215,315 research outputs found
Twenty questions about design behavior for sustainability, report of the International Expert Panel on behavioral science for design
How behavioral scientists, engineers, and architects can work together to
advance how we all understand and practice design—in order to enhance
sustainability in the built environment, and beyond.https://www.nature.com/documents/design_behavior_for_sustainability.pdfPublished versio
Learning to Reason: End-to-End Module Networks for Visual Question Answering
Natural language questions are inherently compositional, and many are most
easily answered by reasoning about their decomposition into modular
sub-problems. For example, to answer "is there an equal number of balls and
boxes?" we can look for balls, look for boxes, count them, and compare the
results. The recently proposed Neural Module Network (NMN) architecture
implements this approach to question answering by parsing questions into
linguistic substructures and assembling question-specific deep networks from
smaller modules that each solve one subtask. However, existing NMN
implementations rely on brittle off-the-shelf parsers, and are restricted to
the module configurations proposed by these parsers rather than learning them
from data. In this paper, we propose End-to-End Module Networks (N2NMNs), which
learn to reason by directly predicting instance-specific network layouts
without the aid of a parser. Our model learns to generate network structures
(by imitating expert demonstrations) while simultaneously learning network
parameters (using the downstream task loss). Experimental results on the new
CLEVR dataset targeted at compositional question answering show that N2NMNs
achieve an error reduction of nearly 50% relative to state-of-the-art
attentional approaches, while discovering interpretable network architectures
specialized for each question
Intrinsic Motivation Systems for Autonomous Mental Development
Exploratory activities seem to be intrinsically rewarding
for children and crucial for their cognitive development.
Can a machine be endowed with such an intrinsic motivation
system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations
which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology.
Key words: Active learning, autonomy, behavior, complexity,
curiosity, development, developmental trajectory, epigenetic
robotics, intrinsic motivation, learning, reinforcement learning,
values
Herbert Simon's decision-making approach: Investigation of cognitive processes in experts
This is a post print version of the article. The official published can be obtained from the links below - PsycINFO Database Record (c) 2010 APA, all rights reserved.Herbert Simon's research endeavor aimed to understand the processes that participate in human decision making. However, despite his effort to investigate this question, his work did not have the impact in the “decision making” community that it had in other fields. His rejection of the assumption of perfect rationality, made in mainstream economics, led him to develop the concept of bounded rationality. Simon's approach also emphasized the limitations of the cognitive system, the change of processes due to expertise, and the direct empirical study of cognitive processes involved in decision making. In this article, we argue that his subsequent research program in problem solving and expertise offered critical tools for studying decision-making processes that took into account his original notion of bounded rationality. Unfortunately, these tools were ignored by the main research paradigms in decision making, such as Tversky and Kahneman's biased rationality approach (also known as the heuristics and biases approach) and the ecological approach advanced by Gigerenzer and others. We make a proposal of how to integrate Simon's approach with the main current approaches to decision making. We argue that this would lead to better models of decision making that are more generalizable, have higher ecological validity, include specification of cognitive processes, and provide a better understanding of the interaction between the characteristics of the cognitive system and the contingencies of the environment
Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies
RoboCup soccer competitions are considered among the most challenging
multi-robot adversarial environments, due to their high dynamism and the
partial observability of the environment. In this paper we introduce a method
based on a combination of Monte Carlo search and data aggregation (MCSDA) to
adapt discrete-action soccer policies for a defender robot to the strategy of
the opponent team. By exploiting a simple representation of the domain, a
supervised learning algorithm is trained over an initial collection of data
consisting of several simulations of human expert policies. Monte Carlo policy
rollouts are then generated and aggregated to previous data to improve the
learned policy over multiple epochs and games. The proposed approach has been
extensively tested both on a soccer-dedicated simulator and on real robots.
Using this method, our learning robot soccer team achieves an improvement in
ball interceptions, as well as a reduction in the number of opponents' goals.
Together with a better performance, an overall more efficient positioning of
the whole team within the field is achieved
MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving
Autonomous driving requires operation in different behavioral modes ranging
from lane following and intersection crossing to turning and stopping. However,
most existing deep learning approaches to autonomous driving do not consider
the behavioral mode in the training strategy. This paper describes a technique
for learning multiple distinct behavioral modes in a single deep neural network
through the use of multi-modal multi-task learning. We study the effectiveness
of this approach, denoted MultiNet, using self-driving model cars for driving
in unstructured environments such as sidewalks and unpaved roads. Using labeled
data from over one hundred hours of driving our fleet of 1/10th scale model
cars, we trained different neural networks to predict the steering angle and
driving speed of the vehicle in different behavioral modes. We show that in
each case, MultiNet networks outperform networks trained on individual modes
while using a fraction of the total number of parameters.Comment: Published in IEEE WACV 201
Project Quality of Offshore Virtual Teams Engaged in Software Requirements Analysis: An Exploratory Comparative Study
The off-shore software development companies in countries such as India use a global delivery model in which initial requirement analysis phase of software projects get executed at client locations to leverage frequent and deep interaction between user and developer teams. Subsequent phases such as design, coding and testing are completed at off-shore locations. Emerging trends indicate an increasing interest in off-shoring even requirements analysis phase using computer mediated communication. We conducted an exploratory research study involving students from Management Development Institute (MDI), India and Marquette University (MU), USA to determine quality of such off-shored requirements analysis projects. Our findings suggest that project quality of teams engaged in pure off-shore mode is comparable to that of teams engaged in collocated mode. However, the effect of controls such as user project monitoring on the quality of off-shored projects needs to be studied further
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