Demonstrations are a powerful way of increasing the transparency of AI policies. Though informative demonstrations may be selected a priori through the machine teaching paradigm, student learning may deviate from the preselected curriculum in situ. This paper thus explores augmenting a curriculum with a closed-loop teaching framework inspired by principles from the education literature, such as the zone of proximal development and the testing effect. We utilize tests accordingly to close to the loop and maintain a novel particle filter model of human beliefs throughout the learning process, allowing us to provide demonstrations that are targeted to the human's current understanding in real time. A user study finds that our proposed closed-loop teaching framework reduces the regret in human test responses by 43% over a baseline.

Large Language Models (LLMs) have demonstrated impressive and diverse abilities that can benefit various domains, such as zero and few-shot information extraction from clinical text without domain-specific training. However, for the ICD coding task, they often hallucinate key details and produce high recall but low precision results due to the high-dimensional and skewed distribution of the ICD codes. Existing LLM-based methods fail to account for the complex and dynamic interactions among the human agents involved in coding, such as patients, physicians, and coders, and they lack interpretability and reliability. In this paper, we present a novel multi-agent method for ICD coding, which mimics the real-world coding process with five agents: a patient agent, a physician agent, a coder agent, a reviewer agent, and an adjuster agent. Each agent has a specific function and uses a LLM-based model to perform it. We evaluate our method on the MIMIC-III dataset and show that our proposed multi-agent coding framework substantially improves performance on both common and rare codes compared to Zero-shot Chain of Thought (CoT) prompting and self-consistency with CoT. The ablation study confirms the proposed agent roles' efficacy. Our method also matches the state-of-the-art ICD coding methods that require pre-training or fine-tuning, in terms of coding accuracy, rare code accuracy, and explainability.

Addressing complex cooperative tasks in safety-critical environments poses significant challenges for Multi-Agent Systems, especially under conditions of partial observability. This work introduces a hybrid approach that integrates Multi-Agent Reinforcement Learning with control-theoretic methods to ensure safe and efficient distributed strategies. Our contributions include a novel setpoint update algorithm that dynamically adjusts agents' positions to preserve safety conditions without compromising the mission's objectives. Through experimental validation, we demonstrate significant advantages over conventional MARL strategies, achieving comparable task performance with zero safety violations. Our findings indicate that integrating safe control with learning approaches not only enhances safety compliance but also achieves good performance in mission objectives.

With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments, where the learning tasks, data, and distributions can vary over time. Our survey provides a comprehensive review of continual learning methods that are widely used in smart city development. The content consists of three parts: 1) Methodology-wise. We categorize a large number of basic CL methods and advanced CL frameworks in combination with other learning paradigms including graph learning, spatial-temporal learning, multi-modal learning, and federated learning. 2) Application-wise. We present numerous CL applications covering transportation, environment, public health, safety, networks, and associated datasets related to urban computing. 3) Challenges. We discuss current problems and challenges and envision several promising research directions. We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends.

Agent-based simulation (ABS) models are potent tools for analyzing complex systems. However, understanding and validating ABS models can be a significant challenge. To address this challenge, cutting-edge data-driven techniques offer sophisticated capabilities for analyzing the outcomes of ABS models. One such technique is process mining, which encompasses a range of methods for discovering, monitoring, and enhancing processes by extracting knowledge from event logs. However, applying process mining to event logs derived from ABSs is not trivial, and deriving meaningful insights from the resulting process models adds an additional layer of complexity. Although process mining is invaluable in extracting insights from ABS models, there is a lack of comprehensive methodological guidance for its application in ABS evaluation in the research landscape. In this paper, we propose a methodology, based on the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, to assess ABS models using process mining techniques. We incorporate process mining techniques into the stages of the CRISP-DM methodology, facilitating the analysis of ABS model behaviors and their underlying processes. We demonstrate our methodology using an established agent-based model, Schelling model of segregation. Our results show that our proposed methodology can effectively assess ABS models through produced event logs, potentially paving the way for enhanced agent-based model validity and more insightful decision-making.

Autonomous assistance of people with motor impairments is one of the most promising applications of autonomous robotic systems. Recent studies have reported encouraging results using deep reinforcement learning (RL) in the healthcare domain. Previous studies showed that assistive tasks can be formulated as multi-agent RL, wherein there are two agents: a caregiver and a care-receiver. However, policies trained in multi-agent RL are often sensitive to the policies of other agents. In such a case, a trained caregiver's policy may not work for different care-receivers. To alleviate this issue, we propose a framework that learns a robust caregiver's policy by training it for diverse care-receiver responses. In our framework, diverse care-receiver responses are autonomously learned through trials and errors. In addition, to robustify the care-giver's policy, we propose a strategy for sampling a care-receiver's response in an adversarial manner during the training. We evaluated the proposed method using tasks in an Assistive Gym. We demonstrate that policies trained with a popular deep RL method are vulnerable to changes in policies of other agents and that the proposed framework improves the robustness against such changes.

This paper investigates the network-assisted full-duplex (NAFD) cell-free millimeter-wave (mmWave) networks, where the distribution of the transmitting access points (T-APs) and receiving access points (R-APs) across distinct geographical locations mitigates cross-link interference, facilitating the attainment of a truly flexible duplex mode. To curtail deployment expenses and power consumption for mmWave band operations, each AP incorporates a hybrid digital-analog structure encompassing precoder/combiner functions. However, this incorporation introduces processing intricacies within channel estimation and precoding/combining design. In this paper, we first present a hybrid multiple-input multiple-output (MIMO) processing framework and derive explicit expressions for both uplink and downlink achievable rates. Then we formulate a power allocation problem to maximize the weighted bidirectional sum rates. To tackle this non-convex problem, we develop a collaborative multi-agent deep reinforcement learning (MADRL) algorithm called multi-agent twin delayed deep deterministic policy gradient (MATD3) for NAFD cell-free mmWave networks. Specifically, given the tightly coupled nature of both uplink and downlink power coefficients in NAFD cell-free mmWave networks, the MATD3 algorithm resolves such coupled conflicts through an interactive learning process between agents and the environment. Finally, the simulation results validate the effectiveness of the proposed channel estimation methods within our hybrid MIMO processing paradigm, and demonstrate that our MATD3 algorithm outperforms both multi-agent deep deterministic policy gradient (MADDPG) and conventional power allocation strategies.

The evolution of Artificial Intelligence (AI) stands as a pivotal force shaping our society, finding applications across diverse domains such as education, sustainability, and safety. Leveraging AI within mobile applications makes it easily accessible to the public, catalyzing its transformative potential. In this paper, we present a methodology for the rapid development of AI agent applications using the development platform provided by MIT App Inventor. To demonstrate its efficacy, we share the development journey of three distinct mobile applications: SynchroNet for fostering sustainable communities; ProductiviTeams for addressing procrastination; and iHELP for enhancing community safety. All three applications seamlessly integrate a spectrum of generative AI features, leveraging OpenAI APIs. Furthermore, we offer insights gleaned from overcoming challenges in integrating diverse tools and AI functionalities, aiming to inspire young developers to join our efforts in building practical AI agent applications.

We formulate active perception for an autonomous agent that explores an unknown environment as a two-player zero-sum game: the agent aims to maximize information gained from the environment while the environment aims to minimize the information gained by the agent. In each episode, the environment reveals a set of actions with their potentially erroneous information gain. In order to select the best action, the robot needs to recover the true information gain from the erroneous one. The robot does so by minimizing the discrepancy between its estimate of information gain and the true information gain it observes after taking the action. We propose an online convex optimization algorithm that achieves sub-linear expected regret $O(T^{3/4})$ for estimating the information gain. We also provide a bound on the regret of active perception performed by any (near-)optimal prediction and trajectory selection algorithms. We evaluate this approach using semantic neural radiance fields (NeRFs) in simulated realistic 3D environments to show that the robot can discover up to 12% more objects using the improved estimate of the information gain. On the M3ED dataset, the proposed algorithm reduced the error of information gain prediction in occupancy map by over 67%. In real-world experiments using occupancy maps on a Jackal ground robot, we show that this approach can calculate complicated trajectories that efficiently explore all occluded regions.

Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safety issues as various vulnerabilities are exposed. Faced with the problem, the field of red teaming is experiencing fast-paced growth, which highlights the need for a comprehensive organization covering the entire pipeline and addressing emerging topics for the community. Our extensive survey, which examines over 120 papers, introduces a taxonomy of fine-grained attack strategies grounded in the inherent capabilities of language models. Additionally, we have developed the searcher framework that unifies various automatic red teaming approaches. Moreover, our survey covers novel areas including multimodal attacks and defenses, risks around multilingual models, overkill of harmless queries, and safety of downstream applications. We hope this survey can provide a systematic perspective on the field and unlock new areas of research. Warning: This paper contains examples that may be offensive, harmful, or biased.