Over the last year, the amount of research in hierarchical planning has increased, leading to significant improvements in the performance of planners. However, the research is diverging and planners are somewhat hard to compare against each other. This is mostly caused by the fact that there is no standard set of benchmark domains, nor even a common description language for hierarchical planning problems. As a consequence, the available planners support a widely varying set of features and (almost) none of them can solve (or even parse) any problem developed for another planner. With this paper, we propose to create a new track for the IPC in which hierarchical planners will compete. This competition will result in a standardised description language, broader support for core features of that language among planners, a set of benchmark problems, a means to fairly and objectively compare HTN planners, and for new challenges for planners. Introduction When the International Planning Competition (IPC) started out in 1998 it aimed to include both classical and hierarchical planners as competitors (McDermott 2000).

AI agents are increasingly deployed and used to make automated decisions that affect our lives on a daily basis. It is imperative to ensure that these systems embed ethical principles and respect human values. We focus on how we can attest to whether AI agents treat users fairly without discriminating against particular individuals or groups through biases in language. In particular, we discuss human unconscious biases, how they are embedded in language, and how AI systems inherit those biases by learning from and processing human language. Then, we outline a roadmap for future research to better understand and attest problematic AI biases derived from language.

Though Deep Neural Networks (DNN) show excellent performance across various computer vision tasks, several works show their vulnerability to adversarial samples, i.e., image samples with imperceptible noise engineered to manipulate the network's prediction. Adversarial sample generation methods range from simple to complex optimization techniques. Majority of these methods generate adversaries through optimization objectives that are tied to the pre-softmax or softmax output of the network. In this work we, (i) show the drawbacks of such attacks, (ii) propose two new evaluation metrics: Old Label New Rank (OLNR) and New Label Old Rank (NLOR) in order to quantify the extent of damage made by an attack, and (iii) propose a new adversarial attack FDA: Feature Disruptive Attack, to address the drawbacks of existing attacks. FDA works by generating image perturbation that disrupt features at each layer of the network and causes deep-features to be highly corrupt. This allows FDA adversaries to severely reduce the performance of deep networks. W e experimentally validate that FDA generates stronger adversaries than other state-of-the-art methods for image classification, even in the presence of various defense measures. More importantly, we show that FDA disrupts feature-representation based tasks even without access to the task-specific network or methodology.

The diversity of agent behaviors is an important topic for the quality of video games and virtual environments in general. Offering the most compelling experience for users with different skills is a difficult task, and usually needs important manual human effort for tuning existing code. This can get even harder when dealing with adaptive difficulty systems. Our paper's main purpose is to create a framework that can automatically create behaviors for game agents of different difficulty classes and enough diversity. In parallel with this, a second purpose is to create more automated tests for showing defects in the source code or possible logic exploits with less human effort.

Mixed cooperative-competitive control scenarios such as human-machine interaction with individual goals of the interacting partners are very challenging for reinforcement learning agents. In order to contribute towards intuitive human-machine collaboration, we focus on problems in the continuous state and control domain where no explicit communication is considered and the agents do not know the others' goals or control laws but only sense their control inputs retrospectively. Our proposed framework combines a learned partner model based on online data with a reinforcement learning agent that is trained in a simulated environment including the partner model. Thus, we overcome drawbacks of independent learners and, in addition, benefit from a reduced amount of real world data required for reinforcement learning which is vital in the human-machine context. We finally analyze an example that demonstrates the merits of our proposed framework which learns fast due to the simulated environment and adapts to the continuously changing partner due to the partner approximation. Keywords: Reinforcement Learning, Mixed Cooperative-Competitive Control, Opponent Modeling.

Combination of Unified Embedding Model and Observed Features for Knowledge Graph Completion T akuma Ebisu 1,2 and Ryutaro Ichise 2,1,3 1 SOKENDAI (The Graduate University for Advanced Studies) 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan 2 National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan 3 National Institute of Advanced Industrial Science and Technology 2-3-26 Aomi, Koto-ku, Tokyo, Japan {takuma,ichise}@nii.ac.jp Abstract Knowledge graphs are useful for many artificial intelligence tasks but often have missing data. Hence, a method for completing knowledge graphs is required. Existing approaches include embedding models, the Path Ranking Algorithm, and rule evaluation models. However, these approaches have limitations. For example, all the information is mixed and difficult to interpret in embedding models, and traditional rule evaluation models are basically slow. In this paper, we provide an integrated view of various approaches and combine them to compensate for their limitations. We first unify state-of-the-art embedding models, such as ComplEx and TorusE, reinterpreting them as a variant of translation-based models. Then, we show that these models utilize paths for link prediction and propose a method for evaluating rules based on this idea. Finally, we combine an embedding model and observed feature models to predict missing triples. This is possible because all of these models utilize paths. We also conduct experiments, including link prediction tasks, with standard datasets to evaluate our method and framework. The experiments show that our method can evaluate rules faster than traditional methods and that our framework outperforms state-of-the-art models in terms of link prediction. 1 Introduction Knowledge graphs are used to describe many types of real-world relations in a form that can be easily processed by a computer.

--Many real-world applications reveal difficulties in learning classifiers from imbalanced data. The rising big data era has been witnessing more classification tasks with large-scale but extremely imbalance and low-quality datasets. Most of existing learning methods suffer from poor performance or low computation efficiency under such a scenario. T o tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers. T aking those factors into consideration, we propose a novel framework for imbalance classification that aims to generate a strong ensemble by self-paced harmonizing data hardness via under-sampling. Extensive experiments have shown that this new framework, while being very computationally efficient, can lead to robust performance even under highly overlapping classes and extremely skewed distribution. Note that, our methods can be easily adapted to most of existing learning methods (e.g., C4.5, SVM, GBDT and Neural Network) to boost their performance on imbalanced data. I NTRODUCTION The development of information technology brings the explosion of massive data in our daily life. However, many real applications usually generate very imbalanced datasets for corresponding key classification tasks. For instance, online advertising services can give rise to a high amount of datasets, consisting of user views or clicks on ads, for the task of click-through rate prediction [1]. Commonly, user clicks only constitute a small rate of user behaviors . For another example, credit fraud detection [2] relies on the dataset containing massive real credit card transactions where only a small proportion are frauds. Similar situations also exist in the tasks of medical diagnosis, record linkage and network intrusion detection etc [3]-[5]. In addition, real-world datasets are likely to contain other difficulty factors, including noises and missing values. Such highly imbalanced, large-scale and noisy data brings serious challenges of downstream classification tasks.

Bipartite b -matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas such as healthcare, advertising, education, and genera l resource allocation. Traditionally, the primary goal of su ch models is to maximize a linear function of the constituent matches (e.g., linear social welfare maximization) subjec t to some constraints. Recent work has studied a new goal of balancing whole-match diversity and economic efficiency, where the objective is instead a monotone submodular function ove r the matching. These more general models are largely NPhard. In this work, we develop a combinatorial algorithm tha t constructs provably-optimal diverse b -matchings in pseudo-polynomial time. Then, we show how to extend our algorithm to solve new variations of the diverse b -matching problem. We then compare directly, on real-world datasets, against the state-of-the-art, quadratic-programming-based appr oach to solving diverse b -matching problems and show that our method outperforms it in both speed and (anytime) solution quality.

Probabilistic Boolean Networks (PBNs) were introduced as a computational model for studying gene interactions in Gene Regulatory Networks (GRNs). Controllability of PBNs, and hence GRNs, is the process of making strategic interventions to a network in order to drive it from a particular state towards some other potentially more desirable state. This is of significant importance to systems biology as successful control could be used to obtain potential gene treatments by making therapeutic interventions. Recent advancements in Deep Reinforcement Learning have enabled systems to develop policies merely by interacting with the environment, without complete knowledge of the underlying Markov Decision Process (MDP). In this paper we have implemented a Deep Q Network with Double Q Learning, that directly interacts with the environment -that is, a Probabilistic Boolean Network. Our approach develops a control policy by sampling experiences obtained from the environment using Prioritized Experience Replay which successfully drives a PBN from any state towards the desired one. This novel approach sets the foundations for overcoming the inability to scale to larger PBNs and opens up the spectrum in which to consider control of GRNs without the need of a computational model, i.e. by direct interventions to the GRN.

Automated machine learning aims to automate the whole process of machine learning, including model configuration. In this paper, we focus on automated hyperparameter optimization (HPO) based on sequential model-based optimization (SMBO). Though conventional SMBO algorithms work well when abundant HPO trials are available, they are far from satisfactory in practical applications where a trial on a huge dataset may be so costly that an optimal hyperparameter configuration is expected to return in as few trials as possible. Observing that human experts draw on their expertise in a machine learning model by trying configurations that once performed well on other datasets, we are inspired to speed up HPO by transferring knowledge from historical HPO trials on other datasets. We propose an end-to-end and efficient HPO algorithm named as Transfer Neural Processes (TNP), which achieves transfer learning by incorporating trials on other datasets, initializing the model with well-generalized parameters, and learning an initial set of hyperparameters to evaluate. Experiments on extensive OpenML datasets and three computer vision datasets show that the proposed model can achieve state-of-the-art performance in at least one order of magnitude less trials.