In goal recognition, the basic problem domain consists of the following: Recent advances in robotics and artificial intelligence have brought a variety of assistive robots designed to help humans - a set E of environment fluents; accomplish their goals. However, many have limited autonomy and lack the ability to seamlessly integrate with - a state S that is a value assignment to those fluents; human teams. One capability that can facilitate such humanrobot - a set A of actions that describe potential transitions between teaming is the robot's ability to recognize its teammates' states (with preconditions and effects defined over goals, and react appropriately. This function permits E, and parameterized over a set of environment objects the robot to actively assist the team and avoid performing O); and redundant or counterproductive actions.

Traditional data on influenza vaccination has several limitations: high cost, limited coverage of underrepresented groups, and low sensitivity to emerging public health issues. Social media, such as Twitter, provide an alternative way to understand a population’s vaccination-related opinions and behaviors. In this study, we build and employ several natural language classifiers to examine and analyze behavioral patterns regarding influenza vaccination in Twitter across three dimensions: temporality (by week and month), geography (by US region), and demography (by gender). Our best results are highly correlated official government data, with a correlation over 0.90, providing validation of our approach. We then suggest a number of directions for future work.

Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach's potential to greatly reduce analyst workloads.

As autonomous intelligent systems become more widespread, society is beginning to ask: "What are the machines up to?". Various forms of artificial intelligence control our latest cars, load balance components of our power grids, dictate much of the movement in our stock markets and help doctors diagnose and treat our ailments. As they become increasingly able to learn and model more complex phenomena, so the ability of human users to understand the reasoning behind their decisions often decreases. It becomes very difficult to ensure that the robot will perform properly and that it is possible to correct errors. In this paper, we outline a variety of techniques for generating the underlying knowledge required for explainable artificial intelligence, ranging from early work in expert systems through to systems based on Behavioural Cloning. These are techniques that may be used to build intelligent robots that explain their decisions and justify their actions. We will then illustrate how decision trees are particularly well suited to generating these kinds of explanations. We will also discuss how additional explanations can be obtained, beyond simply the structure of the tree, based on knowledge of how the training data was generated. Finally, we will illustrate these capabilities in the context of a robot learning to drive over rough terrain in both simulation and in reality.

We consider the problem of actively eliciting preferences from a human by a planning system. While prior work in planning have explored the use of domain knowledge and preferences, they assume that the knowledge must be provided before the planner starts the planning process. Our work is in building more collaborative systems where a system can solicit advice as needed. We verify empirically that this approach lead to faster and better solutions, while reducing the burden on the human expert.

Retirees who lost big in the 2008 financial crisis have good reason to worry about President Trump's new rollbacks of Wall Street regulations. In an executive order issued today, Trump sought to undo parts of the 2010 Dodd-Frank act, which curbed some of the finance industry's most egregious recession-spawning practices. In a separate memo, the president ordered a review of the "fiduciary duty rule," which requires retirement fund investors to act in the best interests of their clients. The Wall St bankers may be popping champagne, but Americans haven't forgotten the 2008 financial crisis – and they won't forget today. The opposition may have an unlikely ally in this fight: fintech, the segment of the tech industry trying to automate away so much of what Wall Street does.

This week, more than 2,000 Google employees walked out of work to protest President Trump's immigration ban. Far from disciplining them for leaving their desks, CEO Sundar Pichai and co-founder Sergey Brin treated workers to impassioned speeches of support. "Proud, moved, and touched to be at a company that boldly stands for its people," Googler Sam Tse tweeted. While Pichai and Brin were no doubt speaking from personal conviction--Brin's family fled the former Soviet Union when he was a boy--they also had little choice but to back their employees. Trump's directive cut to the heart of Silicon Valley's treasured values of globalism and openness, values widely embraced by the workers themselves.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Need help preparing a romantic dinner for two? ARMAR will give you a hand.

U.S. Capitol Police are probing the activities of several IT contractors who worked for dozens of House Democrats after they allegedly inappropriately accessed House computers, took congressional computer hardware and made questionable IT-related purchases on behalf of lawmakers. House officials already have revoked the IT and access privileges for the five congressional IT contractors, as police investigate. No arrests have yet been made. Fox News is told the contract employees, shared by about 40 House Democratic members, removed hundreds of thousands of dollars of equipment from offices, including computers and servers. There also were bookkeeping and inventory irregularities where the employees would purchase equipment at one price, bill the congressional office for another and potentially pocket the difference.

Former ExxonMobil Corp. Chief Executive Rex Tillerson was sworn in only Wednesday, and already Congress is moving to benefit the new secretary of state's former--and only--place of work by shredding two major oil industry regulations. Early Friday morning, the Republican-led Senate voted 52 to 47 on a House resolution scrapping a Securities and Exchange Commission rule requiring companies like Exxon and Chevron Corp. to disclose payments they make to foreign governments for the ability to extract oil, minerals and natural gas from their territory. Known as the "extraction rule," it was meant to curb corruption and boost transparency within the oil industry. Standing before the upper house Thursday night, Sen. Elizabeth Warren of Massachusetts railed against the effort to discard the rule. "One of the Republican Party's first orders of business is a giveaway to ExxonMobil that will help corrupt and repressive foreign regimes and make it easy to funnel money to terrorists around the world," she said, adding that companies like Exxon "regularly pay millions" to "corrupt officials" for the rights to drill on their land, and highlighting the "years" necessary to garner bipartisan and even investor support for the law's passage.