In this paper, we present a new modality for intelligent inte r-active narratives within the theatre domain. We discuss the possibilities of using an intelligent agent that serves as a drama manager a nd as an actor that plays a character within the live theatre exper ience. We pose a set of research challenges that arise from our analysi s towards the implementation of such an agent, as well as potential method ologies as a starting point to bridge the gaps between current literatu re and the proposed modality.

Noname manuscript No. (will be inserted by the editor)Active Collaboration in Relative Observation for Multi-agent Visual SLAM based on Deep Q Network Zhaoyi Pei · Piaosong Hao · Meixiang Quan · Muhammad Zuhair Qadir · Guo Li Received: date / Accepted: date Abstract This paper proposes a unique active relative localization mechanism for multi-agent Simultaneous Localization and Mapping(SLAM),in which a agent to be observed are considered as a task, which is performed by others assisting that agent by relative observation. A task allocation algorithm based on deep reinforcement learning are proposed for this mechanism. Each agent can choose whether to localize other agents or to continue independent SLAM on it own initiative. By this way, the process of each agent SLAM will be interacted by the collaboration. Firstly, based on the characteristics of ORBSLAM, a unique observation function which models the whole MAS is obtained. Secondly, a novel type of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondence between Q Value and state-action pair, abstract representation of agents in MAS are learned in the process of collaboration amongZhaoyi Pei Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Email: peizhaoyi@stu.hit.edu.cn Songhao Piao Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Email: piaosh@hit.edu.cn Meixiang Quan Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Email: 15b903042@hit.edu.cn

A novel model developed by MIT and Microsoft researchers identifies instances in which autonomous systems have "learned" from training examples that don't match what's actually happening in the real world. Engineers could use this model to improve the safety of artificial intelligence systems, such as driverless vehicles and autonomous robots. The AI systems powering driverless cars, for example, are trained extensively in virtual simulations to prepare the vehicle for nearly every event on the road. But sometimes the car makes an unexpected error in the real world because an event occurs that should, but doesn't, alter the car's behavior. Consider a driverless car that wasn't trained, and more importantly doesn't have the sensors necessary, to differentiate between distinctly different scenarios, such as large, white cars and ambulances with red, flashing lights on the road.

Following sexual assault allegations Antonio Brown has been released from the Patriots. Antonio Brown has been cut loose by two NFL teams this month amid a list of controversies that include sexual assault and rape allegations – but some of the other teams in the league are still expressing interest in his services as a top-flight wide receiver, according to his agent. However, the unnamed teams said to be interested in Brown "want information regarding his legal situation and the NFL investigation" into the accusations made against him, Drew Rosenhaus told ESPN on Saturday. Brown, 31, a seven-time Pro Bowl player, was let go by the New England Patriots on Friday, after a lawyer representing one of his female accusers alerted the NFL about allegedly "intimidating" emails believed to have been sent to the woman by Brown earlier in the week. Not long after Brown was let go, the NFL issued a statement regarding the status of Brown's relationship with the league.

This paper presents an initial design concept and specification of a civilian Unmanned Aerial Vehicle (UAV) management simulation system that focuses on explainability for the human-in-the-loop control of semi-autonomous UAVs. The goal of the system is to facilitate the operator intervention in critical scenarios (e.g. avoid safety issues or financial risks). Explainability is supported via user-friendly abstractions on Belief-Desire-Intention agents. To evaluate the effectiveness of the system, a human-computer interaction study is proposed.

Game Theory, quite unlike its name, is a serious affair to deal with when it comes to the configuration and planning of an AI model. In essence, while linear machine learning deals largely with single-dimensional elements in their very nature, the true power of AI is actually unleashed with game theory application, and it's various facets. To understand game theory power in AI, however, it is essential to understand the basics of what actually constitutes game theory and its applications. So here's the promised primer on what game theory actually comprises. In its textbook definition, "Game Theory is the study of strategic interaction".

We study dual-based algorithms for distributed convex optimization problems over networks, where the objective is to minimize a sum $\sum_{i=1}^{m}f_i(z)$ of functions over in a network. We provide complexity bounds for four different cases, namely: each function $f_i$ is strongly convex and smooth, each function is either strongly convex or smooth, and when it is convex but neither strongly convex nor smooth. Our approach is based on the dual of an appropriately formulated primal problem, which includes a graph that models the communication restrictions. We propose distributed algorithms that achieve the same optimal rates as their centralized counterparts (up to constant and logarithmic factors), with an additional optimal cost related to the spectral properties of the network. Initially, we focus on functions for which we can explicitly minimize its Legendre-Fenchel conjugate, i.e., admissible or dual friendly functions. Then, we study distributed optimization algorithms for non-dual friendly functions, as well as a method to improve the dependency on the parameters of the functions involved. Numerical analysis of the proposed algorithms is also provided.

This paper investigates the evaluation of learned multiagent strategies in the incomplete information setting, which plays a critical role in ranking and training of agents. Traditionally, researchers have relied on Elo ratings for this purpose, with recent works also using methods based on Nash equilibria. Unfortunately, Elo is unable to handle intransitive agent interactions, and other techniques are restricted to zero-sum, two-player settings or are limited by the fact that the Nash equilibrium is intractable to compute. Recently, a ranking method called {\alpha}-Rank, relying on a new graph-based game-theoretic solution concept, was shown to tractably apply to general games. However, evaluations based on Elo or {\alpha}-Rank typically assume noise-free game outcomes, despite the data often being collected from noisy simulations, making this assumption unrealistic in practice. This paper investigates multiagent evaluation in the incomplete information regime, involving general-sum many-player games with noisy outcomes. We derive sample complexity guarantees required to confidently rank agents in this setting. We propose adaptive algorithms for accurate ranking, provide correctness and sample complexity guarantees, then introduce a means of connecting uncertainties in noisy match outcomes to uncertainties in rankings. We evaluate the performance of these approaches in several domains, including Bernoulli games, a soccer meta-game, and Kuhn poker.

In their quest to "ensure that artificial general intelligence benefits all of humanity", researchers at OpenAI discovered that their virtual AI agents engaged in a simulated game of "Hide and Seek" had learned six distinct new strategies for playing the game outside of the designed environment. Through training in our new simulated hide-and-seek environment, agents build a series of six distinct strategies and counterstrategies, some of which we did not know our environment supported. The self-supervised emergent complexity in this simple environment further suggests that multi-agent co-adaptation may one day produce extremely complex and intelligent behavior. This surprising type of learning behavior suggests intrinsic motivation and/or competition with others, although the researchers are still figuring it all out. We've shown that agents can learn sophisticated tool use in a high fidelity physics simulator; however, there were many lessons learned along the way to this result.

In our environment, agents play a team-based hide-and-seek game. Hiders (blue) are tasked with avoiding line-of-sight from the seekers (red), and seekers are tasked with keeping vision of the hiders. There are objects scattered throughout the environment that hiders and seekers can grab and lock in place, as well as randomly generated immovable rooms and walls that agents must learn to navigate. Before the game begins, hiders are given a preparation phase where seekers are immobilized to give hiders a chance to run away or change their environment. There are no explicit incentives for agents to interact with objects in the environment; the only supervision given is through the hide-and-seek objective.