Planning as Mixed-initiative Goal Manipulation

Michael T. Cox and Chen Zhang

Mixed-initiative planning systems attempt to integrate human and AI planners so that the synthesis results in high quality plans. In the AI community, the dominant model of planning is search. In state-space planning, search consists of backward and forward chaining through the effects and preconditions of operator representations. Although search is an acceptable mechanism to use in performing automated planning, we present an alternative model to present to the user at the interface of a mixed-initiative planning system. That is we propose to model planning as a goal manipulation task. Here planning involves moving goals through a hyperspace in order to reach equilibrium between available resources and the constraints of a dynamic environment. The users can establish and "steer" goals through a visual representation of the planning domain. They can associate resources with particular goals and shift goals along various dimensions in response to changing conditions as well as change the structure of previous plans. Users need not know details of the underlying technology, even when search is used within. Here we empirically examine user performance under both alternatives and see that many users do better with the alternative model.


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