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Research talk

Adapting Knowledge-Level Planning for Natural Language Dialogue, R. Petrick, slides from a talk presented at the Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK, 2010-03-19.

[ slides ]

Abstract

The problem of planning a sequence of natural language dialogue moves has many parallels to the general AI planning problem, and can be viewed as an instance of the problem of planning with incomplete information and sensing. While "classical" planning actions model actions that change the state of the world, sensing actions change the knowledge state of the agent, often leaving the world state unchanged. Sensing actions also complicate the planning process, giving rise to potentially infinite state spaces and requiring planning systems with the ability to reason about an agent's knowledge state as distinct from the world state. Recent approaches from the knowledge representation and planning communities, however, have been effective at overcoming some of the representational and inferential drawbacks of sensing actions, and such actions have been applied in a variety of domains.

In this talk, I describe ongoing work aimed at adapting automated planning techniques for natural language dialogue. I give an overview of PKS (Planning with Knowledge and Sensing), a contingent planner that forms the basis of our approach. Unlike traditional planners, PKS operates at the "knowledge level" to generate plans using sensing actions and incomplete information, by reasoning about the planner's knowledge state rather than the world state. PKS also supports features like functions, run-time variables, and program-like constructs, as part of its action representation. Building on ideas from the knowledge representation and planning communities, I describe a set of extensions to PKS for speech act-based dialogue, illustrating that the same underlying techniques for ordinary action planning can be applied to dialogue planning.

This talk describes joint work with M. Steedman and is presented in the context of PACO-PLUS, an EU project investigating perception, action, and language in real-world robot environments.