4,225 research outputs found
Q-CP: Learning Action Values for Cooperative Planning
Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Distributed Event-Triggered Online Learning for Multi-Agent System Control using Gaussian Process Regression
For the cooperative control of multi-agent systems with unknown dynamics,
data-driven methods are commonly employed to infer models from the collected
data. Due to the flexibility to model nonlinear functions and the existence of
theoretical prediction error bound, Gaussian process (GP) regression is widely
used in such control problems. Online learning, i.e. adding newly collected
training data to the GP models, promises to improve control performance via
improved predictions during the operation. In this paper, we propose a
distributed event-triggered online learning algorithm for multi-agent system
control. The proposed algorithm only employs locally available information from
the neighbors and achieves a guaranteed overall control performance with
desired tracking error bound. Moreover, the exclusion of the Zeno behavior for
each agent is proved. Finally, the effectiveness of the proposed
event-triggered online learning is demonstrated in simulations
Negative emotions boost users activity at BBC Forum
We present an empirical study of user activity in online BBC discussion
forums, measured by the number of posts written by individual debaters and the
average sentiment of these posts. Nearly 2.5 million posts from over 18
thousand users were investigated. Scale free distributions were observed for
activity in individual discussion threads as well as for overall activity. The
number of unique users in a thread normalized by the thread length decays with
thread length, suggesting that thread life is sustained by mutual discussions
rather than by independent comments. Automatic sentiment analysis shows that
most posts contain negative emotions and the most active users in individual
threads express predominantly negative sentiments. It follows that the average
emotion of longer threads is more negative and that threads can be sustained by
negative comments. An agent based computer simulation model has been used to
reproduce several essential characteristics of the analyzed system. The model
stresses the role of discussions between users, especially emotionally laden
quarrels between supporters of opposite opinions, and represents many observed
statistics of the forum.Comment: 29 pages, 6 figure
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Cooperative Set Function Optimization Without Communication or Coordination
We introduce a new model for cooperative agents that seek to optimize a common goal without communication or coordination. Given a universe of elements V, a set of agents, and a set function f, we ask each agent i to select a subset Si â V such that the size of Si is constrained (i.e., |Si| < k). The goal is for the agents to cooperatively choose the sets Si to maximize the function evaluated at the union of these sets, âȘiSi; we seek max f(âȘiSi). We assume the agents can neither communicate nor coordinate how they choose their sets. This model arises naturally in many real-world settings such as swarms of surveillance robots and colonies of foraging insects. Even for simple classes of set functions, there are strong lower bounds on the achievable performance of coordinating deterministic agents. We show, surprisingly, that for the fundamental class of submodular set functions, there exists a near-optimal distributed algorithm for this problem that does not require communication. We demonstrate that our algorithm performs nearly as well as recently published algorithms that allow full coordination
- âŠ