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

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