This position paper describes an AI application for game spectators, e.g., those watching Twitch. The aim of this application is to automatically generate game plays by nonplayer characters -- not human players -- and recommend those plays to spectators. The generation part leads to development of a new field: procedural play generation (PPG). The recommendation part requires new techniques in recommender systems (RS) for incorporation of play content into RS to obtain promising recommendation results. Rather than proposing solutions to all relevant topics, this paper aims at drawing attention to this new field and serves as a seed for discussion and collaboration among the readers, workshop participants, and authors.

We propose a new linear algebraic approach to the computation of Tarskian semantics in logic. We embed a finite model M in first-order logic with N entities in N-dimensional Euclidean space R^N by mapping entities of M to N dimensional one-hot vectors and k-ary relations to order-k adjacency tensors (multi-way arrays). Second given a logical formula F in prenex normal form, we compile F into a set Sigma_F of algebraic formulas in multi-linear algebra with a nonlinear operation. In this compilation, existential quantifiers are compiled into a specific type of tensors, e.g., identity matrices in the case of quantifying two occurrences of a variable. It is shown that a systematic evaluation of Sigma_F in R N gives the truth value, 1(true) or 0(false), of F in M. Based on this framework, we also propose an unprecedented way of computing the least models defined by Datalog programs in linear spaces via matrix equations and empirically show its effectiveness compared to state-of-the-art approaches.

It is often critical in real-world applications to: (i) perform inverse learning of the cost parameters of a multi-objective reward based on observed agent behavior; (ii) perform sensitivity analyses of policies to various parameter settings; and (iii) analyze and optimize policy performance as a function of policy parameters. When such problems have mixed discrete and continuous state and/or action spaces, this leads to parameterized hybrid MDPs (PHMDPs) that are often approximately solved via discretization, sampling, and/or local gradient methods (when optimization is involved). In this paper we combine two recent advances that allow for the first exact solution and optimization of PHMDPs. We first show how each of the aforementioned use cases can be formalized as PHMDPs, which can then be solved via an extension of symbolic dynamic programming (SDP) even when the solution is piecewise nonlinear. Secondly, we leverage recent advances in non-convex solvers such as dReal and dOp (that offer δ-optimality guarantees for nonlinear problems given a symbolic function) for non-convex global optimization in (i), (ii), and (iii) using SDP to derive symbolic solutions to each PHMDP formalization. We demonstrate the efficacy and scalability of our framework by calculating the first known exact solutions to complex nonlinear examples of each of the aforementioned use cases.

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.

Visual style transfer involves combining the content of one image with the style of another, and recent work has produced some compelling results. This paper proposes a related task that requires additional system intelligence and autonomy—that of style creation. Rather than using the style of an existing source image, the goal is to have the system autonomously create a rendering style based on a simple (text- based) semantic description. Results demonstrate the system’s ability to autonomously create interesting, semantically appropriate styles that can be applied for image rendering.

In distributed machine learning, data is dispatched to multiple machines for processing. Motivated by the fact that similar data points often belong to the same or similar classes, and more generally, classification rules of high accuracy tend to be "locally simple but globally complex" (Vapnik and Bottou 1993), we propose data dependent dispatching that takes advantage of such structure. We present an in-depth analysis of this model, providing new algorithms with provable worst-case guarantees, analysis proving existing scalable heuristics perform well in natural non worst-case conditions, and techniques for extending a dispatching rule from a small sample to the entire distribution. We overcome novel technical challenges to satisfy important conditions for accurate distributed learning, including fault tolerance and balancedness. We empirically compare our approach with baselines based on random partitioning, balanced partition trees, and locality sensitive hashing, showing that we achieve significantly higher accuracy on both synthetic and real world image and advertising datasets. We also demonstrate that our technique strongly scales with the available computing power.

This is an Out-of-vocabulary (OOV) words still account for a significant extremely conservative use of crowdsourcing, particularly number of the mistakes by both speech recognizers as their crowdsource workers really do speak the words in and text-to-speech synthesizers. These are not words that their experiments, rather than selecting the correct pronunciation are merely very rare, but words that were unknown to the in a multiple choice question format. Our approach lexicon used by the automatic speech recognizer (ASR) or uses nothing more than a larger number of speakers (101) text-to-speech synthesizer (TTS). In the case of ASR, even and an acoustic model in order to find the pronunciation if the pronunciation is accurately modelled, there can be a almost ab nihilo, by constructing phone lattices and submitting question as to how to spell it correctly. In the case of TTS candidate pronunciation paths to a simple weighted systems, the pronunciation of the word may be unknown, as voting algorithm that combines results across crowdsource the component euphemistically known as "letter-to-sound" workers. Our only assumption is that the basic phonetic inventory or "grapheme-to-phoneme" rules may in fact not be able to is known to the acoustic model (e.g., the pronunciation infer the pronunciation from the spelling of the word, particularly of Rodriguez selected using an English acoustic model if its provenance is unknown, or the writing system is would never trill the r's). Furthermore, whereas Rutherford more logographically constructed.

The advent of multiple study on crowdsourcing for speech applications concluded crowdsourcing vendors and software infrastructure has that "although the crowd sometimes approached the level greatly helped this effort. Several providers also offer integrated of the experts, it never surpassed it" (Parent and Eskenazi filtering tools that allow users to customize different 2011)). This is exacerbated during multimodal dialog data aspects of their data collection, including target population, collections, where it becomes harder to quality-control for geographical location, demographics and sometimes usable audio-video data, due to a variety of factors including even education level and expertise. Managed crowdsourcing poor visual quality caused by variable lighting, position, providers extend these options by offering further customization or occlusions, participant or administrator error, or technical and end-to-end management of the entire data issues with the system or network (McDuff, Kaliouby, and collection operation.

We present a framework for automated analysis and categorization of .onion websites in the darkweb to facilitate analyst situational awareness of new content that emerges from this dynamic landscape. Over the last two years, our team has developed a large-scale darkweb crawling infrastructure called OnionCrawler that acquires new onion domains on a daily basis, and crawls and indexes millions of pages from these new and previously known .onion sites. It stores this data into a research repository designed to help better understand Tor’s hidden service ecosystem. The analysis component of our framework is called Automated Tool for Onion Labeling (ATOL), which introduces a two-stage thematic labeling strategy: (1) it learns descriptive and discriminative keywords for different categories, and (2) uses these terms to map onion site content to a set of thematic labels. We also present empirical results of ATOL and our ongoing experimentation with it, as we have gained experience applying it to the entirety of our darkweb repository, now over 70 million indexed pages. We find that ATOL can perform site-level thematic label assignment more accurately than keywordbased schemes developed by domain experts — we expand the analyst-provided keywords using an automatic keyword discovery algorithm, and get 12% gain in accuracy by using a machine learning classification model. We also show how ATOL can discover categories on previously unlabeled onions and discuss applications of ATOL in supporting various analyses and investigations of the darkweb.

A kidney exchange is a centrally-administered barter market where patients swap their willing yet incompatible donors. Modern kidney exchanges use 2-cycles, 3-cycles, and chains initiated by non-directed donors (altruists who are willing to give a kidney to anyone) as the means for swapping. We propose significant generalizations to kidney exchange. We allow more than one donor to donate in exchange for their desired patient receiving a kidney. We also allow for the possibility of a donor willing to donate if any of a number of patients receive kidneys. Furthermore, we combine these notions and generalize them.The generalization is to exchange among organ clubs, where a club is willing to donate organs outside the club if and only if the club receives organs from outside the club according to given specifications. Forms of organ clubs already exist — under an arrangement where one gets to be in the club as a potential recipient if one is willing to donate one's organs to the club upon death. Our approach can be used as an inter-club exchange mechanism that increases systemwide good (and can also be applied to live donation). In this paper we introduce these ideas, present the notion of operation frames that can be used to sequence the operations across batches, and present integer programming formulations for the market clearing problems for these new types of organ exchanges.