Personal assistant agents capable of proactively offering assistance can be more helpful to their users through their ability to perform tasks that otherwise would require user involvement. This article characterizes the properties desired of proactive behavior by a personal assistant agent in the realm of task management and develops an operational framework to implement such capabilities. We present an extended agent architectural model that features a meta-level layer charged with identifying potentially helpful actions and determining when it is appropriate to perform them. The reasoning that answers these questions draws on a theory of proactivity that describes user desires and a model of helpfulness. Operationally, assistance patterns represent a compiled form of this knowledge, instantiating meta- * Authors listed in reverse alphabetical order. 1250004-1N. Yorke-Smith et al.reasoning over the agent's beliefs about its user's activities as well as over world state. The resulting generic framework for proactive goal generation and deliberation has been implemented as part of a personal assistant agent in the computer desktop domain.

We will demonstrate distributed conflict resolution in the context of personalized meeting scheduling. The demonstration will show how distributed constraint optimization can be used to facilitate interaction between cognitive agents and their users. The system is part of the CALO personal cognitive assistant that will also be explored during the demonstration.

The Adept Task Learning system is an end-user programming environment that combines programming by demonstration and direct manipulation to support customization by nonprogrammers. Previously, Adept enforced a rigid procedure-authoring workflow consisting of demonstration followed by editing. However, a series of system evaluations with end users revealed a desire for more feedback during learning and more flexibility in authoring. We present a new approach that interleaves incremental learning from demonstration and assisted editing to provide users with a more flexible procedureauthoring experience. The approach relies on maintaining a "soup" of alternative hypotheses during learning, propagating user edits through the soup, and suggesting repairs as needed. We discuss the learning and reasoning techniques that support the new approach and identify the unique interaction design challenges they raise, concluding with an evaluation plan to resolve the design challenges and complete the improved system.