Artificial Intelligence

---

Surrogate Scoring Rules and a Uniform Dominant Truth Serum (1802.09158v4)

Yang Liu, Yiling Chen

2018-02-26

Strictly proper scoring rules (SPSR) are widely used when designing incentive mechanisms to elicit private information from strategic agents using realized ground truth signals, and they can help quantify the value of elicited information. In this paper, we extend such scoring rules to settings where a mechanism designer does not have access to ground truth. We consider two such settings: (i) a setting when the mechanism designer has access to a noisy proxy version of the ground truth, with {\em known} biases; and (ii) the standard peer prediction setting where agents' reports, and possibly some limited prior knowledge of ground truth, are the only source of information that the mechanism designer has. We introduce {\em surrogate scoring rules} (SSR) for the first setting, which use the noisy ground truth to evaluate quality of elicited information. We show that SSR preserves the strict properness of SPSR. Using SSR, we then develop a multi-task scoring mechanism -- called \emph{uniform dominant truth serum} (DTS) -- to achieve strict properness when there are sufficiently many tasks and agents, and when the mechanism designer only has access to agents' reports and one bit information about the marginal of the entire set of tasks' ground truth. In comparison to standard equilibrium concepts in peer prediction, we show that DTS can achieve truthfulness in \emph{uniform dominant strategy} in a multi-task setting when agents adopt the same strategy for all the tasks that they are assigned (hence the term uniform). A salient feature of SSR and DTS is that they quantify the quality of information despite lack of ground truth, just as proper scoring rules do for the {\em with} verification setting. Our method is verified both theoretically and empirically using data collected from real human participants.

Pragmatic inference and visual abstraction enable contextual flexibility during visual communication (1903.04448v1)

Judith Fan, Robert Hawkins, Mike Wu, Noah Goodman

2019-03-11

Visual modes of communication are ubiquitous in modern life. Here we investigate drawing, the most basic form of visual communication. Communicative drawing poses a core challenge for theories of how vision and social cognition interact, requiring a detailed understanding of how sensory information and social context jointly determine what information is relevant to communicate. Participants (N=192) were paired in an online environment to play a sketching-based reference game. On each trial, both participants were shown the same four objects, but in different locations. The sketcher's goal was to draw one of these objects - the target - so that the viewer could select it from the array. There were two types of trials: close, where objects belonged to the same basic-level category, and far, where objects belonged to different categories. We found that people exploited information in common ground with their partner to efficiently communicate about the target: on far trials, sketchers achieved high recognition accuracy while applying fewer strokes, using less ink, and spending less time on their drawings than on close trials. We hypothesized that humans succeed in this task by recruiting two core competencies: (1) visual abstraction, the capacity to perceive the correspondence between an object and a drawing of it; and (2) pragmatic inference, the ability to infer what information would help a viewer distinguish the target from distractors. To evaluate this hypothesis, we developed a computational model of the sketcher that embodied both competencies, instantiated as a deep convolutional neural network nested within a probabilistic program. We found that this model fit human data well and outperformed lesioned variants, providing an algorithmically explicit theory of how perception and social cognition jointly support contextual flexibility in visual communication.

Physics Enhanced Artificial Intelligence (1903.04442v1)

Patrick O'Driscoll, Jaehoon Lee, Bo Fu

2019-03-11

We propose that intelligently combining models from the domains of Artificial Intelligence or Machine Learning with Physical and Expert models will yield a more "trustworthy" model than any one model from a single domain, given a complex and narrow enough problem. Based on mean-variance portfolio theory and bias-variance trade-off analysis, we prove combining models from various domains produces a model that has lower risk, increasing user trust. We call such combined models - physics enhanced artificial intelligence (PEAI), and suggest use cases for PEAI.

Better-than-expert detection of early coronary artery occlusion from 12 lead electrocardiograms using deep learning (1903.04421v1)

Rob Brisk, Raymond R Bond. Dewar D Finlay, James McLaughlin, Alicja Piadlo, Stephen J Leslie, David E Gossman, Ian B A Menown, David J McEneaney

2019-03-11

Early diagnosis of acute coronary artery occlusion based on electrocardiogram (ECG) findings is essential for prompt delivery of primary percutaneous coronary intervention. Current ST elevation (STE) criteria are specific but insensitive. Consequently, it is likely that many patients are missing out on potentially life-saving treatment. Experts combining non-specific ECG changes with STE detect ischaemia with higher sensitivity, but at the cost of specificity. We show that a deep learning model can detect ischaemia caused by acute coronary artery occlusion with a better balance of sensitivity and specificity than STE criteria, existing computerised analysers or expert cardiologists.

Building an Affordances Map with Interactive Perception (1903.04413v1)

Leni K. Le Goff, Oussama Yaakoubi, Alexandre Coninx, Stephane Doncieux

2019-03-11

Robots need to understand their environment to perform their task. If it is possible to pre-program a visual scene analysis process in closed environments, robots operating in an open environment would benefit from the ability to learn it through their interaction with their environment. This ability furthermore opens the way to the acquisition of affordances maps in which the action capabilities of the robot structure its visual scene understanding. We propose an approach to build such affordances maps by relying on an interactive perception approach and an online classification. In the proposed formalization of affordances, actions and effects are related to visual features, not objects, and they can be combined. We have tested the approach on three action primitives and on a real PR2 robot.

Stroke-based Artistic Rendering Agent with Deep Reinforcement Learning (1903.04411v1)

Zhewei Huang, Wen Heng, Shuchang Zhou

2019-03-11

Excellent painters can use only a few strokes to create a fantastic painting, which is a symbol of human intelligence and art. Reversing the simulator to interpret images is also a challenging task of computer vision in recent years. In this paper, we present SARA, a stroke-based artistic rendering agent that combines the neural renderer and deep reinforcement learning (DRL), allowing the machine to learn the ability to deconstruct images using strokes and create amazing visual effects. Our agent is an end-to-end program that converts natural images into paintings. The training process does not require the experience of human painting or stroke tracking data.

Accuracy Booster: Performance Boosting using Feature Map Re-calibration (1903.04407v1)

Pravendra Singh, Pratik Mazumder, Vinay P. Namboodiri

2019-03-11

Convolution Neural Networks (CNN) have been extremely successful in solving intensive computer vision tasks. The convolutional filters used in CNNs have played a major role in this success, by extracting useful features from the inputs. Recently researchers have tried to boost the performance of CNNs by re-calibrating the feature maps produced by these filters, e.g., Squeeze-and-Excitation Networks (SENets). These approaches have achieved better performance by \textit{Exciting} up the important channels or feature maps while diminishing the rest. However, in the process, architectural complexity has increased. We propose an architectural block that introduces much lower complexity than the existing methods of CNN performance boosting while performing significantly better than them. We carry out experiments on the CIFAR, ImageNet and MS-COCO datasets, and show that the proposed block can challenge the state-of-the-art results. Our method boosts the ResNet-50 architecture to perform comparably to the ResNet-152 architecture, which is a three times deeper network, on classification. We also show experimentally that our method is not limited to classification but also generalizes well to other tasks such as object detection.

Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research (1903.00742v2)

Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel

2019-03-02

Evolution has produced a multi-scale mosaic of interacting adaptive units. Innovations arise when perturbations push parts of the system away from stable equilibria into new regimes where previously well-adapted solutions no longer work. Here we explore the hypothesis that multi-agent systems sometimes display intrinsic dynamics arising from competition and cooperation that provide a naturally emergent curriculum, which we term an autocurriculum. The solution of one social task often begets new social tasks, continually generating novel challenges, and thereby promoting innovation. Under certain conditions these challenges may become increasingly complex over time, demanding that agents accumulate ever more innovations.

Reachability and Coverage Planning for Connected Agents: Extended Version (1903.04300v1)

Tristan Charrier, Arthur Queffelec, Ocan Sankur, François Schwarzentruber

2019-03-11

Motivated by the increasing appeal of robots in information-gathering missions, we study multi-agent path planning problems in which the agents must remain interconnected. We model an area by a topological graph specifying the movement and the connectivity constraints of the agents. We study the theoretical complexity of the reachability and the coverage problems of a fleet of connected agents on various classes of topological graphs. We establish the complexity of these problems on known classes, and introduce a new class called sight-moveable graphs which admit efficient algorithms.

An Optimization Framework for Task Sequencing in Curriculum Learning (1901.11478v2)

Francesco Foglino, Christiano Coletto Christakou, Matteo Leonetti

2019-01-31

Curriculum learning in reinforcement learning is used to shape exploration by presenting the agent with increasingly complex tasks. The idea of curriculum learning has been largely applied in both animal training and pedagogy. In reinforcement learning, all previous task sequencing methods have shaped exploration with the objective of reducing the time to reach a given performance level. We propose novel uses of curriculum learning, which arise from choosing different objective functions. Furthermore, we define a general optimization framework for task sequencing and evaluate the performance of popular metaheuristic search methods on several tasks. We show that curriculum learning can be successfully used to: improve the initial performance, take fewer suboptimal actions during exploration, and discover better policies.

---