There is significant concern about the impact of generative AI on society. Modern AI tools are capable of generating ever more realistic text, images, and videos, and functional code, from minimal prompts. Accompanying this rise in ability and usability, there is increasing alarm about the misuses to which these tools can be put, and the intentional and unintentional harms to individuals and society that may result. In this paper, we argue that \emph{agency} is the appropriate lens to study these harms and benefits, but that doing so will require advancement in the theory of agency, and advancement in how this theory is applied in (agent-based) models.

Considering the difficulty of interpreting generative model output, there is significant current research focused on determining meaningful evaluation metrics. Several recent approaches utilize "precision" and "recall," borrowed from the classification domain, to individually quantify the output fidelity (realism) and output diversity (representation of the real data variation), respectively. With the increase in metric proposals, there is a need for a unifying perspective, allowing for easier comparison and clearer explanation of their benefits and drawbacks. To this end, we unify a class of kth-nearest-neighbors (kNN)-based metrics under an information-theoretic lens using approaches from kNN density estimation. Additionally, we propose a tri-dimensional metric composed of Precision Cross-Entropy (PCE), Recall Cross-Entropy (RCE), and Recall Entropy (RE), which separately measure fidelity and two distinct aspects of diversity, inter- and intra-class. Our domain-agnostic metric, derived from the information-theoretic concepts of entropy and cross-entropy, can be dissected for both sample- and mode-level analysis. Our detailed experimental results demonstrate the sensitivity of our metric components to their respective qualities and reveal undesirable behaviors of other metrics.

Optimal allocation of agricultural water in the event of droughts is an important global problem. In addressing this problem, many aspects, including the welfare of farmers, the economy, and the environment, must be considered. Under this backdrop, our work focuses on several resource-matching problems accounting for agents with multi-crop portfolios, geographic constraints, and fairness. First, we address a matching problem where the goal is to maximize a welfare function in two-sided markets where buyers' requirements and sellers' supplies are represented by value functions that assign prices (or costs) to specified volumes of water. For the setting where the value functions satisfy certain monotonicity properties, we present an efficient algorithm that maximizes a social welfare function. When there are minimum water requirement constraints, we present a randomized algorithm which ensures that the constraints are satisfied in expectation. For a single seller--multiple buyers setting with fairness constraints, we design an efficient algorithm that maximizes the minimum level of satisfaction of any buyer. We also present computational complexity results that highlight the limits on the generalizability of our results. We evaluate the algorithms developed in our work with experiments on both real-world and synthetic data sets with respect to drought severity, value functions, and seniority of agents.

Evacuation planning is a crucial part of disaster management. However, joint optimization of its two essential components, routing and scheduling, with objectives such as minimizing average evacuation time or evacuation completion time, is a computationally hard problem. To approach it, we present MIP-LNS, a scalable optimization method that utilizes heuristic search with mathematical optimization and can optimize a variety of objective functions. We also present the method MIP-LNS-SIM, where we combine agent-based simulation with MIP-LNS to estimate delays due to congestion, as well as, find optimized plans considering such delays. We use Harris County in Houston, Texas, as our study area. We show that, within a given time limit, MIP-LNS finds better solutions than existing methods in terms of three different metrics. However, when congestion dependent delay is considered, MIP-LNS-SIM outperforms MIP-LNS in multiple performance metrics. In addition, MIP-LNS-SIM has a significantly lower percent error in estimated evacuation completion time compared to MIP-LNS.

Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States.

Abstract-- We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature. The synthetic networks are generated by integrating data about human populations from several sources, including the Census, transportation surveys, and geographical data. The resulting networks represent an approximation of daily or weekly human interaction. Our results indicate that the synthetically generated graphs according to our methodology are closer to the real world graphs, as measured across multiple structural measures, than a range of stylized graphs generated using common network models from the literature. I. INTRODUCTION Artificially generated graphs benefit from high demand in several application domains, wherever the phenomena of interest are driven by interactions between people, including health and medicine, communications, the economy, and national security. Lack of access to appropriate network data hampers the research community's ability to develop algorithms toanalyze and gain insight from these transactional graph datasets. Due to the access restrictions to real network data, there is value in crafting methods of synthetically generated data which faithfully represent behaviors of real world processes. As such, many stylized methods for creating graphs with rigorously understood structural properties have been established, making collective steady progress towards better approximating structures of real world processes. Despite this progress, these relatively simple stylized methods aren't universally applicable and suffer from lack of realism for some applications. We are particularly interested in creating realistic graphs which represent a complex set of interrelated processes involving a common subset of actors (i.e., the coherent alignment of disparate subgraphs which have many vertices in common and which represent different types of underlying activity).

The Association for the Advancement of Artificial Intelligence was pleased to present the 2013 Fall Symposium Series, held Friday through Sunday, November 15–17, at the Westin Arlington Gateway in Arlington, Virginia near Washington DC USA. The titles of the five symposia were as follows: Discovery Informatics: AI Takes a Science-Centered View on Big Data (FS-13-01); How Should Intelligence be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or --? The highlights of each symposium are presented in this report.

Rinke Hoekstra (VU University from transferring and adapting semantic web Amsterdam) presented linked open data tools technologies to the big data quest. Finally, in the Social to discover connections within established scientific Networks and Social Contagion symposium, a data sets. Louiqa Rashid (University of Maryland) community of researchers explored topics such as social presented work on similarity metrics linking together contagion, game theory, network modeling, network-based drugs, genes, and diseases. Kyle Ambert (Intel) presented inference, human data elicitation, and Finna, a text-mining system to identify passages web analytics. Highlights of the symposia are contained of interest containing descriptions of neuronal in this report.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2012 Fall Symposium Series, held Friday through Sunday, November 2–4, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia were as follows: AI for Gerontechnology (FS-12-01), Artificial Intelligence of Humor (FS-12-02), Discovery Informatics: The Role of AI Research in Innovating Scientific Processes (FS-12-03), Human Control of Bio-Inspired Swarms (FS-12-04), Information Retrieval and Knowledge Discovery in Biomedical Text (FS-12-05), Machine Aggregation of Human Judgment (FS-12-06), Robots Learning Interactively from Human Teachers (FS-12-07), and Social Networks and Social Contagion (FS-12-08). The highlights of each symposium are presented in this report.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2012 Fall Symposium Series, held Friday through Sunday, November 2–4, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia were as follows: AI for Gerontechnology (FS-12-01), Artificial Intelligence of Humor (FS-12-02), Discovery Informatics: The Role of AI Research in Innovating Scientific Processes (FS-12-03), Human Control of Bio-Inspired Swarms (FS-12-04), Information Retrieval and Knowledge Discovery in Biomedical Text (FS-12-05), Machine Aggregation of Human Judgment (FS-12-06), Robots Learning Interactively from Human Teachers (FS-12-07), and Social Networks and Social Contagion (FS-12-08). The highlights of each symposium are presented in this report.