In timeline-based planning, domains are described as sets of independent, but interacting, components, whose behaviour over time (the set of timelines) is governed by a set of temporal constraints. A distinguishing feature of timeline-based planning systems is the ability to integrate planning with execution by synthesising control strategies for flexible plans. However, flexible plans can only represent temporal uncertainty, while more complex forms of nondeterminism are needed to deal with a wider range of realistic problems. In this paper, we propose a novel game-theoretic approach to timeline-based planning problems, generalising the state of the art while uniformly handling temporal uncertainty and nondeterminism. We define a general concept of timeline-based game and we show that the notion of winning strategy for these games is strictly more general than that of control strategy for dynamically controllable flexible plans. Moreover, we show that the problem of establishing the existence of such winning strategies is decidable using a doubly exponential amount of space.

Manufacturing is transitioning from a mass production model to a manufacturing as a service model in which manufacturing facilities'bid' to produce products. To decide whether to bid for a complex, previously unseen product, a manufacturing facility must be able to synthesize, 'on the fly', a process plan controller that delegates abstract manufacturing tasks in the supplied process recipe to the appropriate manufacturing resources, e.g., CNC machines, robots etc. Previous work in applying AI behaviour composition to synthesize process plan controllers has considered only finite state ad-hoc representations. Here, we study the problem in the relational setting of the Situation Calculus. By taking advantage of recent work on abstraction in the Situation Calculus, process recipes and available resources are represented by Con-Golog programs over, respectively, an abstract and a concrete action theory. This allows us to capture the problem in a formal, general framework, and show decidability for the case of bounded action theories. We also provide techniques for actually synthesizing the controller.

Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization of stream learning research, it provides multiple state of the art methods for stream learning, stream generators and evaluators. scikit-multiflow builds upon popular open source frameworks including scikit-learn, MOA and MEKA. Development follows the FOSS principles and quality is enforced by complying with PEP8 guidelines and using continuous integration and automatic testing. The source code is publicly available at https://github.com/scikit-multiflow/scikit-multiflow.

Scarcity of labeled data is one of the most frequent problems faced in machine learning. This is particularly true in relation extraction in text mining, where large corpora of texts exists in many application domains, while labeling of text data requires an expert to invest much time to read the documents. Overall, state-of-the art models, like the convolutional neural network used in this paper, achieve great results when trained on large enough amounts of labeled data. However, from a practical point of view the question arises whether this is the most efficient approach when one takes the manual effort of the expert into account. In this paper, we report on an alternative approach where we first construct a relation extraction model using distant supervision, and only later make use of a domain expert to refine the results. Distant supervision provides a mean of labeling data given known relations in a knowledge base, but it suffers from noisy labeling. We introduce an active learning based extension, that allows our neural network to incorporate expert feedback and report on first results on a complex data set.

In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential. Although classic regularizers provide sparsity, they fail to return highly accurate models. On the contrary, state-of-the-art group-lasso regularizers provide better results at the expense of low sparsity. In this paper, we apply a greedy variable selection algorithm, called Orthogonal Matching Pursuit, for the text classification task. We also extend standard group OMP by introducing overlapping group OMP to handle overlapping groups of features. Empirical analysis verifies that both OMP and overlapping GOMP constitute powerful regularizers, able to produce effective and super-sparse models. Code and data are available here.

We study the problem of estimating heterogeneous treatment effects from observational data, where the treatment policy on the collected data was determined by potentially many confounding observable variables. We propose orthogonal random forest, an algorithm that combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests [Athey et al., 2017], a flexible method for estimating treatment effect heterogeneity. We prove a consistency rate result of our estimator in the partially linear regression model, and en route we provide a consistency analysis for a general framework of performing generalized method of moments (GMM) estimation. We also provide a comprehensive empirical evaluation of our algorithms, and show that they consistently outperform baseline approaches.

As you might expect, this year, many companies use artificial intelligence (AI) and machine learning at the core of their business to deliver innovative products and service offerings. Anyone interested in AI should know about these 14 pioneering businesses.# This London-based company was founded in 2013 and operates under two business units: BenevolentTech's focus is to develop the artificial intelligence platform that will drive innovation by transforming the way scientists access and use the information available to them. BenevolentBio is the division that applies the tech to generate new ideas that will impact human health such as better medicines and research, insights and innovation for rare diseases. With a mission to make law free and understandable, Casetext leverages artificial intelligence technology to help legal researchers find the most relevant cases quickly.

The logo of the software and computer services company IBM is displayed during the Viva Technology show at Parc des Expositions Porte de Versailles on May 25, 2018 in Paris, France. IBM missed a couple of high tech rallies in recent years. Big Blue's shares underperformed badly the technology sector. Over the last five years, IBM shares have lost 25.85% of their value as the Invesco QQQ Trust (QQQ) shares have gained 113.23%--see table 1. The company's business initiatives have failed to produce sufficient sales to offset the decline in sales in the old businesses. That has many investors writing off IBM, including legendary investor Warren Buffett.

For two weeks every summer, the centre of the tennis world is in the London suburb of Wimbledon. Millions of tennis fans follow the action. They want scores, they want player and tournament information. And they want the highlights. With an average of three matches per day on each of the six main show courts, hundreds of hours of video can quickly mount up.

Last month, Amazon Alexa AI science leader Young-Bum Kim wrote a blog post about a paper he's presenting at the annual conference of the Association for Computational Linguistics (ACL), which describes the first part of the two-part system that will enable Alexa to select the one skill out of thousands that's best suited to a particular customer request. He described the second part in a paper presented at a meeting of the North American chapter of the ACL, which was under way at the time. ACL begins in Melbourne, Australia on July 15, and in addition to Kim, two other Amazon AI researchers are coauthors of papers accepted to the conference. Anima Anandkumar, a principal scientist in Amazon's Rekognition and Video group, and colleagues at Cornell University will present a new technique for producing word embeddings, which attempt to mathematically capture words' semantic similarities. And Alessandro Moschitti, a principal scientist in Amazon's Machine Translation/Natural Language Processing group, is coauthor on a pair of papers that use innovative machine-learning systems for classifying discussion-forum threads as a springboard for addressing some more general problems.