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.

This paper describes a system that leads us to believe in the feasibility of constructing natural spoken dialogue systems in task-oriented domains. It specifically addresses the issue of robust interpretation of speech in the presence of recognition errors. Robustness is achieved by a combination of statistical error post-correction, syntactically- and semantically-driven robust parsing, and extensive use of the dialogue context. We present an evaluation of the system using time-to-completion and the quality of the final solution that suggests that most native speakers of English can use the system successfully with virtually no training.

We introduce an architecture and toolkit for building dialogue managers currently being developed in the TRINDI project, based on the notions of information state and dialogue move engine. The aim is to provide a framework for experimenting with implementations of di erent theories of information state, information state update and dialogue control. Anumber of dialogue managers are currently being built using the toolkit, and we present a detailed look at one of them. We believe that this framework will make implementation of dialogue processing theories easier, also facilitating comparison of di erent types of dialogue systems, thus helping to achieve a prerequisite for arriving at a best practice for the development of the dialogue management component ofaspoken dialogue system. 1

This work shows how a dialogue model can be represented as a Partially Observable Markov Decision Process (POMDP) with observations composed of a discrete and continuous component. The continuous component enables the model to directly incorporate a confidence score for automated planning. Using a testbed simulated dialogue management problem, we show how recent optimization techniques are able to find a policy for this continuous POMDP which outperforms a traditional MDP approach. Further, we present a method for automatically improving handcrafted dialogue managers by incorporating POMDP belief state monitoring, including confidence score information. Experiments on the testbed system show significant improvements for several example handcrafted dialogue managers across a range of operating conditions. 1

We are working toward animated agents that can collaborate with human students in virtual worlds. The agent's objective is to help students learn to perform physical, procedural tasks, such as operating and maintaining equipment. Like most of the previous research on task-oriented dialogues, the agent (computer) serves as an expert that can provide guidance to a human novice. Research on such dialogues dates back more than twenty years (Deutsch 1974), and the subject remains an active research area (Allen et al. 1996; Lochbaum 1994; Walker 1996). However, most of that research has focused solely on verbal dialogues, even though the earliest studies clearly showed the ubiquity of nonverbal communication in human task-oriented dialogues (Deutsch 1974). To allow a wider variety of interactions among agents and human students, we use virtual reality (Durlach and Mavor 1995); agents and students cohabit a threedimensional, interactive, simulated mock-up of the student'

Abstract. Mixed-initiative problem solving lies at the heart of knowledge-based learning environments. While learners are actively engaged in problem-solving activities, learning environments should monitor their progress and provide them with feedback in a manner that contributes to achieving the twin goals of learning effectiveness and learning efficiency. Mixed-initiative interactions are particularly critical for constructivist learning environments in which learners participate in active problem solving. We have recently begun to see the emergence of believable agents with lifelike qualities. Featured prominently in constructivist learning environments, lifelike pedagogical agents could couple key feedback functionalities with a strong visual presence by observing learners ’ progress and providing them with visually contextualized advice during mixed-initiative problem solving. For the past three years, we have been engaged in a large-scale research program on lifelike pedagogical agents and their role in constructivist learning environments. In the resulting computational framework, lifelike pedagogical agents are specified by (1) a behavior space containing animated and vocal behaviors, (2) a design-centered context model that maintains constructivist problem representations, multimodal advisory contexts, and evolving problem-solving tasks, and (3) a behavior sequencing engine that in realtime dynamically selects and assembles agents ’ actions to create pedagogically effective, lifelike behaviors. To empirically investigate this framework, it has been instantiated in a full-scale implementation of a lifelike pedagogical agent for DESIGN-A-PLANT, a learning environment developed for the domain of botanical anatomy and physiology for middle school students. Experience with focus group studies conducted with middle school students interacting with the implemented agent suggests that lifelike pedagogical agents hold much promise for mixed-initiative learning. Key words: Lifelike agents, pedagogicalagents, animated agents, knowledge-basedlearning environments, mixed-initiative interaction, intelligent tutoring systems, intelligent multimedia presentation,

This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method is

... this paper, we present a new type of recommendation system that carries out a personalized dialogue with the user. This system -- the Adaptive Place Advisor -- treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. The system incorporates a user model that contains item, attribute, and value preferences, which it updates during each conversation and maintains across sessions. The Place Advisor uses both the conversational context and the user model to retrieve candidate items from a case base. The system then continues to ask questions, using personalized heuristics to select which attribute to ask about next, presenting complete items to the user only when a few remain. We report experimental results demonstrating the effectiveness of user modeling in reducing the time and number of interactions required to find a satisfactory item

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

Collaborative learning tasks involve interaction between multiple participants, who thus need to maintain some degree of mutual understanding. The process by which this is accomplished is termed grounding. The way in which collaboration, grounding and learning take place is largely determined by the task, the situation and the tools available. This paper discusses relations between grounding, collaboration and learning, drawing on research from two main areas: the Language Sciences and Cultural-Historical Activity Theory ("CHAT"). We build a unifying perspective of mutual understanding mediated by material and semiotic tools that can be used for analysis as well as for design of collaborative learning tasks, especially those that are carried out via computer-mediated communication. We illustrate the perspective with reference to a particular computermediated collaborative learning situation in the domain of physics. 1. Introduction Collaborative learning is a complex phenomenon that c...