37,335 research outputs found
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications
One major factor impeding more widespread adoption of deep neural networks
(DNNs) is their lack of robustness, which is essential for safety-critical
applications such as autonomous driving. This has motivated much recent work on
adversarial attacks for DNNs, which mostly focus on pixel-level perturbations
void of semantic meaning. In contrast, we present a general framework for
adversarial attacks on trained agents, which covers semantic perturbations to
the environment of the agent performing the task as well as pixel-level
attacks. To do this, we re-frame the adversarial attack problem as learning a
distribution of parameters that always fools the agent. In the semantic case,
our proposed adversary (denoted as BBGAN) is trained to sample parameters that
describe the environment with which the black-box agent interacts, such that
the agent performs its dedicated task poorly in this environment. We apply
BBGAN on three different tasks, primarily targeting aspects of autonomous
navigation: object detection, self-driving, and autonomous UAV racing. On these
tasks, BBGAN can generate failure cases that consistently fool a trained agent.Comment: Accepted at AAAI'2
Learn to Interpret Atari Agents
Deep Reinforcement Learning (DeepRL) agents surpass human-level performances
in a multitude of tasks. However, the direct mapping from states to actions
makes it hard to interpret the rationale behind the decision making of agents.
In contrast to previous a-posteriori methods of visualizing DeepRL policies, we
propose an end-to-end trainable framework based on Rainbow, a representative
Deep Q-Network (DQN) agent. Our method automatically learns important regions
in the input domain, which enables characterizations of the decision making and
interpretations for non-intuitive behaviors. Hence we name it Region Sensitive
Rainbow (RS-Rainbow). RS-Rainbow utilizes a simple yet effective mechanism to
incorporate visualization ability into the learning model, not only improving
model interpretability, but leading to improved performance. Extensive
experiments on the challenging platform of Atari 2600 demonstrate the
superiority of RS-Rainbow. In particular, our agent achieves state of the art
at just 25% of the training frames. Demonstrations and code are available at
https://github.com/yz93/Learn-to-Interpret-Atari-Agents
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