, they hold signi cant promise for substantially increasing students ' enjoyment of their learning experiences. Akey problem posed by lifelike agents that inhabit arti cial worlds is deictic believability. In the same manner that humans refer to objects in their environment through judicious combinations of speech, locomotion, and gesture, animated agents should be able to move through their environment, and point to and refer to objects appropriately as they provide problem-solving advice. In this paper we describe a framework for achieving deictic believabil-ity in animated agents. A deictic behavior planner exploits a world model and the evolving explanation plan as it selects and coordinates locomotive, gestural, and speech behaviors. The resulting behaviors and utterances are believable, and the references exhibit a lack ofambiguity. This approach to spatial deixis has been implemented in a lifelike animated agent, Cosmo, who inhabits a learning environment for the domain of Internet packet routing. Cosmo provides realtime advice to students as they escort packets through a virtual world of interconnected routers. Results of an informal focus group study with the Cosmo agent suggest that the spatial deixis framework produces clear explanatory animated behaviors. 1 1

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. In this paper, we argue for the need to distinguish between task initiative and dialogue initiative, and present an evidential model for tracking shifts in both types of initiatives in collaborative dialogue interactions. Our model predicts the task and dialogue initiative holders for the next dialogue turn based on the current initiative holders and the effect that observed cues have on changing them. Our evaluation across various corpora shows that the use of cues consistently provides significant improvement in the system's prediction of task and dialogue initiative holders. Finally, we show how this initiative tracking model may be employed by a dialogue system to enable the system to tailor its responses to user utterances based on application domain, system's role in the domain, dialogue history, and user characteristics. Key words: initiative, control, dialogue systems, collaborative interactions This paper has not been submitted elsewhere in identical or similar form, nor ...

Human-human dialogue does not just comprise statements about the task at hand, about the joint and separate goals of the interlocutors, and about their plans. In human-human conversation participants often engage in talk that, on the surface, does not seem to move the dialogue forward at all. However, this talk -- about the weather, current events, and many other topics without significant overt relationship to the task at hand -- may, in fact, be essential to how humans obtain information about one another's goals and plans and decide whether collaborative work is worth engaging in at all. For example, realtors use small talk to gather information to form stereotypes (a collection of frequently co-occurring characteristics) of their clients -- people who drive minivans are more likely to have children, and therefore to be searching for larger homes in neighbourhoods with good schools. Realtors---and salespeople in general---also use small talk to increase intimacy with

Spurred by a range of potential applications, there has been a growing body of research in computational models of human emotion. To advance the development of these models, it is critical that we evaluate them against the phenomena they purport to model. In this paper, we present one method to evaluate an emotion model that compares the behavior of the model against human behavior using a standard clinical instrument for assessing human emotion and coping. We use this method to evaluate the EMA model of emotion [1-3]. The evaluation highlights strengths of the approach and identifies where the model needs further development. 1.