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Using artificial intelligence to enhance complex systems

#artificialintelligence

In any system, you need some kind of input and output, with an action taking place in between. But when that action is particularly complex or requires large amounts of synchronized data, how do you know what input is needed to get the right output? Researchers from the Laboratory of Applied Photonics Devices (LAPD) and Optics Laboratory (LO) at EPFL have found a solution. They've invented an algorithm that can determine what data needs to be fed into a fiber optic network in order to get the desired result at the other end. Their research has just been published in the journal Nature Machine Intelligence.


Using artificial intelligence to enhance complex systems – IAM Network

#artificialintelligence

EPFL researchers have invented a way of automatically working out what data needs to be put into a complex system--such as a fiber optic network--in order to get the desired result. Their solution could prove especially useful in robotics, medicine and image projection. In any system, you need some kind of input and output, with an action taking place in between. But when that action is particularly complex or requires large amounts of synchronized data, how do you know what input is needed to get the right output? Researchers from the Laboratory of Applied Photonics Devices (LAPD) and Optics Laboratory (LO) at EPFL have found a solution.


Using artificial intelligence to enhance complex systems

#artificialintelligence

EPFL researchers have invented a way of automatically working out what data needs to be put into a complex system--such as a fiber optic network--in order to get the desired result. Their solution could prove especially useful in robotics, medicine and image projection. In any system, you need some kind of input and output, with an action taking place in between. But when that action is particularly complex or requires large amounts of synchronized data, how do you know what input is needed to get the right output? Researchers from the Laboratory of Applied Photonics Devices (LAPD) and Optics Laboratory (LO) at EPFL have found a solution.


What Is Adversarial Artificial Intelligence And Why Does It Matter? - Liwaiwai

#artificialintelligence

Artificial intelligence (AI) is quickly becoming a critical component in how government, business and citizens defend themselves against cyber attacks. Starting with technology designed to automate specific manual tasks, and advancing to machine learning using increasingly complex systems to parse data, breakthroughs in deep learning capabilities will become an integral part of the security agenda. Much attention is paid to how these capabilities are helping to build a defence posture. But how enemies might harness AI to drive a new generation of attack vectors, and how the community might respond, is often overlooked. Ultimately, the real danger of AI lies in how it will enable attackers.


Impact of different belief facets on agents' decision -- a refined cognitive architecture

arXiv.org Artificial Intelligence

This paper presents a conceptual refinement of agent cognitive architecture inspired from the beliefs-desires-intentions (BDI) and the theory of planned behaviour (TPB) models, with an emphasis on different belief facets. This enables us to investigate the impact of personality and the way that an agent weights its internal beliefs and social sanctions on an agent's actions. The study also uses the concept of cognitive dissonance associated with the fairness of institutions to investigate the agents' behaviour. To showcase our model, we simulate two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company. The results demonstrate the importance of internal beliefs of agents as a pivotal aspect for following institutional rules.


Intention Propagation for Multi-agent Reinforcement Learning

arXiv.org Machine Learning

Collaborative multi-agent reinforcement learning is an important sub-field of the multiagent reinforcement learning (MARL), where the agents learn to coordinate to achieve joint success. It has wide applications in traffic control [Kuyer et al., 2008], autonomous driving [Shalev-Shwartz et al., 2016] and smart grid [Yang et al., 2018]. To learn a coordination, the interactions between agents are indispensable. For instance, humans can reason about other's behaviors or know other peoples' intentions through communication and then determine an effective coordination plan. However, how to design a mechanism of such interaction in a principled way and at the same time solve the large scale real-world applications is still a challenging problem. Recently, there is a surge of interest in solving the collaborative MARL problem [Foerster et al., 2018, Qu et al., 2019, Lowe et al., 2017]. Among them, joint policy approaches have demonstrated their superiority [Rashid et al., 2018, Sunehag et al., 2018, Oliehoek et al., 2016]. A straightforward approach is to replace the action in the single-agent reinforcement learning by the joint action a (a 1, a 2,..., a N), while it obviously suffers from the issue of the exponentially large action space.


Omdena Building AI Solutions Through Global Collaboration

#artificialintelligence

Omdena runs AI projects with organizations that want to get started with Artificial Intelligence, solve a real-world problem, or build deployable solutions within two months. The projects are powered by our unique Collaborative AI processes, which results in fast development, innovation, and trusted solutions through a bottom-up development process. At first, an organization submits a problem or idea. Next, we publicly announce the AI project and select up to 50 engineers that work with the organization to refine the problem statement, collect the data, and build their solutions.


F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

arXiv.org Artificial Intelligence

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse of dimensionality and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. In this paper, we propose a flexible fully decentralized actor-critic MARL framework, which can combine most of actor-critic methods, and handle large-scale general cooperative multi-agent setting. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, our framework can achieve scalability and stability for large-scale environment and reduce information transmission, by the parameter sharing mechanism and a novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that our decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.


Trump's WHO attack accelerates breakdown in global cooperation

The Japan Times

U.S. President Donald Trump's broadside against the World Health Organization is another blow to international institutions designed to help nations confront global crises -- and may leave countries even less prepared for the next one. Trump's move on Tuesday to suspend WHO funding amid a pandemic that has cost at least 130,000 lives is the latest salvo in a broader struggle between the U.S. and China over global leadership. Both countries are courting other nations and public opinion as they cover up their own shortcomings in the pandemic and position themselves for the post-virus world. China -- widely criticized for missteps early in the outbreak -- has ramped up efforts to send medical supplies to hard-hit nations, even as reports emerged that much of that gear was faulty or expired. The U.S., meanwhile, announced $300 million in aid to countries fighting the virus but rebuffed requests for the most essential gear while receiving donations from the governments of Egypt, Taiwan and Vietnam among others.


Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control

arXiv.org Artificial Intelligence

In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing the \textit{context} in which an agent learns (e.g., a change in flow rates experienced by it), as well as the effects of reducing agent observability of the true environment state. Partial observability may cause distinct states (in which distinct actions are optimal) to be seen as the same by the traffic signal agents. This, in turn, may lead to sub-optimal performance. We show that the lack of suitable sensors to provide a representative observation of the real state seems to affect the performance more drastically than the changes to the underlying traffic patterns.