Misinformation spans various domains, but detection methods trained on specific domains often perform poorly when applied to others. With the rapid development of Large Language Models (LLMs), researchers have begun to utilize LLMs for cross-domain misinformation detection. However, existing LLM-based methods often fail to adequately analyze news in the target domain, limiting their detection capabilities. More importantly, these methods typically rely on manually designed decision rules, which are limited by domain knowledge and expert experience, thus limiting the generalizability of decision rules to different domains. To address these issues, we propose a MultiAgent Framework for cross-domain misinformation detection with Automated Decision Rule Optimization (MARO). Under this framework, we first employs multiple expert agents to analyze target-domain news. Subsequently, we introduce a question-reflection mechanism that guides expert agents to facilitate higherquality analysis. Furthermore, we propose a decision rule optimization approach based on carefully-designed cross-domain validation tasks to iteratively enhance the effectiveness of decision rules in different domains. Experimental results and in-depth analysis on commonlyused datasets demonstrate that MARO achieves significant improvements over existing methods.

It is commonly perceived that online fake news and reliable news exhibit stark differences in writing styles, such as the use of sensationalist versus objective language. However, we emphasize that style-related features can also be exploited for style-based attacks. Notably, the rise of powerful Large Language Models (LLMs) has enabled malicious users to mimic the style of trustworthy news outlets at minimal cost. Our analysis reveals that LLM-camouflaged fake news content leads to substantial performance degradation of state-of-the-art text-based detectors (up to 38% decrease in F1 Score), posing a significant challenge for automated detection in online ecosystems. To address this, we introduce SheepDog, a style-agnostic fake news detector robust to news writing styles. SheepDog achieves this adaptability through LLM-empowered news reframing, which customizes each article to match different writing styles using style-oriented reframing prompts. By employing style-agnostic training, SheepDog enhances its resilience to stylistic variations by maximizing prediction consistency across these diverse reframings. Furthermore, SheepDog extracts content-focused veracity attributions from LLMs, where the news content is evaluated against a set of fact-checking rationales. These attributions provide supplementary information and potential interpretability that assist veracity prediction. On three benchmark datasets, empirical results show that SheepDog consistently yields significant improvements over competitive baselines and enhances robustness against LLM-empowered style attacks.

Robotic shepherding is a bio-inspired approach to autonomously guiding a swarm of agents towards a desired location. The research area has earned increasing research interest recently due to the efficacy of controlling a large number of agents in a swarm (sheep) using a smaller number of actuators (sheepdogs). However, shepherding a highly dispersed swarm in an obstacle-cluttered environment remains challenging for existing methods. To improve the efficacy of shepherding in complex environments with obstacles and dispersed sheep, this paper proposes a planning-assisted context-sensitive autonomous shepherding framework with collision avoidance abilities. The proposed approach models the swarm shepherding problem as a single Travelling Salesperson Problem (TSP), with two sheepdogs\textquoteright\ modes: no-interaction and interaction. An adaptive switching approach is integrated into the framework to guide real-time path planning for avoiding collisions with static and dynamic obstacles; the latter representing moving sheep swarms. We then propose an overarching hierarchical mission planning system, which is made of three sub-systems: a clustering approach to group and distinguish sheep sub-swarms, an Ant Colony Optimisation algorithm as a TSP solver for determining the optimal herding sequence of the sub-swarms, and an online path planner for calculating optimal paths for both sheepdogs and sheep. The experiments on various environments, both with and without obstacles, objectively demonstrate the effectiveness of the proposed shepherding framework and planning approaches.

Jingulu--a language spoken by the Jingili people in the Northern Territory--has characteristics that allow it to be easily translated into AI commands. An Aboriginal language could hold the key to solving some of the most challenging communication problems between humans and artificial intelligence (AI) systems. A new paper, published by Frontiers in Physics and led by UNSW Canberra's Professor Hussein Abbass, explains how Jingulu--a language spoken by the Jingili people in the Northern Territory--has characteristics that allow it to be easily translated into AI commands. "The Aboriginal people have a long history of contributions to the defense of Australia," Professor Abbass said. "During the Second World War their languages were used for secret communications. Today we are discovering that the wealth and richness of the Aboriginal languages and culture could hold the secret in human-AI interaction."

Bi-directional communication between humans and swarm systems begs for efficient languages to communicate information between the humans and the Artificial Intelligence (AI)-enabled agents in a manner that is most appropriate for the context. We discuss the criteria for effective teaming and functional bi-directional communication between humans and AI, and the design choices required to create effective languages. We then present a human-AI-teaming communication language inspired by the Australian Aboriginal language of Jingulu, which we call JSwarm. We present the motivation and structure of the language. An example is used to demonstrate how the language operates for a shepherding swarm guidance task.

Jingulu – a language spoken by the Jingili people in the Northern Territory – has characteristics that allow it to be easily translated into AI commands. A new language inspired by Jingulu could be applied to any situation where communication between humans and a large number of AI agents is required. An Aboriginal language could hold the key to solving some of the most challenging communication problems between humans and artificial intelligence (AI) systems. A new paper, published by Frontiers in Physics and led by UNSW Canberra's Professor Hussein Abbass, explains how Jingulu – a language spoken by the Jingili people in the Northern Territory – has characteristics that allow it to be easily translated into AI commands. "The Aboriginal people have a long history of contributions to the defence of Australia," Professor Abbass said.

In computational In this paper, we present an evolutionary path planning intelligence research, the concept is used more broadly to approach for shepherding that takes into account the collection model and analyze the behaviour of biologically inspired and movement of the swarm (sheep) in addition to the swarms, where multiple agents of different type interact with sheepdog. The problem is different from conventional path each other in a proactive and reactive manner. The reactive planning for robot navigation in the sense that the control agents are analogous to the sheep in the problem; they respond agents (sheepdog) have access to global information when to the presence of the proactive agent, the sheepdog, and are seeking an optimal path, while the movement of others (sheep) repulsed from it. The sheepdog makes a sequence of decisions is purely reactive. The two-phase algorithm starts by identifying to influence the sheep and to guide them towards a goal the path for the sheepdog to move from any initial position area. A recent comprehensive review on the subject can be to a position behind the swarm. The path is constrained to be found in [1]. The shepherding problem using robotic swarms obstacle free and so as not to impact the sheep; lest the sheep is of interest in several applications beyond the biological be repulsed and scatter, making their collection even harder inspiration of shepherding itself; applications include crowd and more time-consuming. In the second phase, the algorithm control [2], cleanup of oil spills [3], disaster relief and rescue plans the path for the sheepdog by identifying the next series operations [4], and security/military procedures [5], among of way points to guide the sheep towards their final destination.

This is the moment a robotic dog tries its metal paws at herding unruly sheep on a farm in New Zealand. Spot gathered together the animals before pushing them through the field, with the help of two biological sheepdogs. Developed by Boston Dynamics, it can reach speeds of up to 3mph and costs less than a car, which average £30,000, to lease, according to reports. It has been heralded as the future of farming. The robot was seen helping the dogs to keep the sheep together.

A shepherd is out tending a flock when a presence appears above. It descends from the sky and communicates vital information. It may sound like a nativity scene, but for an increasing number of farmers it's a daily occurrence – and that celestial being is a drone. Corey Lambeth, a New Zealand farmer, originally purchased a drone for photography, but he quickly realised the device had more practical applications. "I thought'I'll just give it a nudge on the sheep and see what that goes like' and it actually worked out quite well," he says.

The simultaneous control of multiple coordinated robotic agents represents an elaborate problem. If solved, however, the interaction between the agents can lead to solutions to sophisticated problems. The concept of swarming, inspired by nature, can be described as the emergence of complex system-level behaviors from the interactions of relatively elementary agents. Due to the effectiveness of solutions found in nature, bio-inspired swarming-based control techniques are receiving a lot of attention in robotics. One method, known as swarm shepherding, is founded on the sheep herding behavior exhibited by sheepdogs, where a swarm of relatively simple agents are governed by a shepherd (or shepherds) which is responsible for high-level guidance and planning. Many studies have been conducted on shepherding as a control technique, ranging from the replication of sheep herding via simulation, to the control of uninhabited vehicles and robots for a variety of applications. We present a comprehensive review of the literature on swarm shepherding to reveal the advantages and potential of the approach to be applied to a plethora of robotic systems in the future.