OntoScene exploits ontologies for representing knowledge and Prolog for specifying the interpretation rules that domain experts may adopt, and for implementing the SceneInterpreter engine. Ontologies allow the designer to formalize the domain in a reusable way, and make the system modular and interoperable with existing multiagent systems, while Prolog provides a solid basis to define complex rules of interpretation in a way that can be affordable even for people with no background in Computational Logics. The domain selected for experimenting OntoScene is that of prehistoric rock art, which provides us with a fascinating and challenging testbed. Under consideration in Theory and Practice of Logic Programming (TPLP) KEYWORDS: Prolog; Ontologies; Multiagent Systems; Visual Languages; Scene Interpretation1 Introduction Human perception of complex visual scenes has been studied for a long time in psychology and neuroscience (Kondo et al. 2017): according to the seminal work on "high-level scene perception" (Henderson and Hollingworth 1999), besides low-level or early vision, concerned with extraction of physical properties such as depth, color, and texture from an image (Marr 1982), and intermediate-level vision, concerned with extraction of shape and spatial relations that can be determined without regard to meaning (Ullman 1996), a further level of vision is required to perceive and understand a scene: high-level vision concerns the mapping from visual representations to meaning and includes [...] the identification of objects and scenes. In their recent studies, Kveraga, Bar, and Baldassano (Kveraga and Bar 2014; Baldassano 2015) demonstrate that the brain has regions related to higher-order properties like overall geometry, arXiv:1911.04863v1

The idea of experience sharing between cooperative agents naturally emerges from our understanding of how humans learn. Our evolution as a species is tightly linked to the ability to exchange learned knowledge with one another. It follows that experience sharing (ES) between autonomous and independent agents could become the key to accelerate learning in cooperative multiagent settings. We investigate if randomly selecting experiences to share can increase the performance of deep reinforcement learning agents, and propose three new methods for selecting experiences to accelerate the learning process. Firstly, we introduce Focused ES, which prioritizes unexplored regions of the state space. Secondly, we present Prioritized ES, in which temporal-difference error is used as a measure of priority. Finally, we devise Focused Prioritized ES, which combines both previous approaches. The methods are empirically validated in a control problem. While sharing randomly selected experiences between two Deep Q-Network agents shows no improvement over a single agent baseline, we show that the proposed ES methods can successfully outperform the baseline. In particular, the Focused ES accelerates learning by a factor of 2, reducing by 51% the number of episodes required to complete the task.

The goal of coordinated multi-robot exploration tasks is to employ a team of autonomous robots to explore an unknown environment as quickly as possible. Compared with human-designed methods, which began with heuristic and rule-based approaches, learning-based methods enable individual robots to learn sophisticated and hard-to-design cooperation strategies through deep reinforcement learning technologies. However, in decentralized multi-robot exploration tasks, learning-based algorithms are still far from being universally applicable to the continuous space due to the difficulties associated with area calculation and reward function designing; moreover, existing learning-based methods encounter problems when attempting to balance the historical trajectory issue and target area conflict problem. Furthermore, the scalability of these methods to a large number of agents is poor because of the exponential explosion problem of state space. Accordingly, this paper proposes a novel approach - Multi-head Attention-based Multi-robot Exploration in Continuous Space (MAMECS) - aimed at reducing the state space and automatically learning the cooperation strategies required for decentralized multi-robot exploration tasks in continuous space. Computational geometry knowledge is applied to describe the environment in continuous space and to design an improved reward function to ensure a superior exploration rate. Moreover, the multi-head attention mechanism employed helps to solve the historical trajectory issue in the decentralized multi-robot exploration task, as well as to reduce the quadratic increase of action space.

An autonomous robotic system invented by researchers at MIT and the Woods Hole Oceanographic Institution (WHOI) efficiently sniffs out the most scientifically interesting -- but hard-to-find -- sampling spots in vast, unexplored waters. Environmental scientists are often interested in gathering samples at the most interesting locations, or "maxima," in an environment. One example could be a source of leaking chemicals, where the concentration is the highest and mostly unspoiled by external factors. But a maximum can be any quantifiable value that researchers want to measure, such as water depth or parts of coral reef most exposed to air. Efforts to deploy maximum-seeking robots suffer from efficiency and accuracy issues.

Microsoft today announced the public preview of its Power Virtual Agents tool, a new no-code tool for building chatbots that's part of the company's Power Platform, which also includes Microsoft Flow automation tool, which is being renamed to Power Automate today, and Power BI. Built on top of Azure's existing AI smarts and tools for building bots, Power Virtual Agents promises to make building a chatbot almost as easy as writing a Word document. With this, anybody within an organization could build a bot that walks a new employee through the onboarding experience for example. "Power virtual agent is the newest addition to the Power Platform family," said Microsoft's Charles Lamanna in an interview ahead of today's announcement. "Power Virtual Agent is very much focused on the same type of low code, accessible to anybody, no matter whether they're a business user or business analyst or professional developer, to go build a conversational agent that's AI-driven and can actually solve problems for your employees, for your customers, for your partners, in a very natural way." Power Virtual Agents handles the full lifecycle of the bot building experience, from the creation of the dialog to making it available in chat systems that include Teams, Slack, Facebook Messenger and others.

How do groups of individuals achieve consensus in movement decisions? Do individuals follow their friends, the one predetermined leader, or whomever just happens to be nearby? To address these questions computationally, we formalize Coordination Strategy Inference Problem. In this setting, a group of multiple individuals moves in a coordinated manner towards a target path. Each individual uses a specific strategy to follow others (e.g. nearest neighbors, pre-defined leaders, preferred friends). Given a set of time series that includes coordinated movement and a set of candidate strategies as inputs, we provide the first methodology (to the best of our knowledge) to infer the set of strategies that each individual uses to achieve movement coordination at the group level. We evaluate and demonstrate the performance of the proposed framework by predicting the direction of movement of an individual in a group in both simulated datasets as well as two real-world datasets: a school of fish and a troop of baboons. Moreover, since there is no prior methodology for inferring individual-level strategies, we compare our framework with the state-of-the-art approach for the task of classification of group-level-coordination models. The results show that our approach is highly accurate in inferring the correct strategy in simulated datasets even in complicated mixed strategy settings, which no existing method can infer. In the task of classification of group-level-coordination models, our framework performs better than the state-of-the-art approach in all datasets. Animal data experiments show that fish, as expected, follow their neighbors, while baboons have a preference to follow specific individuals. Our methodology generalizes to arbitrary time series data of real numbers, beyond movement data.

Temporal prediction is critical for making intelligent and robust decisions in complex dynamic environments. Motion prediction needs to model the inherently uncertain future which often contains multiple potential outcomes, due to multi-agent interactions and the latent goals of others. Towards these goals, we introduce a probabilistic framework that efficiently learns latent variables to jointly model the multi-step future motions of agents in a scene. Our framework is data-driven and learns semantically meaningful latent variables to represent the multimodal future, without requiring explicit labels. Using a dynamic attention-based state encoder, we learn to encode the past as well as the future interactions among agents, efficiently scaling to any number of agents. Finally, our model can be used for planning via computing a conditional probability density over the trajectories of other agents given a hypothetical rollout of the 'self' agent. We demonstrate our algorithms by predicting vehicle trajectories of both simulated and real data, demonstrating the state-of-the-art results on several vehicle trajectory datasets.

Thanks to its generality, RL has been widely studied in many areas, such as control theory, game theory, operations research, multi-agent systems, machine learning, artificial intelligence, and statistics [23]. In recent years, combining with deep learning, RL has demonstrated its great potential in addressing challenging practical control and optimization problems [17, 21]. Among all possible algorithms, the temporal difference (TD) learning has arguably become one of the most popular RL algorithms so far, which is further dominated by the celebrated TD(0) algorithm [22]. TD learning provides an iterative process to update an estimate of the so-termed value function v π(s) with respect to a given policy π based on temporally successive samples. Dealing with a finite state space, the classical version of the TD(0) algorithm adopts a tabular representation for v π(s), which stores entry-wise value estimates on a per state basis. J. Sun and Q. Yang are with the College of Control Science and Engineering, and the State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China. G. Wang and G. B. Giannakis are with the Digital Technology Center and the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA. Z. Yang is with the Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China.

Making decisions in the presence of a strategic opponent requires one to take into account the opponent's ability to actively mask its intended objective. To describe such strategic situations, we introduce the non-cooperative inverse reinforcement learning (N-CIRL) formalism. The N-CIRL formalism consists of two agents with completely misaligned objectives, where only one of the agents knows the true objective function. Formally, we model the N-CIRL formalism as a zero-sum Markov game with one-sided incomplete information. Through interacting with the more informed player, the less informed player attempts to both infer, and act according to, the true objective function. As a result of the one-sided incomplete information, the multi-stage game can be decomposed into a sequence of single-stage games expressed by a recursive formula. Solving this recursive formula yields the value of the N-CIRL game and the more informed player's equilibrium strategy. Another recursive formula, constructed by forming an auxiliary game, termed the dual game, yields the less informed player's strategy. Building upon these two recursive formulas, we develop a computationally tractable algorithm to approximately solve for the equilibrium strategies. Finally, we demonstrate the benefits of our N-CIRL formalism over the existing multi-agent IRL formalism via extensive numerical simulation in a novel cyber security setting.

Reinforcement Learning is a popular research area. This is mainly because it aims to model systems that otherwise seem intractable. From the famous Atari paper by Deepmind, we have come far. An interesting avenue of study in reinforcement learning is that of communicating agents: a setup where agents can send messages to each other in order to cooperate. A good case where communication will be essential is that of an environment that is only partially observable to each agent, whereas more information is required for the agents to complete the task cooperatively.