83,545 research outputs found
SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning
Deep reinforcement learning (DRL) has gained great success by learning
directly from high-dimensional sensory inputs, yet is notorious for the lack of
interpretability. Interpretability of the subtasks is critical in hierarchical
decision-making as it increases the transparency of black-box-style DRL
approach and helps the RL practitioners to understand the high-level behavior
of the system better. In this paper, we introduce symbolic planning into DRL
and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can
handle both high-dimensional sensory inputs and symbolic planning. The
task-level interpretability is enabled by relating symbolic actions to
options.This framework features a planner -- controller -- meta-controller
architecture, which takes charge of subtask scheduling, data-driven subtask
learning, and subtask evaluation, respectively. The three components
cross-fertilize each other and eventually converge to an optimal symbolic plan
along with the learned subtasks, bringing together the advantages of long-term
planning capability with symbolic knowledge and end-to-end reinforcement
learning directly from a high-dimensional sensory input. Experimental results
validate the interpretability of subtasks, along with improved data efficiency
compared with state-of-the-art approaches
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network
Music creation is typically composed of two parts: composing the musical
score, and then performing the score with instruments to make sounds. While
recent work has made much progress in automatic music generation in the
symbolic domain, few attempts have been made to build an AI model that can
render realistic music audio from musical scores. Directly synthesizing audio
with sound sample libraries often leads to mechanical and deadpan results,
since musical scores do not contain performance-level information, such as
subtle changes in timing and dynamics. Moreover, while the task may sound like
a text-to-speech synthesis problem, there are fundamental differences since
music audio has rich polyphonic sounds. To build such an AI performer, we
propose in this paper a deep convolutional model that learns in an end-to-end
manner the score-to-audio mapping between a symbolic representation of music
called the piano rolls and an audio representation of music called the
spectrograms. The model consists of two subnets: the ContourNet, which uses a
U-Net structure to learn the correspondence between piano rolls and
spectrograms and to give an initial result; and the TextureNet, which further
uses a multi-band residual network to refine the result by adding the spectral
texture of overtones and timbre. We train the model to generate music clips of
the violin, cello, and flute, with a dataset of moderate size. We also present
the result of a user study that shows our model achieves higher mean opinion
score (MOS) in naturalness and emotional expressivity than a WaveNet-based
model and two commercial sound libraries. We open our source code at
https://github.com/bwang514/PerformanceNetComment: 8 pages, 6 figures, AAAI 2019 camera-ready versio
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
Recommended from our members
Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
Towards a framework for investigating tangible environments for learning
External representations have been shown to play a key role in mediating cognition. Tangible environments offer the opportunity for novel representational formats and combinations, potentially increasing representational power for supporting learning. However, we currently know little about the specific learning benefits of tangible environments, and have no established framework within which to analyse the ways that external representations work in tangible environments to support learning. Taking external representation as the central focus, this paper proposes a framework for investigating the effect of tangible technologies on interaction and cognition. Key artefact-action-representation relationships are identified, and classified to form a structure for investigating the differential cognitive effects of these features. An example scenario from our current research is presented to illustrate how the framework can be used as a method for investigating the effectiveness of differential designs for supporting science learning
- …