This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the-art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.

Abstract: "Teams of robots often have to assign target locations among themselves and then plan collision-free paths to their target locations. Examples include autonomous aircraft towing vehicles and automated warehouse systems. Today, hundreds of robots already navigate autonomously in Amazon fulfillment centers to move inventory pods all the way from their storage locations to the packing stations. Path planning for these robots can be NP-hard, yet one must find high-quality collision-free paths for them in real-time. The shorter these paths are, the fewer robots are needed and the cheaper it is to open new fulfillment centers.

Commonsense reasoning is in principle a central problem in artificial intelligence, but it is a very difficult one. One approach that has been pursued since the earliest days of the field has been to encode commonsense knowledge as statements in a logic-based representation language and to implement commonsense reasoning as some form of logical inference. This paper surveys the use of logic-based representations of commonsense knowledge in artificial intelligence research.

Financial Gravity, a tax services and wealth management firm in Dallas, is sponsoring an AI design challenge for the creation of an an artificially intelligent tax advisor. The AI-enabled automated tax planning assistant software, as it's being called, will be named Odele; and its target end users are business owners, entrepreneurs and high net worth families with multiple sources of income, some investments, and the ability to save for the future. Financial Gravity reports that it has a database of ideal tax scenarios that maximize take-home income. The firm wants Odele to connect individuals to their most ideal scenario.

Anamind helps organizations build business planning and forecasting capability. With simplicity at the core of our approach we offer a world class planning system - PLANAMIND, process consulting services, and training for business planning. This course has been specifically designed by us to help planning professionals as well as aspirants of this function worldwide, to understand and build their skills in business planning. The course contains both quantitative and qualitative aspects of planning. This course is business oriented and not purely academic in nature.

This year's survey aims to define the current market trends around the concept of "work," including the impact of the Gig Economy, the extent to which specific technologies pave the road for the Future of Work, and the performance, strategies, and capabilities within today's contingent workforce management (CWM) programs. For the purposes of this research study, "non-employee" talent includes temporary workers (sourced via staffing suppliers), freelancers, independent contractors, robotics, professional services, and "gig" workers.

From medicines to materials, small organic molecules are indispensable for human well-being. To plan their syntheses, chemists employ a problem solving technique called retrosynthesis. In retrosynthesis, target molecules are recursively transformed into increasingly simpler precursor compounds until a set of readily available starting materials is obtained. Computer-aided retrosynthesis would be a highly valuable tool, however, past approaches were slow and provided results of unsatisfactory quality. Here, we employ Monte Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS was combined with an expansion policy network that guides the search, and an "in-scope" filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on 12 million reactions, which represents essentially all reactions ever published in organic chemistry. Our system solves almost twice as many molecules and is 30 times faster in comparison to the traditional search method based on extracted rules and hand-coded heuristics. Finally after a 60 year history of computer-aided synthesis planning, chemists can no longer distinguish between routes generated by a computer system and real routes taken from the scientific literature. We anticipate that our method will accelerate drug and materials discovery by assisting chemists to plan better syntheses faster, and by enabling fully automated robot synthesis.

Figure 1 depicts a flow diagram of a process for including parallelism when generating a virtual machine migration plan according to an embodiment. Exemplary embodiments relate to using machine learning for virtual machine (VM) migration plan generation. Embodiments can enforce both a colocation and an anti-colocation policy using colocation and anti-colocation contracts. A VM migration plan can be created by processing a first mapping of VMs to hosts along with a second mapping of VMs to hosts. Pre-processing can be performed followed by machine search techniques with heuristics and pruning mechanisms to generate serialized optimal paths from the first state (i.e., an origin state) to a second state (i.e., a goal state).

In this paper we present UbuntuWorld 1.0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system. Specifically, we propose to use the Bash terminal as a simulator of the Ubuntu environment for a learning-based agent, and demonstrate the usefulness of adopting reinforcement learning (RL) techniques for basic problem solving and troubleshooting in this environment. We provide a plug-and-play interface to the simulator as a python package where different types of agents can be plugged in and evaluated, and provide pathways for integrating data from online support forums like Ask Ubuntu into an automated agent's learning process. Finally, we show that the use of this data significantly improves the agent's learning efficiency. We believe that this platform can be adopted as a real-world test bed for research on automated technical support.

Google has released an update that will allow G Suite users to access coworkers' real-time free/busy information through both Google Calendar's Find a Time feature and Microsoft Outlook's Scheduling Assistant interchangeably. G Suite admins can enable the new Calendar Interop management feature through the Settings for Calendar option in the admin console. Admins will also be able to easily pinpoint issues with the setup via a troubleshooting tool, which will also provide suggestions for resolving those issues, and can track interoperability successes and failures for each user through logs Google has made available. The new feature is available on Android, iOS and web versions of Google Calendar as well as desktop, mobile and web clients for Outlook 2010, for admins who choose to enable it. Google says the full rollout should be completed within three days.