40 research outputs found
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A legacy of learning at Whiskeytown Environmental School: Fieldnotes from an interview with Ellen Petrick
An interview with a leading NPS environmental educator
Action Selection for Interaction Management: Opportunities and Lessons for Automated Planning
The central problem in automated planning---action selection---is also a
primary topic in the dialogue systems research community, however, the
nature of research in that community is significantly different from that
of planning, with a focus on end-to-end systems and user evaluations. In
particular, numerous toolkits are available for developing speech-based
dialogue systems that include not only a method for representing states and
actions, but also a mechanism for reasoning and selecting the actions,
often combined with a technical framework designed to simplify the task of
creating end-to-end systems. We contrast this situation with that of
automated planning, and argue that the dialogue systems community could
benefit from some of the directions adopted by the planning community, and
that there also exist opportunities and lessons for automated planning
Using General-Purpose Planning for Action Selection in Human-Robot Interaction
A central problem in designing and implementing interactive
systems—action selection—is also a core research topic in
automated planning. While numerous toolkits are available
for building end-to-end interactive systems, the tight coupling
of representation, reasoning, and technical frameworks found
in these toolkits often makes it difficult to compare or change
the underlying domain models. In contrast, the automated
planning community provides general-purpose representation
languages and multiple planning engines that support these
languages. We describe our recent work on automated planning
for task-based social interaction, using a robot that must
interact with multiple humans in a bartending domain
Action Selection for Interaction Management: Opportunities and Lessons for Automated Planning
The central problem in automated planning---action selection---is also a
primary topic in the dialogue systems research community, however, the
nature of research in that community is significantly different from that
of planning, with a focus on end-to-end systems and user evaluations. In
particular, numerous toolkits are available for developing speech-based
dialogue systems that include not only a method for representing states and
actions, but also a mechanism for reasoning and selecting the actions,
often combined with a technical framework designed to simplify the task of
creating end-to-end systems. We contrast this situation with that of
automated planning, and argue that the dialogue systems community could
benefit from some of the directions adopted by the planning community, and
that there also exist opportunities and lessons for automated planning
Separating representation, reasoning, and implementation for interaction management
Numerous toolkits are available for developing speech-based dialogue systems. Many of these toolkits include not only a method for representing states and actions, but also a mechanism for reasoning and selecting the actions, often combined with a technical framework designed to simplify the task of creating end-to-end systems. This tight coupling of representation, reasoning, and implementation makes it difficult both to compare different approaches, as well as to analyse the properties of individual techniques. We contrast this situation with the state of the art in a related research area---AI planning---where a set of common representations have been defined and are widely used to enable direct comparison of different reasoning approaches. We argue that adopting a similar separation would greatly benefit the dialogue research community
What Would You Like to Drink? Recognising and Planning with Social States in a Robot Bartender Domain
Planning for Social Interaction with Sensor Uncertainty
A robot coexisting with humans must not only be able to
perform physical tasks, but must also be able to interact
with humans in a socially appropriate manner. In this
paper, we describe an extension of prior work on planning
for task-based social interaction using a robot that
must interact with multiple human agents in a simple
bartending domain. We describe how the initial state representation
developed for this robot has been extended
to handle the full range of uncertainty resulting from the
input sensors, and outline how the planner will use the
resulting uncertainty in the state during plan generation