1,649 research outputs found
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Nonstrict hierarchical reinforcement learning for interactive systems and robots
Conversational systems and robots that use reinforcement learning for policy optimization in large domains often face the problem of limited scalability. This problem has been addressed either by using function approximation techniques that estimate the approximate true value function of a policy or by using a hierarchical decomposition of a learning task into subtasks. We present a novel approach for dialogue policy optimization that combines the benefits of both hierarchical control and function approximation and that allows flexible transitions between dialogue subtasks to give human users more control over the dialogue. To this end, each reinforcement learning agent in the hierarchy is extended with a subtask transition function and a dynamic state space to allow flexible switching between subdialogues. In addition, the subtask policies are represented with linear function approximation in order to generalize the decision making to situations unseen in training. Our proposed approach is evaluated in an interactive conversational robot that learns to play quiz games. Experimental results, using simulation and real users, provide evidence that our proposed approach can lead to more flexible (natural) interactions than strict hierarchical control and that it is preferred by human users
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
We investigate the task of building open domain, conversational dialogue
systems based on large dialogue corpora using generative models. Generative
models produce system responses that are autonomously generated word-by-word,
opening up the possibility for realistic, flexible interactions. In support of
this goal, we extend the recently proposed hierarchical recurrent
encoder-decoder neural network to the dialogue domain, and demonstrate that
this model is competitive with state-of-the-art neural language models and
back-off n-gram models. We investigate the limitations of this and similar
approaches, and show how its performance can be improved by bootstrapping the
learning from a larger question-answer pair corpus and from pretrained word
embeddings.Comment: 8 pages with references; Published in AAAI 2016 (Special Track on
Cognitive Systems
Social Network Analysis Using a Multi-agent System: A School System Case
The quality of k-12 education has been a major concern in the nation for years. School systems, just like many other social networks, appear to have a hierarchical structure. Understanding this structure could be the key to better evaluate student performance and improve school quality. Many researches have been focusing on detecting hierarchical structure by using hierarchical clustering algorithms. Compared to existing methods, we design an interaction-based similarity measure to accomplish hierarchical clustering in order to detect hierarchical structures in social networks (e.g. school district networks). This method uses a Multi-agent System for it is based on agent interactions. With the network structure detected, we also build a model, which is inspired by the MAXQ algorithm, to decompose funding policy task into subtask and then evaluate these subtasks by using funding distribution policies from past years and looking for possible relationships between student performances and funding policies. For experiment, we use real school data from Bexar county’s 15 school districts. The first result shows that our interaction based method is able to generate meaningful clustering and dendrogram for social networks. And our policy evaluation model is able to evaluate funding policies from past three years in Bexar County and conclude that increasing funding does not necessarily have a positive impact on student performance and it is generally not the case that the more spend the better
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
Survey on reinforcement learning for language processing
In recent years some researchers have explored the use of reinforcement
learning (RL) algorithms as key components in the solution of various natural
language processing tasks. For instance, some of these algorithms leveraging
deep neural learning have found their way into conversational systems. This
paper reviews the state of the art of RL methods for their possible use for
different problems of natural language processing, focusing primarily on
conversational systems, mainly due to their growing relevance. We provide
detailed descriptions of the problems as well as discussions of why RL is
well-suited to solve them. Also, we analyze the advantages and limitations of
these methods. Finally, we elaborate on promising research directions in
natural language processing that might benefit from reinforcement learning
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