Assessing the quality of group deliberation is essential for improving our understanding of deliberative processes. The Deliberative Reason Index (DRI) offers a sophisticated metric for evaluating group reasoning, but its implementation has been constrained by the complex and time-consuming process of statement generation. This thesis introduces an innovative, automated approach to DRI statement generation that leverages advanced natural language processing (NLP) and large language models (LLMs) to substantially reduce the human effort involved in survey preparation. Key contributions are a systematic framework for automated DRI statement generation and a methodological innovation that significantly lowers the barrier to conducting comprehensive deliberative process assessments. In addition, the findings provide a replicable template for integrating generative artificial intelligence into social science research methodologies.

There is much interest in using large pre-trained models in Automatic Game Design (AGD), whether via the generation of code, assets, or more abstract conceptualization of design ideas. But so far this interest largely stems from the ad hoc use of such generative models under persistent human supervision. Much work remains to show how these tools can be integrated into longer-time-horizon AGD pipelines, in which systems interface with game engines to test generated content autonomously. To this end, we introduce ScriptDoctor, a Large Language Model (LLM)-driven system for automatically generating and testing games in PuzzleScript, an expressive but highly constrained description language for turn-based puzzle games over 2D gridworlds. ScriptDoctor generates and tests game design ideas in an iterative loop, where human-authored examples are used to ground the system's output, compilation errors from the PuzzleScript engine are used to elicit functional code, and search-based agents play-test generated games. ScriptDoctor serves as a concrete example of the potential of automated, open-ended LLM-based workflows in generating novel game content.

Implementing Deep Neural Networks (DNNs) on resource-constrained edge devices is a challenging task that requires tailored hardware accelerator architectures and a clear understanding of their performance characteristics when executing the intended AI workload. To facilitate this, we present an automated generation approach for fast performance models to accurately estimate the latency of a DNN mapped onto systematically modeled and concisely described accelerator architectures. Using our accelerator architecture description method, we modeled representative DNN accelerators such as Gemmini, UltraTrail, Plasticine-derived, and a parameterizable systolic array. Together with DNN mappings for those modeled architectures, we perform a combined DNN/hardware dependency graph analysis, which enables us, in the best case, to evaluate only 154 loop kernel iterations to estimate the performance for 4.19 billion instructions achieving a significant speedup. We outperform regression and analytical models in terms of mean absolute percentage error (MAPE) compared to simulation results, while being several magnitudes faster than an RTL simulation.

Domain probe lists--used to determine which URLs to probe for Web censorship--play a critical role in Internet censorship measurement studies. Indeed, the size and accuracy of the domain probe list limits the set of censored pages that can be detected; inaccurate lists can lead to an incomplete view of the censorship landscape or biased results. Previous efforts to generate domain probe lists have been mostly manual or crowdsourced. This approach is time-consuming, prone to errors, and does not scale well to the ever-changing censorship landscape. In this paper, we explore methods for automatically generating probe lists that are both comprehensive and up-to-date for Web censorship measurement. We start from an initial set of 139,957 unique URLs from various existing test lists consisting of pages from a variety of languages to generate new candidate pages. By analyzing content from these URLs (i.e., performing topic and keyword extraction), expanding these topics, and using them as a feed to search engines, our method produces 119,255 new URLs across 35,147 domains. We then test the new candidate pages by attempting to access each URL from servers in eleven different global locations over a span of four months to check for their connectivity and potential signs of censorship. Our measurements reveal that our method discovered over 1,400 domains--not present in the original dataset--we suspect to be blocked. In short, automatically updating probe lists is possible, and can help further automate censorship measurements at scale.

In recent years, data has emerged as the new gold, serving as a powerful tool for creating intelligent systems. However, procuring high-quality data remains challenging, especially for code. To address this, we developed TinyPy Generator, a tool that generates random Python programs using a context-free grammar. The generated programs are guaranteed to be correct by construction. Our system uses custom production rules (in the Backus-Naur Form (BNF) format) to recursively generate code. This allows us to generate code with different levels of complexity, ranging from code containing only assignments to more complex code containing conditionals and loops. Our proposed tool enables effortless large-scale Python code generation, beneficial for a wide range of applications. TinyPy Generator is particularly useful in the field of machine learning, where it can generate substantial amounts of Python code for training Python language models. Additionally, researchers who are studying programming languages can utilize this tool to create datasets for their experiments, which can help validate the robustness of code interpreters or compilers. Unlike existing research, we have open-sourced our implementation. This allows customization according to user needs and extends potential usage to other languages.

With increasing complexity of Automated Driving Systems (ADS), ensuring their safety and reliability has become a critical challenge. The Verification and Validation (V&V) of these systems are particularly demanding when AI components are employed to implement perception and/or control functions. In ESA-funded project VIVAS, we developed a generic framework for system-level simulation-based V&V of autonomous systems. The approach is based on a simulation model of the system, an abstract model that describes symbolically the system behavior, and formal methods to generate scenarios and verify the simulation executions. Various coverage criteria can be defined to guide the automated generation of the scenarios. In this paper, we describe the instantiation of the VIVAS framework for an ADS case study. This is based on the integration of CARLA, a widely-used driving simulator, and its ScenarioRunner tool, which enables the creation of diverse and complex driving scenarios. This is also used in the CARLA Autonomous Driving Challenge to validate different ADS agents for perception and control based on AI, shared by the CARLA community. We describe the development of an abstract ADS model and the formulation of a coverage criterion that focuses on the behaviors of vehicles relative to the vehicle with ADS under verification. Leveraging the VIVAS framework, we generate and execute various driving scenarios, thus testing the capabilities of the AI components. The results show the effectiveness of VIVAS in automatically generating scenarios for system-level simulation-based V&V of an automated driving system using CARLA and ScenarioRunner. Therefore, they highlight the potential of the approach as a powerful tool in the future of ADS V&V methodologies.

Semantic image synthesis (SIS) refers to the problem of generating realistic imagery given a semantic segmentation mask that defines the spatial layout of object classes. Most of the approaches in the literature, other than the quality of the generated images, put effort in finding solutions to increase the generation diversity in terms of style i.e. texture. However, they all neglect a different feature, which is the possibility of manipulating the layout provided by the mask. Currently, the only way to do so is manually by means of graphical users interfaces. In this paper, we describe a network architecture to address the problem of automatically manipulating or generating the shape of object classes in semantic segmentation masks, with specific focus on human faces. Our proposed model allows embedding the mask class-wise into a latent space where each class embedding can be independently edited. Then, a bi-directional LSTM block and a convolutional decoder output a new, locally manipulated mask. We report quantitative and qualitative results on the CelebMask-HQ dataset, which show our model can both faithfully reconstruct and modify a segmentation mask at the class level. Also, we show our model can be put before a SIS generator, opening the way to a fully automatic generation control of both shape and texture.

Tabular data is the most common format to publish and exchange structured data online. A clear example is the growing number of open data portals published by all types of public administrations. However, exploitation of these data sources is currently limited to technical people able to programmatically manipulate and digest such data. As an alternative, we propose the use of chatbots to offer a conversational interface to facilitate the exploration of tabular data sources. With our approach, any regular citizen can benefit and leverage them. Moreover, our chatbots are not manually created: instead, they are automatically generated from the data source itself thanks to the instantiation of a configurable collection of conversation patterns.

In this paper we present the Process-To-Text (P2T) framework for the automatic generation of textual descriptive explanations of processes. P2T integrates three AI paradigms: process mining for extracting temporal and structural information from a process, fuzzy linguistic protoforms for modelling uncertain terms, and natural language generation for building the explanations. A real use-case in the cardiology domain is presented, showing the potential of P2T for providing natural language explanations addressed to specialists.

Machine learning (ML) solutions are prevalent in many applications. However, many challenges exist in making these solutions business-grade. For instance, maintaining the error rate of the underlying ML models at an acceptably low level. Typically, the true relationship between feature inputs and the target feature to be predicted is uncertain, and hence statistical in nature. The approach we propose is to separate the observations that are the most likely to be predicted incorrectly into 'attention sets'. These can directly aid model diagnosis and improvement, and be used to decide on alternative courses of action for these problematic observations. We present several algorithms (`strategies') for determining optimal rules to separate these observations. In particular, we prefer strategies that use feature-based slicing because they are human-interpretable, model-agnostic, and require minimal supplementary inputs or knowledge. In addition, we show that these strategies outperform several common baselines, such as selecting observations with prediction confidence below a threshold. To evaluate strategies, we introduce metrics to measure various desired qualities, such as their performance, stability, and generalizability to unseen data; the strategies are evaluated on several publicly-available datasets. We use TOPSIS, a Multiple Criteria Decision Making method, to aggregate these metrics into a single quality score for each strategy, to allow comparison.