As more autonomous vehicles operate on public roads, understanding real-world behavior of autonomous vehicles is critical to analyzing traffic safety, making policies, and public acceptance. This paper proposes SVBRD-LLM, a framework that automatically discovers, verifies, and applies interpretable behavioral rules from real traffic videos through zero-shot prompt engineering. The framework extracts vehicle trajectories using YOLOv8 and ByteTrack, computes kinematic features, and employs GPT-5 zero-shot prompting to compare autonomous and human-driven vehicles, generating 35 structured behavioral rule hypotheses. These rules are tested on a validation set, iteratively refined based on failure cases to filter spurious correlations, and compiled into a high-confidence rule library. The framework is evaluated on an independent test set for speed change prediction, lane change prediction, and autonomous vehicle identification tasks. Experiments on over 1500 hours of real traffic videos show that the framework achieves 90.0% accuracy and 93.3% F1-score in autonomous vehicle identification. The discovered rules clearly reveal distinctive characteristics of autonomous vehicles in speed control smoothness, lane change conservativeness, and acceleration stability, with each rule accompanied by semantic description, applicable context, and validation confidence.

We present BehAV, a novel approach for autonomous robot navigation in outdoor scenes guided by human instructions and leveraging Vision Language Models (VLMs). Our method interprets human commands using a Large Language Model (LLM) and categorizes the instructions into navigation and behavioral guidelines. Navigation guidelines consist of directional commands (e.g., "move forward until") and associated landmarks (e.g., "the building with blue windows"), while behavioral guidelines encompass regulatory actions (e.g., "stay on") and their corresponding objects (e.g., "pavements"). We use VLMs for their zero-shot scene understanding capabilities to estimate landmark locations from RGB images for robot navigation. Further, we introduce a novel scene representation that utilizes VLMs to ground behavioral rules into a behavioral cost map. This cost map encodes the presence of behavioral objects within the scene and assigns costs based on their regulatory actions. The behavioral cost map is integrated with a LiDAR-based occupancy map for navigation. To navigate outdoor scenes while adhering to the instructed behaviors, we present an unconstrained Model Predictive Control (MPC)-based planner that prioritizes both reaching landmarks and following behavioral guidelines. We evaluate the performance of BehAV on a quadruped robot across diverse real-world scenarios, demonstrating a 22.49% improvement in alignment with human-teleoperated actions, as measured by Frechet distance, and achieving a 40% higher navigation success rate compared to state-of-the-art methods.

This paper presents a novel zone-based flocking control approach suitable for dynamic multi-agent systems (MAS). Inspired by Reynolds behavioral rules for $boids$, flocking behavioral rules with the zones of repulsion, conflict, attraction, and surveillance are introduced. For each agent, using only bearing and distance measurements, behavioral deviation vectors quantify the deviations from the local separation, local and global flock velocity alignment, local cohesion, obstacle avoidance and boundary conditions, and strategic separation for avoiding alien agents. The control strategy uses the local perception-based behavioral deviation vectors to guide each agent's motion. Additionally, the control strategy incorporates a directionally-aware obstacle avoidance mechanism that prioritizes obstacles in the agent's forward path. Simulation results validate the effectiveness of this approach in creating flexible, adaptable, and scalable flocking behavior.

The evolution of cooperation has been investigated intensely in various disciplines, such as biology, economics, computer science, physics and psychology. There are two important dimensions, among many (Bowles and Gintis, 2011; Lehmann and Keller, 2006; Nowak, 2006), that have been shown to affect the evolution of cooperation: the interaction structure, i.e., who interacts with whom (Santos et al., 2006), and the mode of cognition, i.e., the extent of deliberation as opposed to intuition (Capraro, 2019). While for the interaction structure there is a substantial consensus that sparse and heavily clustered networks help the spread of cooperation (Nowak, 2006; Ohtsuki et al., 2006), for the mode of cognition results are more articulated and depend on specific features of the social dilemma (Bear et al., 2017; Bear and Rand, 2016) and of the cost of deliberation (Jagau and van Veelen, 2017). An important aspect in evolutionary models is the behavioral rule adopted by agents, which heavily contributes to determining the trajectories of the dynamic adjustment. While the literature has extensively considered behavioral rules encompassing best reply (Bilancini and Boncinelli, 2009) and imitation (Levine and Pesendorfer, 2007) as well as processes of the type death-birth or birth-death (Ohtsuki et al., 2006), little attention has been given to evolutionary dynamics based on reinforcement learning (Tanabe and Masuda, 2012). Reinforcement learning is a prominent behavioral rule originated in behavioral sciences (Skinner, 1938a,b) and recently become extremely popular in computer sciences, with many different applications (Nian et al., 2020).

The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual's dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ``CalBehav''. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.

These devices, particularly the smart mobile phones have transformed over a period of time from merely communication tools to smart and highly personal devices enabling to assist the users in their variety of day-to-day situations in their daily life. In the real word, users' interest on "Mobile Phones" is more and more than other platforms like "Desktop Computer" or "Tablet Computer" over time [36]. People use mobile phones not only for voice communication between individuals but also for various activities such as applications (mobile apps) using, Internet browsing, emailing, using online social network, instant messaging etc [28]. Recent advances in the sensing capabilities of smart mobile phones make them enable to collect the rich contextual information and users' various activity records with mobile phones through the device logs. These historical mobile phone data are simply as the collection of the past contexts and user's activities with the mobile phones for these past contexts. These are phone call logs [39] having phone call activities, app usages logs [45] having various mobile application usages, mobile phone notification logs [22] having the responses with various notifications from different applications, web logs [13] having Internet browsing activities of the mobile phone users. The main characteristic of such kind of phone log data is that it contains the actual diverse activities of the users in different contexts in their real world life. Modeling smartphone user behaviors by developing various computational machine learning methods (rule-based learning) in order to analyze different behavioral patterns in different contexts, and eventually predict the next behaviors or detect strange behaviors utilizing such mobile phone data, can be used for build- 2 Iqbal H. Sarker*