Germany Market Analysis Table 35: German Recent Past, Current & Future Analysis for Artificial Intelligence Analyzed with Annual Revenue Figures in US$ Million for Years 2015 through 2024 (includes corresponding Graph/Chart) 9.4.3 Italy Market Analysis Table 36: Italian Recent Past, Current & Future Analysis for Artificial Intelligence Analyzed with Annual Revenue Figures in US$ Million for Years 2015 through 2024 (includes corresponding Graph/Chart) 9.4.4

Discover how all levels Artificial Intelligence (AI) can be present in the most unimaginable scenarios of ordinary lives. This book explores subjects such as neural networks, agents, multi agent systems, supervised learning, and unsupervised learning. These and other topics will be addressed with real world examples, so you can learn fundamental concepts with AI solutions and apply them to your own projects. People tend to talk about AI as something mystical and unrelated to their ordinary life. Practical Artificial Intelligence provides simple explanations and hands on instructions.

Human social behavior is structured by relationships. We form teams, groups, tribes, and alliances at all scales of human life. These structures guide multi-agent cooperation and competition, but when we observe others these underlying relationships are typically unobservable and hence must be inferred. Humans make these inferences intuitively and flexibly, often making rapid generalizations about the latent relationships that underlie behavior from just sparse and noisy observations. Rapid and accurate inferences are important for determining who to cooperate with, who to compete with, and how to cooperate in order to compete. Towards the goal of building machine-learning algorithms with human-like social intelligence, we develop a generative model of multi-agent action understanding based on a novel representation for these latent relationships called Composable Team Hierarchies (CTH). This representation is grounded in the formalism of stochastic games and multi-agent reinforcement learning. We use CTH as a target for Bayesian inference yielding a new algorithm for understanding behavior in groups that can both infer hidden relationships as well as predict future actions for multiple agents interacting together. Our algorithm rapidly recovers an underlying causal model of how agents relate in spatial stochastic games from just a few observations. The patterns of inference made by this algorithm closely correspond with human judgments and the algorithm makes the same rapid generalizations that people do.

MAPF is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF were on grid, assumed all actions cost the same, agents do not have a volume, and considered discrete time steps. In this work we propose a MAPF algorithm that do not assume any of these assumptions, is complete, and provides provably optimal solutions. This algorithm is based on a novel combination of SIPP, a continuous time single agent planning algorithms, and CBS, a state of the art multi-agent pathfinding algorithm. We analyze this algorithm, discuss its pros and cons, and evaluate it experimentally on several standard benchmarks.

Modern Technology Solutions Inc. has opened a laboratory in Huntsville, Ala., for research and development of artificial intelligence-based technology platforms for the military sector. MTSI said Friday it looks to accomplish a holistic approach to AI application through the new lab along with the company's engineering and data analytics processes. Willie Maddox, manager of AI Lab, said the company aims to apply deep reinforcement learning to address challenges related to multiagent dynamic route planning. Alexandria, Va.-based MTSI offers engineering and technology services to government customers in the missile defense, cybersecurity, intelligence, unmanned and autonomous systems, aviation, space and homeland security areas.

AlphaICs has released alphaEdge, powered by AlphaICs' proprietary Real AI Processor (RAP) inference chip. It is a 60 TOPs/40-Watt solution for L2 driverless cars and autonomous systems including drones, robots and the like. For more information, please visit www.alphaics.ai.

We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4 C root mean squared error (RMSE) in a temperature range of 24– 90 C, employing gradient boosting with decision trees. The RMSE is 3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80 C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.

Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with a task requiring coordination and synchronisation skills, inter-agent communication plays an essential role. In this work, we propose a framework for multi-agent training using deep deterministic policy gradients that enables the concurrent, end-to-end learning of an explicit communication protocol through a memory device. During training, the agents learn to perform read and write operations enabling them to infer a shared representation of the world. We empirically demonstrate that concurrent learning of the communication device and individual policies can improve inter-agent coordination and performance, and illustrate how different communication patterns can emerge for different tasks.

The company collaborated with the University of Electro-Communications in Japan for the research, which they claim can rapidly generate a solution system for sealing off escape routes with police dispersal. It takes approximately five minutes. "Fujitsu Laboratories and the University of Electro-Communications have developed an algorithm to rapidly solve city-scale road network security problems. Compared with previous technology, this makes it possible to find the theoretically optimal security plan 20 times faster, on average, for a 100-node problem, and 500 times faster, on average, for a 200-node problem," Fujitsu says. The company plans to scale up by commercializing the technology through their Fujitsu Limited AI arm, Human Centric Al Zinrai (Zinrai), next year.