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Life-like animated interface agents for knowledge-based leaming environments can provide timely, customized advice to support students' problem solving. Because of their strong visual presence, they hold significant promise for substantially increasing students' enjoyment of their learning experiences. A key problem posed by life-like agents that inhabit artificial worlds is &i&c 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 believability 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 are unambiguous, This approach to spatial deixis has been implemented in a life-like animated agent, Cosmo, who inhabits a learning environment for the domain of Internet packet routing. The product of a large multidisciplinary team of computer scientists, 3D modelers, graphic artists, and animators, Cosmo provides realtime advice to students as they escort packets through a virtual world of interconnected routers.

Lifelike animated agents for knowledge-based learning environments can provide timely, customized advice to support leaners' 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 paper proposes the emotive-kinesthetic behavior sequencing framework for dynamically sequencing lifelike pedagogical agents' full-body emotive e xpression. By exploiting a rich b e h a vior space populated with emotive b ehaviors and structured by pedagogical speech act categories, a behavior sequencing engine operates in realtime to select and assemble contextually appropriate expressive b e h a viors. This framework has been implemented in a lifelike pedagogical agent, Cosmo, who exhibits full-body emotive b e h a viors in response to learners' problem-solving activities.

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