65,177 research outputs found
PRESENCE: A human-inspired architecture for speech-based human-machine interaction
Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system
Expert system technology
The expert system is a computer program which attempts to reproduce the problem-solving behavior of an expert, who is able to view problems from a broad perspective and arrive at conclusions rapidly, using intuition, shortcuts, and analogies to previous situations. Expert systems are a departure from the usual artificial intelligence approach to problem solving. Researchers have traditionally tried to develop general modes of human intelligence that could be applied to many different situations. Expert systems, on the other hand, tend to rely on large quantities of domain specific knowledge, much of it heuristic. The reasoning component of the system is relatively simple and straightforward. For this reason, expert systems are often called knowledge based systems. The report expands on the foregoing. Section 1 discusses the architecture of a typical expert system. Section 2 deals with the characteristics that make a problem a suitable candidate for expert system solution. Section 3 surveys current technology, describing some of the software aids available for expert system development. Section 4 discusses the limitations of the latter. The concluding section makes predictions of future trends
The nature and evaluation of commercial expert system building tools, revision 1
This memorandum reviews the factors that constitute an Expert System Building Tool (ESBT) and evaluates current tools in terms of these factors. Evaluation of these tools is based on their structure and their alternative forms of knowledge representation, inference mechanisms and developer end-user interfaces. Next, functional capabilities, such as diagnosis and design, are related to alternative forms of mechanization. The characteristics and capabilities of existing commercial tools are then reviewed in terms of these criteria
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
Towards a Neural Era in Dialogue Management for Collaboration: A Literature Survey
Dialogue-based human-AI collaboration can revolutionize collaborative
problem-solving, creative exploration, and social support. To realize this
goal, the development of automated agents proficient in skills such as
negotiating, following instructions, establishing common ground, and
progressing shared tasks is essential. This survey begins by reviewing the
evolution of dialogue management paradigms in collaborative dialogue systems,
from traditional handcrafted and information-state based methods to AI
planning-inspired approaches. It then shifts focus to contemporary data-driven
dialogue management techniques, which seek to transfer deep learning successes
from form-filling and open-domain settings to collaborative contexts. The paper
proceeds to analyze a selected set of recent works that apply neural approaches
to collaborative dialogue management, spotlighting prevailing trends in the
field. This survey hopes to provide foundational background for future
advancements in collaborative dialogue management, particularly as the dialogue
systems community continues to embrace the potential of large language models
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning
Providing Emotional Support (ES) to soothe people in emotional distress is an
essential capability in social interactions. Most existing researches on
building ES conversation systems only considered single-turn interactions with
users, which was over-simplified. In comparison, multi-turn ES conversation
systems can provide ES more effectively, but face several new technical
challenges, including: (1) how to adopt appropriate support strategies to
achieve the long-term dialogue goal of comforting the user's emotion; (2) how
to dynamically model the user's state. In this paper, we propose a novel system
MultiESC to address these issues. For strategy planning, drawing inspiration
from the A* search algorithm, we propose lookahead heuristics to estimate the
future user feedback after using particular strategies, which helps to select
strategies that can lead to the best long-term effects. For user state
modeling, MultiESC focuses on capturing users' subtle emotional expressions and
understanding their emotion causes. Extensive experiments show that MultiESC
significantly outperforms competitive baselines in both dialogue generation and
strategy planning. Our codes are available at
https://github.com/lwgkzl/MultiESC.Comment: Accepted by the main conference of EMNLP 202
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