In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

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Situations and Attitudes.

Human-Robot Interaction (HRI) has recently received considerable attention in the academic community, in labs, in technology companies, and through the media. Because of this attention, it is desirable to present a survey of HRI to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HRI within the field. The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coherent "story" of HRI means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HRI story from multiple perspectives with an eye toward identifying themes that cross applications. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HRI, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design.

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Human-Robot Interaction: A Survey

Normal accidents. Living with high-risk technologies

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

The various methods and devices described herein relate to devices which, in at least certain embodiments, may include one or more sensors for providing data relating to user activity and at least one processor for causing the device to respond based on the user activity which was determined, at least in part, through the sensors. The response by the device may include a change of state of the device, and the response may be automatically performed after the user activity is determined.

Automated Response to and Sensing of User Activity in Portable Devices

A system which allows a user to teach a computational device how to perform complex, repetitive tasks that the user usually would perform using the device's graphical user interface (GUI) often but not limited to being a web browser. The system includes software running on a user's computational device. The user “teaches” task steps by inputting natural language and demonstrating actions with the GUI. The system uses a semantic ontology and natural language processing to create an explicit representation of the task that is stored on the computer. After a complete task has been taught, the system is able to automatically execute the task in new situations. Because the task is represented in terms of the ontology and user's intentions, the system is able to adapt to changes in the computer code while still pursuing the objectives taught by the user.

Interactive complex task teaching system that allows for natural language input, recognizes a user's intent, and automatically performs tasks in document object model (DOM) nodes

To be effective, an agent that collaborates with humans needs to be able to learn new tasks from humans they work with. This paper describes a system that learns executable task models from a single collaborative learning session consisting of demonstration, explanation and dialogue. To accomplish this, the system integrates a range of AI technologies: deep natural language understanding, knowledge representation and reasoning, dialogue systems, planning/agent-based systems and machine learning. A formal evaluation shows the approach has great promise.

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PLOW: a collaborative task learning agent

Several research groups have grappled with the problem of characterizing and developing practical approaches for implementing adjustable autonomy and mixed-initiative interaction in deployed systems. However, each group takes a little different approach and uses variations of the same terminology in a somewhat different fashion. In this chapter, we will describe some common dimensions in an effort to better understand these important but ill-characterized topics. We are developing a formalism and implementation of these concepts as part of the KAoS framework in the context of our research on policy-governed autonomous systems.

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Dimensions of adjustable autonomy and mixed-initiative interaction

The KAoS policy management framework pioneered the use of semantically-rich ontological representation and reasoning to specify, analyze, deconflict, and enforce policies [9, 10]. The framework has continued to evolve over the last five years, inspired by both technological advances and the practical needs of its varied applications. In this paper, we describe how these applications have motivated the partitioning of components into a well-defined three-layer policy management architecture that hides ontology complexity from the human user and from the policy-governed system. The power of semantic reasoning is embedded in the middle layer of the architecture where it can provide the most benefit. We also describe how the policy semantics of the core KAoS policy ontology has grown in its comprehensiveness. The flexible and mature architecture of KAoS enables straightforward integration with a variety of deployment platforms, ranging from highly distributed systems, such as the AFRL information management system, to human-robotic interaction, to dynamic management of quality-of-service and cross-domain information management of wireless networks in resource-constrained or security-sensitive environments.

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Hyuckchul Jung

Papers

Interactive complex task teaching system that allows for natural language input, recognizes a user's intent, and automatically performs tasks in document object model (DOM) nodes

PLOW: a collaborative task learning agent

Dimensions of adjustable autonomy and mixed-initiative interaction

New Developments in Ontology-Based Policy Management: Increasing the Practicality and Comprehensiveness of KAoS

Dimensions of adjustable autonomy and mixed-initiative-interaction