Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.

Machine vision, says Utrip, TUI's chosen partner in AI, natural language processing and machine learning From being one of the first to dabble in blockchain technology to driving forward with AI-fuelled partnerships, the TUI Group is on a mission to remain a travel heavy weight. In a recent move, the world's biggest travel company has signed a deal with Seattle-based AI firm Utrip, to up its game in offering a deeply personalised experience throughout the customer journey. The idea, David Schelp, managing director of TUI Destination Services (DS), said in a press release, is to combine its "inventory of unique destination experiences with Utrip's artificial intelligence solution to provide our guests with the most personalised travel planning experience available today". TUI passed on an opportunity to hop on a call to discuss the deal further, the ever enthusiastic Gilad Berenstein, CEO and co-founder of Utrip, was keen to talk about how far his company, which launched in 2012, has come. "The power of AI today is that we can match up people's preferences with the inventory that has already been curated by TUI or by any of the partners we work with," he says.

The two qualities can also create a competitive advantage, which is key priority for growing organizations. While agents hold the keys to your company's success, they are also the most expensive resource you have – accounting for 60% to 70% of expenses in a contact center. Depending on organizational needs, call centers can implement an array of different technologies, from basic to sophisticated. Monet Software predicts in their study that call centers using workforce management systems generally experience a minimum reduction of 2% for staff hours with an average potential savings in the 5-10% range. With workforce management it is generally expected that at least 25% of the time currently devoted to manual input can be saved.

If scientific journals don't make their articles available to everyone, we want nothing to do with them. That's the gist of a petition signed by more than 2,000 artificial intelligence researchers publicized in a tweet on Saturday. Specifically, the scientists are boycotting a recently-announced journal, Nature Machine Intelligence, because it would trap the articles published there behind a paywall. Artificial intelligence research should be transparent and open to the community at large, argues Tom Dietterich of Oregon State University, the machine learning researcher who began the boycott, according to Retraction Watch. Reminder: science publishing is a business. Many journals, especially the most reputable ones (which include those from the Nature Publishing Group), require payment from anyone who wants to read a full article.

We introduce and empirically evaluate two techniques aimed at enhancing the performance of multi-robot prioritized path planning. The first technique is the deterministic procedure for re-scheduling (as opposed to well-known approach based on random restarts), the second one is the heuristic procedure that modifies the search-space of the individual planner involved in the prioritized path finding.

In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action. We investigate the question of avoiding re-planning in subsequent decision steps by directly using sub-trees as action recommender. Firstly, we propose a method for open loop control via a new algorithm taking the decision of re-planning or not at each time step based on an analysis of the statistics of the sub-tree. Secondly, we show that the probability of selecting a suboptimal action at any depth of the tree can be upper bounded and converges towards zero. Moreover, this upper bound decays in a logarithmic way between subsequent depths. This leads to a distinction between node-wise optimality and state-wise optimality. Finally, we empirically demonstrate that our method achieves a compromise between loss of performance and computational gain.

Springer Nature, the publisher of Scientific American and the venerable scientific journal Nature, intends to stride into the white-hot field of machine learning in early 2019 with a new journal called Nature Machine Intelligence. But the community of machine learning researchers, which prides itself on publishing to open-access journals, was immediately put off by the idea of a closed-access journal that requires academic credentials to read. Thomas Dietterich, the former executive editor of the journal Machine Learning and an emeritus professor of computer science at Oregon State University, posted a pledge not to submit, review or edit for Nature Machine Intelligence, and invited other researchers in the field to sign the pledge as well. At the time of writing, the boycott had accumulated more than 2,400 signatures by employees of Google, Facebook, IBM, Harvard, MIT and a cross-section of other prominent institutions--as well as many of the biggest names in artificial intelligence research including neural network pioneers Yann LeCun and Yoshua Bengio and Google Brain co-founder Jeff Dean. "We write the papers, we copyedit the papers, we typeset the papers, and we review the papers," Dietterich told Motherboard in an email.

NEW YORK, NY - JUNE 16: Director of Facebook AI Research Yann LeCun attends the 2016 Wired Business Conference on June 16, 2016 in New York City. Renowned artificial intelligence (AI) experts from almost all of the tech giants are planning to boycott a new journal from Nature Publishing Group, which is widely regarded as one of the most influential science publishers in the world. Nature's new Machine Intelligence Journal is due to be published for the first time in January 2019. Nature said it will cover the "best research from across the field of artificial intelligence" but it will also be a closed access journal, and this has angered many in the AI community who want to see AI research openly available to everyone. Over 2,000 people -- including more than 75 from Google, 25 from Microsoft, 23 from DeepMind, 16 from Facebook, and 11 from Amazon -- have pledged to "not submit to, review, or edit for this new journal".

We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. We show that a deep neural network can be used to learn and represent a \emph{generalized reactive policy} (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances. In contrast to prior efforts in this direction, our approach significantly reduces the dependence of learning on handcrafted domain knowledge or feature selection. Instead, the GRP is trained from scratch using a set of successful execution traces. We show that our approach can also be used to automatically learn a heuristic function that can be used in directed search algorithms. We evaluate our approach using an extensive suite of experiments on two challenging planning problem domains and show that our approach facilitates learning complex decision making policies and powerful heuristic functions with minimal human input. Videos of our results are available at goo.gl/Hpy4e3.

Artificial intelligence is either the greatest thing to ever happen to human work or the dread of our existence. This independently written report explores how AI will reshape the workplace and how analytically minded individuals can stand out. The report also provides tips on how to address security risks when using one type of AI -- internet-connected smart speakers -- in the office. Please fill out this form to instantly receive the report PDF at the email you enter below. By filling out this form you agree to be contacted by our trusted sponsors.