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

Publisher Summary This chapter explores the Procedure Learning On the Web (PLOW) that demonstrates natural language (NL) is a powerful intuitive tool for end users to build Web tasks with significant complexity using only a single demonstration. The natural play-by-play demonstration that would occur in human–human teaching provides enough information for the system to generalize demonstrated actions. Mixed-initiative interaction also makes the task-building process much more convenient and intuitive. Without the system's proactive involvement in learning, the human instructor's job could become very tedious, difficult, and complex. Semantic information in NL description also makes the system more robust by letting it handle the dynamic nature of the Web. Although PLOW sheds more light on NL's roles and the collaborative problem-solving aspects in the end-user programming on the Web, significant challenges still exist and new ones will emerge as application domains are expanded. Better reasoning about tasks, broader coverage of language understanding, and more robust Web object handling will be needed to address the challenges.

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Going beyond PBD: A play-by-play and mixed-initiative approach

One of the challenges of building a policy management framework is making it flexible enough to handle differences in both policy semantics and enforcement strategies across multiple platforms and application domains. The system must be expressive enough in each application domain to provide the richness needed for interesting policies. It must also provide a simple and flexible enforcement mechanism for adaptation to a variety of systems. In this paper we discuss the application of the KAoS policy services framework to human-robot teamwork - an application that involves a variety of application domains and enforcement at different levels of control; from low level network resource control to high level organizational constraints and coordination management. The study culminated in an outdoor field exercise that required coordination of mixed sub teams composed of two people and five robots whose task was to find and apprehend an intruder on a Navy pier.

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Policy Management across Multiple Platforms and Application Domains

Distributed Constraint Satisfaction Problems (DCSP) is a general framework for multi-agent coordination and conflict resolution. While inter-agent communication in most DCSP algorithms is restricted to only exchanging values of variables, this paper provides a detailed experimental investigation of the impact of inter-agent exchange of additional legal values among agents, within a collaborative setting. We present a new run-time model that takes into account the overhead of the additional communication in various computing/networking environments and show that DCSP strategies with extra communication can lead to significant performance improvement in realistic domains.

On Communication in Distributed Constraint Satisfaction

This chapter describes a dialog system for task learning and its application to textual user interfaces. Our system, PLOW, uses observation of user demonstration, together with the user’s play-by-play description of that demonstration, to learn complex tasks. We describe some preliminary experiments which show that this technique may make it possible for users without any programming experience to create tasks via natural language.

Play-by-Play Learning for Textual User Interfaces

Recently researchers in multiagent systems have begun to focus on formal POMDP (Partially Observable Markov Decision Process) models for analysis of multiagent coordination. However, prior work has mostly focused on analysis of communication, such as via the COM-MTDP (Communicative Markov Team Decision Problem) model. This paper provides two extensions to this prior work that goes beyond communication and analyzes other aspects of multiagent coordination. In particular, we first present a formal model called R-COM-MTDP that extends COM-MTDP to analyze team formation and reorganization algorithms. R-COM-MTDP enables a rigorous and systematic analysis of complexity-optimality tradeoffs in team (re)formation approaches in different domain types. It provides the worst-case complexity analysis of the team (re)formation under varying conditions, and illustrates under which conditions role decomposition can provide significant reductions in computational complexity. Next, we propose COM-MTDP as a formal framework to analyze DCSP (Distributed Constraint Satisfaction Problem) strategies for conflict resolution. Different DCSP strategies are mapped onto policies in the COM-MTDP model, and agents compare strategies by evaluating their mapped policies. Thus, the two COM-MTDP based methods could open the door to a range of novel analyses of multiagent team (re)formation, and facilitate automated selection of the most efficient strategy for a given situation.

Hyuckchul Jung

Papers

Going beyond PBD: A play-by-play and mixed-initiative approach

Policy Management across Multiple Platforms and Application Domains

On Communication in Distributed Constraint Satisfaction

Play-by-Play Learning for Textual User Interfaces

Computational models for multiagent coordination analysis: Extending distributed POMDP models