We describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projectiles. Levels are represented using a domain-specific description language, and search in the space defined by this language is performed by a novel variant of the Map-Elites algorithm which incorporates a feasible- infeasible approach to constraint satisfaction. Simulation-based evaluation is used to gauge the fitness of levels, using an agent based on best-first search. The performance of the agent can be tuned according to the two dimensions of strategy and dexterity, making it possible to search for level configurations that require a specific combination of both. As far as we know, this paper describes the first generator for this game genre, and includes several algorithmic innovations.

Accidents and attacks that involve chemical, biological, radiological/nuclear or explosive (CBRNE) substances are rare, but can be of high consequence. Since the investigation of such events is not anybody's routine work, a range of AI techniques can reduce investigators' cognitive load and support decision-making, including: planning the assessment of the scene; ongoing evaluation and updating of risks; control of autonomous vehicles for collecting images and sensor data; reviewing images/videos for items of interest; identification of anomalies; and retrieval of relevant documentation. Because of the rare and high-risk nature of these events, realistic simulations can support the development and evaluation of AI-based tools. We have developed realistic models of CBRNE scenarios and implemented an initial set of tools.

Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents. In this paper, we introduce an approach that integrates human strategies to increase the exploration capacity of multiple deep reinforcement learning agents. We also report the development of our own multi-agent environment called Multiple Tank Defence to simulate the proposed approach. The results show the significant performance improvement of multiple agents that have learned cooperatively with human strategies. This implies that there is a critical need for human intellect teamed with machines to solve complex problems. In addition, the success of this simulation indicates that our developed multi-agent environment can be used as a testbed platform to develop and validate other multi-agent control algorithms. Details of the environment implementation can be referred to http://www.deakin.edu.au/~thanhthi/madrl_human.htm

Rosenstein said during a speech in Montreal the United States is "enhancing its commitment to international law enforcement coordination," through personal relationships, policy changes and additional resources, citing examples of recent collaboration between Canadian and U.S. law enforcement.

KUALA LUMPUR, 1 JUNE 2018 – Celcom Axiata Berhad, in its ongoing journey to create awesome moments and experiences for its customers, today announced its latest channel to serve customers -- a state-of-the-art Intelligent Virtual Agent service. Celcom's Intelligent Virtual Agent service brings together cutting-edge Artificial Intelligence (AI) and Machine Learning technology, giving birth to two personas – Clive and Emma – with their own personalities that will interact with customers 24 7 with regard to their inquiries and transactions. The combination of technology, transaction capability and personality is the first of its kind in Asia. Both Clive and Emma are powered with Microsoft's AI & machine learning technology and will have the opportunity to initiate conversations with consumers with a personal and humanised touch, providing an awesome customer experience anywhere and at any time. Microsoft's Machine Learning feature will allow Clive and Emma to auto-learn questions variations via a knowledge-based system that improves their effectiveness over time.

Our goal is for AI systems to correctly identify and act according to their human user's objectives. Cooperative Inverse Reinforcement Learning (CIRL) formalizes this value alignment problem as a two-player game between a human and robot, in which only the human knows the parameters of the reward function: the robot needs to learn them as the interaction unfolds. Previous work showed that CIRL can be solved as a POMDP, but with an action space size exponential in the size of the reward parameter space. In this work, we exploit a specific property of CIRL---the human is a full information agent---to derive an optimality-preserving modification to the standard Bellman update; this reduces the complexity of the problem by an exponential factor and allows us to relax CIRL's assumption of human rationality. We apply this update to a variety of POMDP solvers and find that it enables us to scale CIRL to non-trivial problems, with larger reward parameter spaces, and larger action spaces for both robot and human. In solutions to these larger problems, the human exhibits pedagogic (teaching) behavior, while the robot interprets it as such and attains higher value for the human.

We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.

Fair division is the problem of dividing one or several goods amongst two or more agents in a way that satisfies a suitable fairness criterion. That is, fair division may be considered part of the larger research area of multiagent resource allocation (Chevaleyre et al., 2006). What is special about fair division is the explicit focus on fairness concerns. These notes give a succinct introduction to the field, focusing on formal and computational aspects that are particularly relevant to research in Computational Social Choice (Chevaleyre et al., 2007b) and Multiagent Systems (Wooldridge, 2009). We begin by briefly outlining how fair division fits into (and relates to) these two disciplines. Like voting, the archetypical instance of a social choice problem, fair division amounts to selecting an outcome from a set of possible collective agreements, given the individual preferences of a group of agents. There are however two main differences when compared to voting. The first difference is that, typically, voting theory assumes that agents (voters) have ordinal preferences (that is, they rank the available candidates and can say for any two candidates which one they like more), while in the context of fair division we usually assume that agents have cardinal preferences (that is, each agent has got a utility function mapping possible outcomes to appropriate numerical values). The second difference is that a fair division problem comes with a certain internal "structure" that is typically absent from problems in voting:

In the future, artificial learning agents are likely to become increasingly widespread in our society. They will interact with both other learning agents and humans in a variety of complex settings including social dilemmas. We consider the problem of how an external agent can promote cooperation between artificial learners by distributing additional rewards and punishments based on observing the learners' actions. We propose a rule for automatically learning how to create right incentives by considering the players' anticipated parameter updates. Using this learning rule leads to cooperation with high social welfare in matrix games in which the agents would otherwise learn to defect with high probability. We show that the resulting cooperative outcome is stable in certain games even if the planning agent is turned off after a given number of episodes, while other games require ongoing intervention to maintain mutual cooperation. However, even in the latter case, the amount of necessary additional incentives decreases over time.

Monday, the 29th, marked the beginning of the EU Green Week, an event organized by the European Commission's Directorate-General for Environment to discuss environmental policies. This year, the focus is "Green jobs for a greener future." The organizers stressed how traditional specializations will be characterized by additional sets of new skills. Being able to deal with technology is certainly one of them, and many jobs in the environmental sciences are already adopting these innovative tools. People working in this sector are no longer restricted to field work and laboratory analyses.