Automated creation of synthetic traffic scenarios is a key part of validating the safety of autonomous vehicles (AVs). In this paper, we propose Scenario Diffusion, a novel diffusion-based architecture for generating traffic scenarios that enables controllable scenario generation. We combine latent diffusion, object detection and trajectory regression to generate distributions of synthetic agent poses, orientations and trajectories simultaneously. To provide additional control over the generated scenario, this distribution is conditioned on a map and sets of tokens describing the desired scenario. We show that our approach has sufficient expressive capacity to model diverse traffic patterns and generalizes to different geographical regions.

We study principal-agent problems in which a principal commits to an outcome-dependent payment scheme -- called contract -- in order to induce an agent to take a costly, unobservable action leading to favorable outcomes. We consider a generalization of the classical (single-round) version of the problem in which the principal interacts with the agent by committing to contracts over multiple rounds. The principal has no information about the agent, and they have to learn an optimal contract by only observing the outcome realized at each round. We focus on settings in which the size of the agent's action space is small. We design an algorithm that learns an approximately-optimal contract with high probability in a number of rounds polynomial in the size of the outcome space, when the number of actions is constant. Our algorithm solves an open problem by Zhu et al.[2022]. Moreover, it can also be employed to provide a $\tilde{\mathcal{O}}(T^{4/5})$ regret bound in the related online learning setting in which the principal aims at maximizing their cumulative utility, thus considerably improving previously-known regret bounds.

Control of networked systems, comprised of interacting agents, is often achieved through modeling the underlying interactions. Constructing accurate models of such interactions--in the meantime--can become prohibitive in applications. Data-driven control methods avoid such complications by directly synthesizing a controller from the observed data. In this paper, we propose an algorithm referred to as Data-driven Structured Policy Iteration (D2SPI), for synthesizing an efficient feedback mechanism that respects the sparsity pattern induced by the underlying interaction network. In particular, our algorithm uses temporary "auxiliary" communication links in order to enable the required information exchange on a (smaller) sub-network during the "learning phase" -- links that will be removed subsequently for the final distributed feedback synthesis. We then proceed to show that the learned policy results in a stabilizing structured policy for the entire network. Our analysis is then followed by showing the stability and convergence of the proposed distributed policies throughout the learning phase, exploiting a construct referred to as the "Patterned monoid.'' The performance of D2SPI is then demonstrated using representative simulation scenarios.

This EPTCS volume contains the proceedings for the Fifth International Workshop on Formal Methods for Autonomous Systems (FMAS 2023), which was held on the 15th and 16th of November 2023. FMAS 2023 was co-located with 18th International Conference on integrated Formal Methods (iFM) (iFM'22), organised by Leiden Institute of Advanced Computer Science of Leiden University. The workshop itself was held at Scheltema Leiden, a renovated 19th Century blanket factory alongside the canal. FMAS 2023 received 25 submissions. We received 11 regular papers, 3 experience reports, 6 research previews, and 5 vision papers. The researchers who submitted papers to FMAS 2023 were from institutions in: Australia, Canada, Colombia, France, Germany, Ireland, Italy, the Netherlands, Sweden, the United Kingdom, and the United States of America. Increasing our number of submissions for the third year in a row is an encouraging sign that FMAS has established itself as a reputable publication venue for research on the formal modelling and verification of autonomous systems. After each paper was reviewed by three members of our Programme Committee we accepted a total of 15 papers: 8 long papers and 7 short papers.

Designing a safe, trusted, and ethical AI may be practically impossible; however, designing AI with safe, trusted, and ethical use in mind is possible and necessary in safety and mission-critical domains like aerospace. Safe, trusted, and ethical use of AI are often used interchangeably; however, a system can be safely used but not trusted or ethical, have a trusted use that is not safe or ethical, and have an ethical use that is not safe or trusted. This manuscript serves as a primer to illuminate the nuanced differences between these concepts, with a specific focus on applications of Human-AI teaming in aerospace system control, where humans may be in, on, or out-of-the-loop of decision-making.

Most existing chain-of-thought (CoT) prompting methods suffer from the issues of generalizability and consistency, as they often rely on instance-specific solutions that may not be applicable to other cases and lack task-level consistency in their reasoning steps. To address these limitations, we propose a comprehensive framework, StrategyLLM, harnessing the capabilities of LLMs to tackle various tasks. The framework improves generalizability by formulating general problem-solving strategies and enhances consistency by producing consistent solutions using these strategies. StrategyLLM employs four LLM-based agents: strategy generator, executor, optimizer, and evaluator, working together to generate, evaluate, and select promising strategies for a given task automatically. The experimental results demonstrate that StrategyLLM outperforms the competitive baseline CoT-SC that requires human-annotated solutions on 13 datasets across 4 challenging tasks without human involvement, including math reasoning (39.2% $\rightarrow$ 43.3%), commonsense reasoning (70.3% $\rightarrow$ 72.5%), algorithmic reasoning (51.7% $\rightarrow$ 62.0%), and symbolic reasoning (30.0% $\rightarrow$ 79.2%).

With the projected surge in the elderly population, service robots offer a promising avenue to enhance their well-being in elderly care homes. Such robots will encounter complex scenarios which will require them to perform decisions with ethical consequences. In this report, we propose to leverage the Intelligent Disobedience framework in order to give the robot the ability to perform a deliberation process over decisions with potential ethical implications. We list the issues that this framework can assist with, define it formally in the context of the specific elderly care home scenario, and delineate the requirements for implementing an intelligently disobeying robot. We conclude this report with some critical analysis and suggestions for future work.

Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents. For instance, existing work usually considers accuracy equity as fairness for different agents in FL, which is limited, especially under the heterogeneous setting, since it is intuitively "unfair" to enforce agents with high-quality data to achieve similar accuracy to those who contribute low-quality data, which may discourage the agents from participating in FL. In this work, we propose a formal FL fairness definition, fairness via agent-awareness (FAA), which takes different contributions of heterogeneous agents into account. Under FAA, the performance of agents with high-quality data will not be sacrificed just due to the existence of large amounts of agents with low-quality data. In addition, we propose a fair FL training algorithm based on agent clustering (FOCUS) to achieve fairness in FL measured by FAA. Theoretically, we prove the convergence and optimality of FOCUS under mild conditions for linear and general convex loss functions with bounded smoothness. We also prove that FOCUS always achieves higher fairness in terms of FAA compared with standard FedAvg under both linear and general convex loss functions. Empirically, we show that on four FL datasets, including synthetic data, images, and texts, FOCUS achieves significantly higher fairness in terms of FAA while maintaining competitive prediction accuracy compared with FedAvg and state-of-the-art fair FL algorithms.

This work addresses learning online fair division under uncertainty, where a central planner sequentially allocates items without precise knowledge of agents' values or utilities. Departing from conventional online algorithm, the planner here relies on noisy, estimated values obtained after allocating items. We introduce wrapper algorithms utilizing \textit{dual averaging}, enabling gradual learning of both the type distribution of arriving items and agents' values through bandit feedback. This approach enables the algorithms to asymptotically achieve optimal Nash social welfare in linear Fisher markets with agents having additive utilities. We establish regret bounds in Nash social welfare and empirically validate the superior performance of our proposed algorithms across synthetic and empirical datasets.

While the situation where all vehicles for overtaking trajectory generation with real time operation on the road are fully autonomous remains a long term capability but often lack safety guarantees. While these goal, it is likely that most initial CAVs introduced will methods have not been applied to incoming lane overtaking, need to operate side by side with human driven vehicles our previous work [10] explored the use of a mixed-integer (HDVs) resulting in a mixed traffic situation. This results model predictive control (MI-MPC) strategy for bidirectional in many additional challenges brought about by the lack overtaking for a single autonomous agent. of cooperation and unpredictability of human drivers [1]. The use of communication among CAVs in order to Overtaking on the incoming lane is a scenario where these improve the overall efficiency and safety of many complex issues play a significant role due to the increased possibility traffic conditions such as highway merging [11] and traffic of head on collisions.