The diversity of agent behaviors is an important topic for the quality of video games and virtual environments in general. Offering the most compelling experience for users with different skills is a difficult task, and usually needs important manual human effort for tuning existing code. This can get even harder when dealing with adaptive difficulty systems. Our paper's main purpose is to create a framework that can automatically create behaviors for game agents of different difficulty classes and enough diversity. In parallel with this, a second purpose is to create more automated tests for showing defects in the source code or possible logic exploits with less human effort.

Mixed cooperative-competitive control scenarios such as human-machine interaction with individual goals of the interacting partners are very challenging for reinforcement learning agents. In order to contribute towards intuitive human-machine collaboration, we focus on problems in the continuous state and control domain where no explicit communication is considered and the agents do not know the others' goals or control laws but only sense their control inputs retrospectively. Our proposed framework combines a learned partner model based on online data with a reinforcement learning agent that is trained in a simulated environment including the partner model. Thus, we overcome drawbacks of independent learners and, in addition, benefit from a reduced amount of real world data required for reinforcement learning which is vital in the human-machine context. We finally analyze an example that demonstrates the merits of our proposed framework which learns fast due to the simulated environment and adapts to the continuously changing partner due to the partner approximation. Keywords: Reinforcement Learning, Mixed Cooperative-Competitive Control, Opponent Modeling.

Bipartite b -matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas such as healthcare, advertising, education, and genera l resource allocation. Traditionally, the primary goal of su ch models is to maximize a linear function of the constituent matches (e.g., linear social welfare maximization) subjec t to some constraints. Recent work has studied a new goal of balancing whole-match diversity and economic efficiency, where the objective is instead a monotone submodular function ove r the matching. These more general models are largely NPhard. In this work, we develop a combinatorial algorithm tha t constructs provably-optimal diverse b -matchings in pseudo-polynomial time. Then, we show how to extend our algorithm to solve new variations of the diverse b -matching problem. We then compare directly, on real-world datasets, against the state-of-the-art, quadratic-programming-based appr oach to solving diverse b -matching problems and show that our method outperforms it in both speed and (anytime) solution quality.

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. Games are represented as procedural extensive-form games, with some natural extensions. The core API and games are implemented in C and exposed to Python.

CTU in Prague opens a competitive call for applications for fully-funded Ph.D. positions in the context of a recently awarded national center of excellence: Research Center for Informatics (RCI). RCI is the center of scientific excellence in computer science and artificial intelligence that boosts and integrates internationally competitive research conducted at Czech Technical University. The goal of RCI is to foster collaboration between the experts in different fields of computer science, between fundamental scientists and application-driven researchers, but chiefly between the experienced, internationally recognized, senior scientists and graduate students, postdocs and young assistant professors. RCI is focused on longer-term sustainability of excellence computer science and artificial intelligence. Research at RCI is focused on artificial intelligence, multiagent systems, game theory, automated planning, computational robotics with applications to cybersecurity, next-generation transportation systems, intelligent manufacturing, computer vision, machine learning, bioinformatics, computer graphics, embedded security, theoretical computer science or high-performance computing.

F ORCE FIELD TERMINOLOGY Taking into consideration a classical mechanics approach, a force is defined as a vectorial quantity that acts on a body to cause a change in its state of motion [25]. Forces can be classified in action-reaction (when bodies, which are in contact, change their momenta [25]) and action-at-a-distance forces (when objects interact without being physically touched). Considering that social interactions can be often modeled as contact-less, it becomes possible to explain social phenomena in a certain environment by modeling interactions between entities with action-at-a-distance forces. A force field null F is defined as a vector point-function which has the property that at every point of the space takes a particular value related to the magnitude and direction of a force acting on a particle of unit of mass placed there [26]. Accordingly, in this work, the particles of unit of mass affected by force fields will be called agents. A central force field null F f ( r)ˆr is a special case of force field in which the motion of agents is affected depending on the distance r to a center of force, which is generally associated with the center of mass of the object that produces the force field.

RESEARCH Static Force Field Representation of Environments Based on Agents' Nonlinear Motions Damian Campo 1*, Alejandro Betancourt 1,2, Lucio Marcenaro 1 and Carlo Regazzoni 1 Abstract This paper presents a methodology that aims at the incremental representation of areas inside environments in terms of attractive forces. It is proposed a parametric representation of velocity fields ruling the dynamics of moving agents. It is assumed that attractive spots in the environment are responsible for modifying the motion of agents. A switching model is used to describe near and far velocity fields, which in turn are used to learn attractive characteristics of environments. The effect of such areas is considered radial over all the scene. Based on the estimation of attractive areas, a map that describes their effects in terms of their localizations, ranges of action and intensities is derived in an online way . Information of static attractive areas is added dynamically into a set of filters that describes possible interactions between moving agents and an environment. The proposed approach is first evaluated on synthetic data, posteriorly, the method is applied on real trajectories coming from moving pedestrians in an indoor environment. Keywords: Kalman filtering; Interactive force models; T rajectory analysis; Representation of environments; Situation awareness1 Introduction Analysis of trajectories performed by moving entities in environments is an important topic for different fields such as video surveillance [1], crowd/vehicle analysis [2, 3] and in general for monitoring systems, on which the dynamics of agents can lead to a better understanding of patterns and situations of interest [4, 5]. Abnormality detection is one of the most explored applications that involves analysis of trajectories. In such approach, by characterizing agents' motions, it is possible to learn and identify normal/abnormal situations in a certain environment. In general, approaches for abnormality detection are based on a set of observations that define the regular behaviors in a scene. Afterwards, abnormalities are defined as behaviors that do not match with patterns previously learned as normal, i.e., behaviors that have not been observed before [6].

Non-Bayesian social learning theory provides a framework that models distributed inference for a group of agents interacting over a social network. In this framework, each agent iteratively forms and communicates beliefs about an unknown state of the world with their neighbors using a learning rule. Existing approaches assume agents have access to precise statistical models (in the form of likelihoods) for the state of the world. However in many situations, such models must be learned from finite data. We propose a social learning rule that takes into account uncertainty in the statistical models using second-order probabilities. Therefore, beliefs derived from uncertain models are sensitive to the amount of past evidence collected for each hypothesis. We characterize how well the hypotheses can be tested on a social network, as consistent or not with the state of the world. We explicitly show the dependency of the generated beliefs with respect to the amount of prior evidence. Moreover, as the amount of prior evidence goes to infinity, learning occurs and is consistent with traditional social learning theory.

Most existing models of multi-agent reinforcement learning (MARL) adopt centralized training with decentralized execution framework. We demonstrate that the decentralized execution scheme restricts agents' capacity to find a better joint policy in team competition games, where each team of agents share the common rewards and cooperate to compete against other teams. To resolve this problem, we propose Signal Instructed Coordination (SIC), a novel coordination module that can be integrated with most existing models. SIC casts a common signal sampled from a pre-defined distribution to team members, and adopts an information-theoretic regularization to encourage agents to exploit in learning the instruction of centralized signals. Our experiments show that SIC can consistently improve team performance over well-recognized MARL models on matrix games and predator-prey games.

Alex Lightman is the first columnist for ICO Crowd magazine, with 35 articles, an Amazon.com He has authored 14 crypto white papers. He has served as an advisor to 20 Blockchain companies and speaks around the world on "solving big problems with Blockchain, AI and IoT", "CryptoHistory 2009-2050", and "Visionary Blockchain Projects". Lightman was the founder and CEO of Token Communities, and became CTO after the acquisition and name change to Sakthi Global. He leads Kingsland's Executive Education program and the 16 hour two day Blockchain program he authored and teaches has received 100% 5 out of 5 star ratings from participants.