Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise, and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.

Increasing energy efficiency in buildings can reduce costs and emissions substantially. Historically, this has been treated as a local, or single-agent, optimization problem. However, many buildings utilize the same types of thermal equipment e.g. electric heaters and hot water vessels. During operation, occupants in these buildings interact with the equipment differently thereby driving them to diverse regions in the state-space. Reinforcement learning agents can learn from these interactions, recorded as sensor data, to optimize the overall energy efficiency. However, if these agents operate individually at a household level, they can not exploit the replicated structure in the problem. In this paper, we demonstrate that this problem can indeed benefit from multi-agent collaboration by making use of targeted exploration of the state-space allowing for better generalization. We also investigate trade-offs between integrating human knowledge and additional sensors. Results show that savings of over 40% are possible with collaborative multi-agent systems making use of either expert knowledge or additional sensors with no loss of occupant comfort. We find that such multi-agent systems comfortably outperform comparable single agent systems.

We propose practical extensions to Bayesian optimization for solving dynamic problems. We model dynamic objective functions using spatiotemporal Gaussian process priors which capture all the instances of the functions over time. Our extensions to Bayesian optimization use the information learnt from this model to guide the tracking of a temporally evolving minimum. By exploiting temporal correlations, the proposed method also determines when to make evaluations, how fast to make those evaluations, and it induces an appropriate budget of steps based on the available information. Lastly, we evaluate our technique on synthetic and real-world problems.

Optimal Transport distances (also known as earth mover's distances or Wasserstein distances) design an optimal plan to move "mass" from one probability distribution to another. This problem can be traced back to the early work of Monge [1] and Kantorovich [2] and has been of constant interest for allowing natural formulations to the problems of comparing, interpolating, and measuring distances of functions [3]. On the other hand, computational optimal transport has gain popularity for its applications in learning theory [4], computer vision [5], computer graphics [6], statistical inference [7], information fusion [8]; and its relative complexity advantages with respect to classical methods [9]. Particularly, large-scale optimal transport has been of recent interest for the latest applications where large quantities of data are available and efficient algorithms are required [10, 11, 12]. Comprehensive accounts of the optimal transport problem and its computational aspects can be found in [13, 14, 15, 3]. One of the common uses of the Wasserstein distance is the aggregation of distributions by considering their barycenter [16], which itself is another distribution [17]. Wasserstein Barycenters has been shown superior to traditional Euclidean-based methods in a range of application such as image processing [16], economics and finance [18] and condensed matter physics [19]. Figure 1 shows a sample of 100 images of the digit 7 from the MNIST dataset [20] and their respective Euclidean mean and Wasserstein mean.

Networked control systems (NCSs) are rapidly gaining in popularity, both in academia and industry. Advancements in control strategies and network technologies enable the systems to closely interact with their environment and share data. Treating communication as a shared resource, as suggested in [1], is an important step to scale NCSs to problems involving many agents. In this paper, we consider NCSs with multiple spatially distributed agents, whose dynamics are independent, but that are coupled through a joint control objective and communicate via a shared network. Figure 1 depicts two agents representative for one communication link in such an NCS. While communication between agents is beneficial or even necessary for coordination (e.g., formation control [2], or multi-agent balancing [3]), the network constitutes a shared and scarce resource and, hence, its usage shall be limited. Event-triggered state estimation (ETSE) [4]-[8] has been proposed to reliably exchange sensor or state data between agents, but with limited inter-agent communication. Many ETSE methods utilize dynamics models to predict other agents' states or measurements (see Figure 1), in order to anticipate their behavior without the need for continuous data transmissions.

Abstract: "I will present distributed decision-making dynamics for multi-agent systems, motivated by studies of animal groups, such as house-hunting honeybees, and their extraordinary ability to make collective decisions that are both robust to disturbance and adaptable to change. The dynamics derive from principles of symmetry, consensus, and bifurcation in networked systems, exploiting instability as a means to flexibly transition from one stable solution to another. Feedback dynamics are derived for the bifurcation control, a variable representing social effort, such that flexible transition is made a controlled adaptive response."

Sophia, a life-like humanoid robot, is pictured at the UN headquarters in New York, Oct. 11, 2017. Sophia was here attending a meeting on "The Future of Everything - Sustainable Development in the Age of Rapid Technological Change". WASHINGTON, March 1 (Xinhua) -- China is gaining momentum in the artificial intelligence (AI) industry, which has been translating its huge market size into commercialized innovations. This is a boon instead of a threat. The cry-wolf alarms that America is losing a race for supremacy in the AI industry by comparing China's catching-up to America's Sputnik panic in the late 1950s, have, in a sense, misinterpreted or misrepresented the true AI story. A typical misinterpretation goes to the "global tech cold war," which was put forward by Eurasia Group, a New York-headquartered think tank, arguing that the winner in AI and super-computing between the United States and China will dominate the coming decades, both economically and geopolitically.

AI research and video games are a match made in heaven. Researchers get a ready-made virtual environment with predefined goals they can control completely, and the AI agent gets to romp around without doing any damage. Sometimes, though, they do break things. Case in point is a paper published this week by a trio of machine learning researchers from the University of Freiburg in Germany. They were exploring a particular method of teaching AI agents to navigate video games (in this case, desktop ports of old Atari titles from the 1980s) when they discovered something odd.

Social dilemmas, where mutual cooperation can lead to high payoffs but participants face incentives to cheat, are ubiquitous in multi-agent interaction. We wish to construct agents that cooperate with pure cooperators, avoid exploitation by pure defectors, and incentivize cooperation from the rest. However, often the actions taken by a partner are (partially) unobserved or the consequences of individual actions are hard to predict. We show that in a large class of games good strategies can be constructed by conditioning one's behavior solely on outcomes (ie. one's past rewards). We call this consequentialist conditional cooperation. We show how to construct such strategies using deep reinforcement learning techniques and demonstrate, both analytically and experimentally, that they are effective in social dilemmas beyond simple matrix games. We also show the limitations of relying purely on consequences and discuss the need for understanding both the consequences of and the intentions behind an action.

Holly Bonner, of Staten Island, says she lost most of her eyesight five years ago and it has made annual holiday egg hunts difficult to do with her children. WCBS-TV reports she heard of a "beeping egg hunt" in Alabama, and asked her regional ATF office for help in bringing the concept to Staten Island.