35,652 research outputs found
A Plan-Based Model for Response Generation in Collaborative Task-Oriented Dialogues
This paper presents a plan-based architecture for response generation in
collaborative consultation dialogues, with emphasis on cases in which the
system (consultant) and user (executing agent) disagree. Our work contributes
to an overall system for collaborative problem-solving by providing a
plan-based framework that captures the {\em Propose-Evaluate-Modify} cycle of
collaboration, and by allowing the system to initiate subdialogues to negotiate
proposed additions to the shared plan and to provide support for its claims. In
addition, our system handles in a unified manner the negotiation of proposed
domain actions, proposed problem-solving actions, and beliefs proposed by
discourse actions. Furthermore, it captures cooperative responses within the
collaborative framework and accounts for why questions are sometimes never
answered.Comment: 8 pages, to appear in the Proceedings of AAAI-94. LaTeX source file,
requires aaai.sty and epsf.tex. Figures included in separate file
Technology assessment of advanced automation for space missions
Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology
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Dialogue with computers: dialogue games in action
With the advent of digital personal assistants for mobile devices, systems that are marketed as engaging in (spoken) dialogue have reached a wider public than ever before. For a student of dialogue, this raises the question to what extent such systems are genuine dialogue partners. In order to address this question, this study proposes to use the concept of a dialogue game as an analytical tool. Thus, we reframe the question as asking for the dialogue games that such systems play. Our analysis, as applied to a number of landmark systems and illustrated with dialogue extracts, leads to a fine-grained classification of such systems. Drawing on this analysis, we propose that the uptake of future generations of more powerful dialogue systems will depend on whether they are self-validating. A self-validating dialogue system can not only talk and do things, but also discuss the why of what it says and does, and learn from such discussions
OFMTutor: An operator function model intelligent tutoring system
The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described
Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog
A number of recent works have proposed techniques for end-to-end learning of
communication protocols among cooperative multi-agent populations, and have
simultaneously found the emergence of grounded human-interpretable language in
the protocols developed by the agents, all learned without any human
supervision!
In this paper, using a Task and Tell reference game between two agents as a
testbed, we present a sequence of 'negative' results culminating in a
'positive' one -- showing that while most agent-invented languages are
effective (i.e. achieve near-perfect task rewards), they are decidedly not
interpretable or compositional.
In essence, we find that natural language does not emerge 'naturally',
despite the semblance of ease of natural-language-emergence that one may gather
from recent literature. We discuss how it is possible to coax the invented
languages to become more and more human-like and compositional by increasing
restrictions on how two agents may communicate.Comment: 9 pages, 7 figures, 2 tables, accepted at EMNLP 2017 as short pape
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