21,591 research outputs found
Prediction of intent in robotics and multi-agent systems.
Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great challenge due to the
lack of connectives as strong linguistic cues, which motivates the use of
annotated implicit connectives to improve the recognition. We propose a feature
imitation framework in which an implicit relation network is driven to learn
from another neural network with access to connectives, and thus encouraged to
extract similarly salient features for accurate classification. We develop an
adversarial model to enable an adaptive imitation scheme through competition
between the implicit network and a rival feature discriminator. Our method
effectively transfers discriminability of connectives to the implicit features,
and achieves state-of-the-art performance on the PDTB benchmark.Comment: To appear in ACL201
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Interactive intelligence: behaviour-based AI, musical HCI and the Turing Test
The field of behaviour-based artificial intelligence (AI), with its roots in the robotics research of Rodney Brooks, is not predominantly tied to linguistic interaction in the sense of the classic Turing test (or, "imitation game"). Yet, it is worth noting, both are centred on a behavioural model of intelligence. Similarly, there is no intrinsic connection between musical AI and the language-based Turing test, though there have been many attempts to forge connections between them. Nonetheless, there are aspects of musical AI and the Turing test that can be considered in the context of non-language-based interactive environments–-in particular, when dealing with real-time musical AI, especially interactive improvisation software. This paper draws out the threads of intentional agency and human indistinguishability from Turing’s original 1950 characterisation of AI. On the basis of this distinction, it considers different approaches to musical AI. In doing so, it highlights possibilities for non-hierarchical interplay between human and computer agents
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
A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition
This study introduces PV-RNN, a novel variational RNN inspired by the
predictive-coding ideas. The model learns to extract the probabilistic
structures hidden in fluctuating temporal patterns by dynamically changing the
stochasticity of its latent states. Its architecture attempts to address two
major concerns of variational Bayes RNNs: how can latent variables learn
meaningful representations and how can the inference model transfer future
observations to the latent variables. PV-RNN does both by introducing adaptive
vectors mirroring the training data, whose values can then be adapted
differently during evaluation. Moreover, prediction errors during
backpropagation, rather than external inputs during the forward computation,
are used to convey information to the network about the external data. For
testing, we introduce error regression for predicting unseen sequences as
inspired by predictive coding that leverages those mechanisms. The model
introduces a weighting parameter, the meta-prior, to balance the optimization
pressure placed on two terms of a lower bound on the marginal likelihood of the
sequential data. We test the model on two datasets with probabilistic
structures and show that with high values of the meta-prior the network
develops deterministic chaos through which the data's randomness is imitated.
For low values, the model behaves as a random process. The network performs
best on intermediate values, and is able to capture the latent probabilistic
structure with good generalization. Analyzing the meta-prior's impact on the
network allows to precisely study the theoretical value and practical benefits
of incorporating stochastic dynamics in our model. We demonstrate better
prediction performance on a robot imitation task with our model using error
regression compared to a standard variational Bayes model lacking such a
procedure.Comment: The paper is accepted in Neural Computatio
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