Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. To trust their behavior, we must make AI intelligible, either by using inherently interpretable models or by developing new methods for explaining and controlling otherwise overwhelmingly complex decisions using local approximation, vocabulary alignment, and interactive explanation. This paper argues that intelligibility is essential, surveys recent work on building such systems, and highlights key directions for research.

Under dynamic conditions on bridges, we need a real-time management. To this end, this paper presents a rule-based decision support system in which the necessary rules are extracted from simulation results made by Aimsun traffic micro-simulation software. Then, these rules are generalized by the aid of fuzzy rule generation algorithms. Then, they are trained by a set of supervised and the unsupervised learning algorithms to get an ability to make decision in real cases. As a pilot case study, Nasr Bridge in Tehran is simulated in Aimsun and WEKA data mining software is used to execute the learning algorithms. Based on this experiment, the accuracy of the supervised algorithms to generalize the rules is greater than 80%. In addition, CART decision tree and sequential minimal optimization (SMO) provides 100% accuracy for normal data and these algorithms are so reliable for crisis management on bridge. This means that, it is possible to use such machine learning methods to manage bridges in the real-time conditions.

Interpretability of the underlying AI representations is a key raison d'\^{e}tre for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring Systems (ITS) research. OLMs provide tools for 'opening' up the AI models of learners' cognition and emotions for the purpose of supporting human learning and teaching. Over thirty years of research in ITS (also known as AI in Education) produced important work, which informs about how AI can be used in Education to best effects and, through the OLM research, what are the necessary considerations to make it interpretable and explainable for the benefit of learning. We argue that this work can provide a valuable starting point for a framework of interpretable AI, and as such is of relevance to the application of both knowledge-based and machine learning systems in other high-stakes contexts, beyond education.

Exploiting the appropriate inductive bias based on the knowledge of data is essential for achieving good performance in statistical machine learning. In practice, however, the domain knowledge of interest often provides information on the relationship of data attributes only distantly, which hinders direct utilization of such domain knowledge in popular regularization methods. In this paper, we propose the knowledge-based distant regularization framework, in which we utilize the distant information encoded in a knowledge graph for regularization of probabilistic model estimation. In particular, we propose to impose prior distributions on model parameters specified by knowledge graph embeddings. As an instance of the proposed framework, we present the factor analysis model with the knowledge-based distant regularization. We show the results of preliminary experiments on the improvement of the generalization capability of such model.

We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.

Are we really committed to prohibiting chemical weapons, or only committed if no one is upset, only committed if no one complains, only committed if no one is put out?

Specialty drugs account for just 2% of all medicines prescribed, yet they are on pace to comprise 50% of the drug spend in the next few years – ballooning to $400B in the US alone by 2020. Traditional approaches to drug utilization and cost management are simply not working. And biopharmaceutical pipelines are filled with new, high-priced, specialty drugs that continue to pressure health care budgets around the world. There is currently estimated to be up to $20billion in annual, solvable Specialty Rx inefficiencies in the US alone. By identifying which drugs are most effective for which patients (precision analytics for precision medicine) can a Decision support system (DSS) help solve the growing problem with Specialty Drug Use and Cost out of Control? This trend is unsustainable to the healthcare system.

BRUSSELS – Member nations of the global chemical weapons watchdog voted Wednesday to give the organization the authority to apportion blame for illegal attacks, expanding its powers following a bitter dispute pitting Britain and its Western allies against Russia and Syria. An 82-24 vote provided the two-thirds majority needed to enlarge the purview of the Organization for the Prohibition of Chemical Weapons. The organization was created to implement a 1997 treaty that banned chemical weapons, but lacked a mandate to name the parties it found responsible for using them. Many participating nations saw the inability to assign responsibility as a senseless hamstring, especially after fatal chemical attacks during the war in Syria. Russia opposed adding a new license to the agency's portfolio, saying that was a decision that belonged to the United Nations.

From 2015 to 2017 a joint United Nations-OPCW team had been appointed to assign blame for chemical attacks in Syria. It found that Syrian government troops used nerve agent sarin and chorine barrel bombs on several occasions, while Islamic State militants were found to have used sulfur mustard.

Britain made its proposal in the wake of the chemical attacks on an ex-spy and his daughter in the English city of Salisbury, as well as in Syria's civil war and attacks by the Islamic State group in Iraq. Britain has accused Russia of using a nerve agent in the attempted assassination in March of former spy Sergei Skripal, which Moscow strongly denies.