Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.
Procedural content generation via Machine Learning @cite_10 is a relatively new field, focused on generating content through machine learning methods. The majority of PCGML approaches represent black box methods, without any prior approach focused on explainability or co-creativity. We note some discussion in the survey paper on potential collaborative approaches. Summerville explored adapting levels to players, but no work to our knowledge looks at adapting models to individual designers.
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Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise. This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content such as sprites and sound effects. In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the resulting generated content. Multiple PCGML methods are covered, including neural networks, long short-term memory (LSTM) networks, autoencoders, and deep convolutional networks; Markov models, @math -grams, and multi-dimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in the application of PCGML, including learning from small datasets, lack of training data, multi-layered learning, style-transfer, parameter tuning, and PCG as a game mechanic. A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities. It is based on the decomposition of a model's predictions on individual contributions of each attribute. Our method works for the so-called black box models such as support vector machines, neural networks, and nearest neighbor algorithms, as well as for ensemble methods such as boosting and random forests. We demonstrate that the generated explanations closely follow the learned models and present a visualization technique that shows the utility of our approach and enables the comparison of different prediction methods. Hundreds of millions of people play computer games every day. For them, game content—from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so far ensured that the quality and quantity of game content matched the demands of the playing community, but is facing new scalability challenges due to the exponential growth over the last decade of both the gamer population and the production costs. Procedural Content Generation for Games (PCG-G) may address these challenges by automating, or aiding in, game content generation. PCG-G is difficult, since the generator has to create the content, satisfy constraints imposed by the artist, and return interesting instances for gamers. Despite a large body of research focusing on PCG-G, particularly over the past decade, ours is the first comprehensive survey of the field of PCG-G. We first introduce a comprehensive, six-layered taxonomy of game content: bits, space, systems, scenarios, design, and derived. Second, we survey the methods used across the whole field of PCG-G from a large research body. Third, we map PCG-G methods to game content layers; it turns out that many of the methods used to generate game content from one layer can be used to generate content from another. We also survey the use of methods in practice, that is, in commercial or prototype games. Fourth and last, we discuss several directions for future research in PCG-G, which we believe deserve close attention in the near future. Procedural modelling deals with semi-automatic content generation by means of a program or procedure. Among other advantages, its data compression and the potential to generate a large variety of detailed content with reduced human intervention, have made procedural modelling attractive for creating virtual environments increasingly used in movies, games and simulations. We survey procedural methods that are useful to generate features of virtual worlds, including terrains, vegetation, rivers, roads, buildings and entire cities. In this survey, we focus particularly on the degree of intuitive control and of interactivity offered by each procedural method, because these properties are instrumental for their typical users: designers and artists. We identify the most promising research results that have been recently achieved, but we also realize that there is far from widespread acceptance of procedural methods among non-technical, creative professionals. We conclude by discussing some of the most important challenges of procedural modelling. The Level Generation Competition, part of the IEEE Computational Intelligence Society (CIS)-sponsored 2010 Mario AI Championship, was to our knowledge the world's first procedural content generation competition. Competitors participated by submitting level generators - software that generates new levels for a version of Super Mario Bros tailored to individual players' playing style. This paper presents the rules of the competition, the software used, the scoring procedure, the submitted level generators, and the results of the competition. We also discuss what can be learned from this competition, both about organizing procedural content generation competitions and about automatically generating levels for platform games. The paper is coauthored by the organizers of the competition (the first three authors) and the competitors. Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward - it is to design models that are inherently interpretable. The paper focuses on the problems of passive and active FSM inference as well as checking sequence generation. We consider the setting where an FSM cannot be reset so that its inference is constrained to a single trace either given a priori in a passive inference scenario or to be constructed in an active inference scenario or aiming at obtaining checking sequence for a given FSM. In each of the last two cases, the expected result is a trace representing a checking sequence for an inferred machine, if it was not given. We demonstrate that this can be achieved by a repetitive use of a procedure that infers an FSM from a given trace (identifying a minimal machine consistent with a trace) avoiding equivalent conjectures. We thus show that FSM inference and checking sequence construction are two sides of the same coin. Following an existing approach of constructing conjectures by SAT solving, we elaborate first such a procedure and then based on it the methods for obtaining checking sequence for a given FSM and inferring a machine from a black box. The novelty of our approach is that it does not use any state identification facilities. We demonstrate that the proposed approach can also be constrained to find a solution in a subset of FSMs represented by a nondeterministic mutation machine. Experiments with a prototype implementation of the developed approach using an existing SAT solver indicate that it scales for FSMs with up to a dozen of states and requires relatively short sequences to identify a black box machine. As technology advances, new products (e.g., digital cameras, computer tablets, etc.) have become increasingly more complex. Researchers often face considerable challenges in understanding consumers’ preferences for such products. This paper proposes an adaptive decompositional framework to elicit consumers’ preferences for complex products. The proposed method starts with collaborative-filtered initial part-worths, followed by an adaptive question selection process that uses a fuzzy support vector machine active learning algorithm to adaptively refine the individual-specific preference estimate after each question. Our empirical and synthetic studies suggest that the proposed method performs well for product categories equipped with as many as 70 to 100 attribute levels, which is typically considered prohibitive for decompositional preference elicitation methods. In addition, we demonstrate that the proposed method provides a natural remedy for a long-standing challenge in adaptive question design by gauging the possibility of response errors on the fly and incorporating the results into the survey design. This research also explores in a live setting how responses from previous respondents may be used to facilitate active learning of the focal respondent’s product preferences. Overall, the proposed approach offers new capabilities that complement existing preference elicitation methods, particularly in the context of complex products.Data, as supplemental material, are available at http: dx.doi.org 10.1287 mksc.2015.0946 . Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global optimisation of black-box functions which are expensive to evaluate. At the core of this approach is a Gaussian process prior that captures our belief about the distribution over functions. However, in many cases a single Gaussian process is not flexible enough to capture non-stationarity in the objective function. Consequently, heteroscedasticity negatively affects performance of traditional Bayesian methods. In this paper, we propose a novel prior model with hierarchical parameter learning that tackles the problem of non-stationarity in Bayesian optimisation. Our results demonstrate substantial improvements in a wide range of applications, including automatic machine learning and mining exploration. The automatic acquisition of a similarity measure for a CBR system is appealing as it frees the system designer from the tedious task of defining it manually. However, acquiring similarity measures with some machine learning approach typically results in some black box representation of similarity whose magic-like combination of high precision and low explainability may decrease a human user’s trust in the system. In this paper, we target this problem by suggesting a method to induce a human-readable and easily understandable – and thus potentially trustworthy – representation of similarity from a previously learned black box-like representation of similarity measures. Our experimental evaluations support the claim that, given some highly precise learned similarity measure, we can induce a less powerful, but human-understandable representation of it while its corresponding level of accuracy is only marginally impaired. This paper introduces an approach to exploration and discovery in high-dimensional data that incorporates a user's knowledge and questions to craft sets of projection functions meaningful to them. Unlike most prior work that defines projections based on their statistical properties, our approach creates projection functions that align with user-specified annotations. Therefore, the resulting derived dimensions represent concepts defined by the user's examples. These especially crafted projection functions, or explainers, can help find and explain relationships between the data variables and user-designated concepts. They can organize the data according to these concepts. Sets of explainers can provide multiple perspectives on the data. Our approach considers tradeoffs in choosing these projection functions, including their simplicity, expressive power, alignment with prior knowledge, and diversity. We provide techniques for creating collections of explainers. The methods, based on machine learning optimization frameworks, allow exploring the tradeoffs. We demonstrate our approach on model problems and applications in text analysis. Telling a story is a difficult task that requires a variety of knowledge and cognitive processes: knowledge about themes, writing techniques, the story world, and presentation; processes such as planning, problem-solving, recall and creativity. This dissertation presents a model of the storytelling process which incorporates theories of creativity, memory and author-level planning. This model has been implemented in a computer program called MINSTREL which tells short, theme-based stories about King Arthur and his Knights of the Round Table. MINSTREL's creativity process is based upon creativity heuristics called Transform-Recall-Adapt Methods (TRAMs). Each TRAM integrates a simple problem transformation which searches the problem-space for new knowledge to apply to a problem with a corresponding adaptation which can adapt any discovered knowledge to the original problem. By using TRAMs to augment the problem-solving process, MINSTREL is able to invent useful new problem solutions. By incorporating creativity into the recall process, MINSTREL makes creativity available to any cognitive process, and permits the use of multiple TRAMs to make creative "leaps". MINSTREL's storytelling process is based upon a model of author-level problem-solving. In addition to a process model of author-level problem-solving, MINSTREL implements four important classes of author-level goals and plans: (1) Thematic, (2) Dramatic, (3) Consistency, and (4) Presentation. MINSTREL uses these goals and plans to tell a number of short stories in the King Arthur domain. MINSTREL is a computational model of the cognitive processes of storytelling and creativity. MINSTREL (1) describes a process model of storytelling, (2) identifies important storytelling goals and plans, (3) identifies fundamental storytelling processes, (4) describes a process model of creativity, (5) explains how a problem-solver can find and adapt old knowledge to create new solutions, (6) identifies useful creativity heuristics, (7) explains creative "leaps", (8) explains the relation of creativity to problem-solving, (9) describes the relationship between memory and creativity, and (10) integrates creativity into a larger cognitive model. Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk. In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection. Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: 1) it provides an elegant approach to action-selection (exploration exploitation) as a function of the uncertainty in learning; and 2) it provides a machinery to incorporate prior knowledge into the algorithms. We first discuss models and methods for Bayesian inference in the simple single-step Bandit model. We then review the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties. We propose Black Box Explanations through Transparent Approximations (BETA), a novel model agnostic framework for explaining the behavior of any black-box classifier by simultaneously optimizing for fidelity to the original model and interpretability of the explanation. To this end, we develop a novel objective function which allows us to learn (with optimality guarantees), a small number of compact decision sets each of which explains the behavior of the black box model in unambiguous, well-defined regions of feature space. Furthermore, our framework also is capable of accepting user input when generating these approximations, thus allowing users to interactively explore how the black-box model behaves in different subspaces that are of interest to the user. To the best of our knowledge, this is the first approach which can produce global explanations of the behavior of any given black box model through joint optimization of unambiguity, fidelity, and interpretability, while also allowing users to explore model behavior based on their preferences. Experimental evaluation with real-world datasets and user studies demonstrates that our approach can generate highly compact, easy-to-understand, yet accurate approximations of various kinds of predictive models compared to state-of-the-art baselines. Bayesian methods for machine learning have been widely investigated,yielding principled methods for incorporating prior information intoinference algorithms. In this survey, we provide an in-depth reviewof the role of Bayesian methods for the reinforcement learning RLparadigm. The major incentives for incorporating Bayesian reasoningin RL are: 1 it provides an elegant approach to action-selection exploration exploitation as a function of the uncertainty in learning; and2 it provides a machinery to incorporate prior knowledge into the algorithms.We first discuss models and methods for Bayesian inferencein the simple single-step Bandit model. We then review the extensiverecent literature on Bayesian methods for model-based RL, where priorinformation can be expressed on the parameters of the Markov model.We also present Bayesian methods for model-free RL, where priors areexpressed over the value function or policy class. The objective of thepaper is to provide a comprehensive survey on Bayesian RL algorithmsand their theoretical and empirical properties. Abstract Nowadays huge volumes of data are produced in the form of fast streams, which are further affected by non-stationary phenomena. The resulting lack of stationarity in the distribution of the produced data calls for efficient and scalable algorithms for online analysis capable of adapting to such changes (concept drift). The online learning field has lately turned its focus on this challenging scenario, by designing incremental learning algorithms that avoid becoming obsolete after a concept drift occurs. Despite the noted activity in the literature, a need for new efficient and scalable algorithms that adapt to the drift still prevails as a research topic deserving further effort. Surprisingly, Spiking Neural Networks, one of the major exponents of the third generation of artificial neural networks, have not been thoroughly studied as an online learning approach, even though they are naturally suited to easily and quickly adapting to changing environments. This work covers this research gap by adapting Spiking Neural Networks to meet the processing requirements that online learning scenarios impose. In particular the work focuses on limiting the size of the neuron repository and making the most of this limited size by resorting to data reduction techniques. Experiments with synthetic and real data sets are discussed, leading to the empirically validated assertion that, by virtue of a tailored exploitation of the neuron repository, Spiking Neural Networks adapt better to drifts, obtaining higher accuracy scores than naive versions of Spiking Neural Networks for online learning environments. Passive global positioning system (GPS) travel survey has proven an innovative alternative to the traditional paper-and-pencil method, as it can record exact time and location of travel activities without incurring much burden on survey respondents. Lying in the core of this technique is how to derive accurate travel information (e.g., trip purpose) from GPS data. Previous studies rely on simple rules that are manually extracted from the inherent structure of GPS data streams. These methods, however, are difficult to generalize for other applications and lack helpful hints for new research. This paper presents a machine learning approach to deriving trip purpose from GPS track data coupled with other relevant data sources. This approach employs a number of attributes (i.e., time stamp and land-use type of trip ends, a set of spatiotemporal indices of travel, and demographic and socioeconomic characteristics of respondents) to construct a decision tree for purpose of classification. Each attribute provides partial evidence to the depiction of a given purpose, but this depiction may or may not be monotonic, and none of them can work alone toward the goal. A reasoning procedure using the adaptive boosting technique was designed to explore how these attributes could work together to achieve trip purpose derivation. This technique generated multiple decision trees to improve the classification results through a mechanism of voting from these trees. Each tree was constructed in the depth-first fashion with the root node and the split of subsequent nodes being determined on the basis of the gain-ratio computed for the relevant attributes. This procedure was implemented in the C5.0 machine learning environment with 226 GPS trip records collected from 36 respondents. The experimental results seemed rather promising: using 10 iterations for adaptive boosting, an overall classification accuracy of 87.6 was achieved.