A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON

"A common decision made by people, whether healthy or with health conditions, is choosing meals like breakfast, lunch, and dinner, comprising combinations of foods for appetizer, main course, side dishes, desserts, and beverages. Often, this decision involves tradeoffs between nutritious choices (e.g., salt and sugar levels, nutrition content) and convenience (e.g., cost and accessibility, cuisine type, food source type). We present a data-driven solution for meal recommendations that considers customizable meal configurations and time horizons. This solution balances user preferences while accounting for food constituents and cooking processes. Our contributions include introducing goodness measures, a recipe conversion method from text to the recently introduced multimodal rich recipe representation (R3) format, learning methods using contextual bandits that show promising preliminary results, and the prototype, usage-inspired, BEACON system."

A Neurosymbolic Fast and Slow Architecture for Graph Coloring

Constraint Satisfaction Problems (CSPs) present significant challenges to artificial intelligence due to their intricate constraints and the necessity for precise solutions. Existing symbolic solvers are often slow, and prior research has shown that Large Language Models (LLMs) alone struggle with CSPs because of their complexity. To bridge this gap, we build upon the existing SOFAI architecture (or SOFAI-v1), which adapts Daniel Kahneman's ''Thinking, Fast and Slow'' cognitive model to AI. Our enhanced architecture, SOFAI-v2, integrates refined metacognitive governance mechanisms to improve adaptability across complex domains, specifically tailored for solving CSPs like graph coloring. SOFAI-v2 combines a fast System 1 (S1) based on LLMs with a deliberative System 2 (S2) governed by a metacognition module. S1's initial solutions, often limited by non-adherence to constraints, are enhanced through metacognitive governance, which provides targeted feedback and examples to adapt S1 to CSP requirements. If S1 fails to solve the problem, metacognition strategically invokes S2, ensuring accurate and reliable solutions. With empirical results, we show that SOFAI-v2 for graph coloring problems achieves a 16.98% increased success rate and is 32.42% faster than symbolic solvers.

Towards Effective Planning Strategies for Dynamic Opinion Networks

In this study, we investigate the under-explored intervention planning aimed at disseminating accurate information within dynamic opinion networks by leveraging learning strategies. Intervention planning involves identifying key nodes (search) and exerting control (e.g., disseminating accurate/official information through the nodes) to mitigate the influence of misinformation. However, as network size increases, the problem becomes computationally intractable. To address this, we first introduce a novel ranking algorithm (search) to identify key nodes for disseminating accurate information, which facilitates the training of neural network (NN) classifiers for scalable and generalized solutions. Second, we address the complexity of label generation (through search) by developing a Reinforcement Learning (RL)-based dynamic planning framework. We investigate NN-based RL planners tailored for dynamic opinion networks governed by two propagation models for the framework. Each model incorporates both binary and continuous opinion and trust representations. Our experimental results demonstrate that our ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets. Moreover, reward strategies focusing on key metrics, such as the number of susceptible nodes and infection rates, outperform those prioritizing faster blocking strategies. Additionally, our findings reveal that Graph Convolutional Networks (GCNs)-based planners facilitate scalable centralized plans that achieve lower infection rates (higher control) across various network scenarios (e.g., Watts-Strogatz topology, varying action budgets, varying initial infected nodes, and varying degree of infected nodes).

PLANTS: A Novel Problem and Dataset for Summarization of Planning-Like (PL) Tasks

Text summarization is a well-studied problem that deals with deriving insights from unstructured text consumed by humans, and it has found extensive business applications. However, many real-life tasks involve generating a series of actions to achieve specific goals, such as workflows, recipes, dialogs, and travel plans. We refer to them as planning-like (PL) tasks noting that the main commonality they share is control flow information. which may be partially specified. Their structure presents an opportunity to create more practical summaries to help users make quick decisions. We investigate this observation by introducing a novel plan summarization problem, presenting a dataset, and providing a baseline method for generating PL summaries. Using quantitative metrics and qualitative user studies to establish baselines, we evaluate the plan summaries from our method and large language models. We believe the novel problem and dataset can reinvigorate research in summarization, which some consider as a solved problem.

BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes

A common, yet regular, decision made by people, whether healthy or with any health condition, is to decide what to have in meals like breakfast, lunch, and dinner, consisting of a combination of foods for appetizer, main course, side dishes, desserts, and beverages. However, often this decision is seen as a trade-off between nutritious choices (e.g., low salt and sugar) or convenience (e.g., inexpensive, fast to prepare/obtain, taste better). In this preliminary work, we present a data-driven approach for the novel meal recommendation problem that can explore and balance choices for both considerations while also reasoning about a food's constituents and cooking process. Beyond the problem formulation, our contributions also include a goodness measure, a recipe conversion method from text to the recently introduced multimodal rich recipe representation (R3) format, and learning methods using contextual bandits that show promising results.

Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens

AI systems are notorious for their fragility; minor input changes can potentially cause major output swings. When such systems are deployed in critical areas like finance, the consequences of their uncertain behavior could be severe. In this paper, we focus on multi-modal time-series forecasting, where imprecision due to noisy or incorrect data can lead to erroneous predictions, impacting stakeholders such as analysts, investors, and traders. Recently, it has been shown that beyond numeric data, graphical transformations can be used with advanced visual models to achieve better performance. In this context, we introduce a rating methodology to assess the robustness of Multi-Modal Time-Series Forecasting Models (MM-TSFM) through causal analysis, which helps us understand and quantify the isolated impact of various attributes on the forecasting accuracy of MM-TSFM. We apply our novel rating method on a variety of numeric and multi-modal forecasting models in a large experimental setup (six input settings of control and perturbations, ten data distributions, time series from six leading stocks in three industries over a year of data, and five time-series forecasters) to draw insights on robust forecasting models and the context of their strengths. Within the scope of our study, our main result is that multi-modal (numeric + visual) forecasting, which was found to be more accurate than numeric forecasting in previous studies, can also be more robust in diverse settings. Our work will help different stakeholders of time-series forecasting understand the models` behaviors along trust (robustness) and accuracy dimensions to select an appropriate model for forecasting using our rating method, leading to improved decision-making.

The Case for Developing a Foundation Model for Planning-like Tasks from Scratch

Foundation Models (FMs) have revolutionized many areas of computing, including Automated Planning and Scheduling (APS). For example, a recent study found them useful for planning problems: plan generation, language translation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. Besides APS, there are many seemingly related tasks involving the generation of a series of actions with varying guarantees of their executability to achieve intended goals, which we collectively call planning-like (PL) tasks like business processes, programs, workflows, and guidelines, where researchers have considered using FMs. However, previous works have primarily focused on pre-trained, off-the-shelf FMs and optionally fine-tuned them. This paper discusses the need for a comprehensive FM for PL tasks from scratch and explores its design considerations. We argue that such an FM will open new and efficient avenues for PL problem-solving, just like LLMs are creating for APS.

From Cloud to Edge: Rethinking Generative AI for Low-Resource Design Challenges

Generative Artificial Intelligence (AI) has shown tremendous prospects in all aspects of technology, including design. However, due to its heavy demand on resources, it is usually trained on large computing infrastructure and often made available as a cloud-based service. In this position paper, we consider the potential, challenges, and promising approaches for generative AI for design on the edge, i.e., in resource-constrained settings where memory, compute, energy (battery) and network connectivity may be limited. Adapting generative AI for such settings involves overcoming significant hurdles, primarily in how to streamline complex models to function efficiently in low-resource environments. This necessitates innovative approaches in model compression, efficient algorithmic design, and perhaps even leveraging edge computing. The objective is to harness the power of generative AI in creating bespoke solutions for design problems, such as medical interventions, farm equipment maintenance, and educational material design, tailored to the unique constraints and needs of remote areas. These efforts could democratize access to advanced technology and foster sustainable development, ensuring universal accessibility and environmental consideration of AI-driven design benefits.

Trust and ethical considerations in a multi-modal, explainable AI-driven chatbot tutoring system: The case of collaboratively solving Rubik's Cube

Artificial intelligence (AI) has the potential to transform education with its power of uncovering insights from massive data about student learning patterns. However, ethical and trustworthy concerns of AI have been raised but are unsolved. Prominent ethical issues in high school AI education include data privacy, information leakage, abusive language, and fairness. This paper describes technological components that were built to address ethical and trustworthy concerns in a multi-modal collaborative platform (called ALLURE chatbot) for high school students to collaborate with AI to solve the Rubik's cube. In data privacy, we want to ensure that the informed consent of children, parents, and teachers, is at the center of any data that is managed. Since children are involved, language, whether textual, audio, or visual, is acceptable both from users and AI and the system can steer interaction away from dangerous situations. In information management, we also want to ensure that the system, while learning to improve over time, does not leak information about users from one group to another.

The Effect of Human v/s Synthetic Test Data and Round-tripping on Assessment of Sentiment Analysis Systems for Bias

Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that output polarity and emotional intensity when given a piece of text as input. Like other AIs, SASs are also known to have unstable behavior when subjected to changes in data which can make it problematic to trust out of concerns like bias when AI works with humans and data has protected attributes like gender, race, and age. Recently, an approach was introduced to assess SASs in a blackbox setting without training data or code, and rating them for bias using synthetic English data. We augment it by introducing two human-generated chatbot datasets and also consider a round-trip setting of translating the data from one language to the same through an intermediate language. We find that these settings show SASs performance in a more realistic light. Specifically, we find that rating SASs on the chatbot data showed more bias compared to the synthetic data, and round-tripping using Spanish and Danish as intermediate languages reduces the bias (up to 68% reduction) in human-generated data while, in synthetic data, it takes a surprising turn by increasing the bias! Our findings will help researchers and practitioners refine their SAS testing strategies and foster trust as SASs are considered part of more mission-critical applications for global use.