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

By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis. However, for agents to intelligently interact with humans, simply capturing the statistical patterns is insufficient. In this paper we investigate if, and how, grounded compositional language can emerge as a means to achieve goals in multi-agent populations. Towards this end, we propose a multi-agent learning environment and learning methods that bring about emergence of a basic compositional language. This language is represented as streams of abstract discrete symbols uttered by agents over time, but nonetheless has a coherent structure that possesses a defined vocabulary and syntax. We also observe emergence of non-verbal communication such as pointing and guiding when language communication is unavailable.

Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize an objective over the long term. This approach to learning has received immense interest in recent times and success manifests itself in the form of human-level performance on games like \textit{Go}. While RL is emerging as a practical component in real-life systems, most successes have been in Single Agent domains. This report will instead specifically focus on challenges that are unique to Multi-Agent Systems interacting in mixed cooperative and competitive environments. The report concludes with advances in the paradigm of training Multi-Agent Systems called \textit{Decentralized Actor, Centralized Critic}, based on an extension of MDPs called \textit{Decentralized Partially Observable MDP}s, which has seen a renewed interest lately.

Cooperation of a large number of possibly small-sized robots, called robotic swarm, will play a significant role in complex missions that existing operational concepts using a few large robots could not deal with [1]. Even if every single robot (or called agent) in a swarm is incapable of accomplishing a task alone, their cooperation will lead to successful outcomes [2]-[5]. The possible applications include environmental monitoring [6], ad-hoc network relay [7], disaster management [8], cooperative radar jamming [9], to name a few. Due to the large cardinality of a swarm robot system, however, it is infeasible for human operators to supervise each agent directly, but needed to entrust the swarm with certain levels of decision-makings (e.g., task allocation, path planning, and individual control). Thereby, what only remains is to provide a high-level mission description, which is manageable for a few or even a single human operator. Nevertheless, there still exist various challenges in the autonomous decisionmaking of robotic swarms. Among them, this paper addresses a task allocation problem where the number of agents is higher than that of tasks: how to partition a set of agents into subgroups and assign the subgroups to each task.

The U.S Department of Homeland Security had funded a research approximately 6 years ago of the virtual border agent technology, better known as AVATAR (Automated Virtual Agent for Truth Assessments in Real Time) and had tested it at the U.S Mexico border on travelers voluntarily. Canada and EU has also tested the robot like kiosk that is asking travelers a series of questions. If the trend continues, International Travelers could be speaking with kiosk to determine if they are lying on any aspect at an airport or border crossings. The technology can also be used to screen the refugees and unwanted travelers travelling to any country. It can also be used to screen the citizenship applications, processing visas and many other such inter-related services.

Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services. Centralized solutions for coordinating multi-robot teams often suffer from poor scalability and vulnerability to communication disruptions. This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications. The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously identify the optimal sequence of tasks it should undertake. The graph weights are determined based on a soft clustering process, which also plays a problem decomposition role seeking to reduce the complexity of the individual-agents' task assignment problems. To evaluate the new Dec-MATA algorithm, a series of case studies (of varying complexity) are performed, with tasks being distributed randomly over an observable 2D environment. A centralized approach, based on a state-of-the-art MILP formulation of the multi-Traveling Salesman problem is used for comparative analysis. While getting within 7-28% of the optimal cost obtained by the centralized algorithm, the Dec-MATA algorithm is found to be 1-3 orders of magnitude faster and minimally sensitive to task-to-robot ratios, unlike the centralized algorithm.

Designing hierarchical reinforcement learning algorithms that induce a notion of safety is not only vital for safety-critical applications, but also, brings better understanding of an artificially intelligent agent's decisions. While learning end-to-end options automatically has been fully realized recently, we propose a solution to learning safe options. We introduce the idea of controllability of states based on the temporal difference errors in the option-critic framework. We then derive the policy-gradient theorem with controllability and propose a novel framework called safe option-critic. We demonstrate the effectiveness of our approach in the four-rooms grid-world, cartpole, and three games in the Arcade Learning Environment (ALE): MsPacman, Amidar and Q*Bert. Learning of end-to-end options with the proposed notion of safety achieves reduction in the variance of return and boosts the performance in environments with intrinsic variability in the reward structure. More importantly, the proposed algorithm outperforms the vanilla options in all the environments and primitive actions in two out of three ALE games.

Active systems can produce a far greater variety of ordered patterns than conventional equilibrium systems. In particular, transitions between disorder and either polar- or nematically ordered phases have been predicted and observed in two-dimensional active systems. However, coexistence between phases of different types of order has not been reported. We demonstrate the emergence of dynamic coexistence of ordered states with fluctuating nematic and polar symmetry in an actomyosin motility assay. Combining experiments with agent-based simulations, we identify sufficiently weak interactions that lack a clear alignment symmetry as a prerequisite for coexistence. Thus, the symmetry of macroscopic order becomes an emergent and dynamic property of the active system.

I talked to Dirk Erlacher, the CEO of Agilox, on this topic. Austrian headquartered Agilox designs and manufactures mobile logistics robots that use "swarm intelligence" to intelligently navigate through warehouses and factories, delivering pallets and totes where they are needed. A mobile logistics robot (MLR) is a more advanced form of an automatic guided vehicle (AGV); AGVs are used to reduce labor by taking over tasks that were traditionally performed with fork lifts. More complex AGVs have fleet management software. This software makes sure that not too many AGVs are in the same aisles, decides which AGV has the right of way at crossings, and in more complex scenarios, decides which unit will be used to complete a particular task and how it will navigate through the facility.

Most artificial intelligence(AI) systems nowadays are based on a single agent tackling a task or, in the case of adversarial models, a couple of agents that compete against each other to improve the overall behavior of a system. However, many cognition problems in the real world are the result of knowledge built by large groups of people. Take for example a self-driving car scenario, the decisions of any agent are the result of the behavior of many other agents in the scenario. Many scenarios in financial markets or economics are also the result of coordinated actions between large groups of entities. How can we mimic that behavior in artificial intelligence(AI) agents?