This paper addresses the time-intensive nature of systematic reviews (SRs) and proposes a solution leveraging advancements in Generative AI (e.g., ChatGPT) and external knowledge augmentation (e.g., Retrieval-Augmented Generation). The proposed system, GEAR-Up, automates query development and translation in SRs, enhancing efficiency by enriching user queries with context from language models and knowledge graphs. Collaborating with librarians, qualitative evaluations demonstrate improved reproducibility and search strategy quality. Access the demo at https://youtu.be/zMdP56GJ9mU.

Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based and data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamic environment. Human-curated symbolic knowledge sources, such as lexicons for standard language and slang terms, can potentially elevate social media signals in evolving language. We introduce a neurosymbolic method that integrates neural networks with symbolic knowledge sources, improving the detection and interpretation of mental health-related tweets relevant to COVID-19. Our method was evaluated using a corpus of large datasets (~12 billion tweets, 2.5 million subreddit data, and 700k news articles) and multiple knowledge graphs. This method dynamically adapts to evolving language, outperforming purely data-driven models with an F1 score exceeding 92%. This approach also showed faster adaptation to new data and lower computational demands than fine-tuning pre-trained large language models (LLMs). This study demonstrates the benefit of neurosymbolic methods in interpreting text in a dynamic environment for tasks such as health surveillance.

Recently, methods such as DeepCubeA have used deep reinforcement learning to learn domain-specific heuristic functions in a largely domain-independent fashion. However, such methods either assume a predetermined goal or assume that goals will be given as fully-specified states. Therefore, specifying a set of goal states to these learned heuristic functions is often impractical. To address this issue, we introduce a method of training a heuristic function that estimates the distance between a given state and a set of goal states represented as a set of ground atoms in first-order logic. Furthermore, to allow for more expressive goal specification, we introduce techniques for specifying goals as answer set programs and using answer set solvers to discover sets of ground atoms that meet the specified goals. In our experiments with the Rubik's cube, sliding tile puzzles, and Sokoban, we show that we can specify and reach different goals without any need to re-train the heuristic function. Our code is publicly available at https://github.com/forestagostinelli/SpecGoal.

After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for continued feedback-based refinements in design using chatbot-patient interactions. We propose Alleviate, a chatbot designed to assist patients suffering from mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed decisions. In addition, Alleviate's modular design and explainable decision-making lends itself to robust and continued feedback-based refinements to its design. In this paper, we explain the different modules of Alleviate and submit a short video demonstrating Alleviate's capabilities to help patients and clinicians understand each other better to facilitate optimal care strategies.

Modern artificial intelligence (AI) methods have been used to solve problems that many humans struggle to solve. This opens up new opportunities for knowledge discovery and education. We demonstrate ALLURE, an educational AI system for learning to solve the Rubik’s cube that is designed to help students improve their problem solving skills. ALLURE can both find and explain its own strategies for solving the Rubik’s cube as well as build on user-provided strategies. Collaboration between AI and user happens using visual and natural language modalities.

Developing the problem solving skills of children is a challenging problem that is crucial for the future of our society. Given that artificial intelligence (AI) has been used to solve problems across a wide variety of domains, AI offers unique opportunities to develop problem solving skills using a multitude of tasks that pique the curiosity of children. To make this a reality, it is necessary to address the uninterpretable “black-box” that AI often appears to be. Towards this goal, we design a collaborative artificial intelligence algorithm that uses a human-in-the-loop approach to allow students to discover their own personalized solutions to problems. This collaborative algorithm builds on state-of-the-art AI algorithms and leverages additional interpretable structures, namely knowledge graphs and decision trees, to create a fully interpretable process that is able to explain solutions in their entirety. We describe this algorithm when applied to solving the Rubik’s cube as well as our planned user-interface and assessment methods.