This paper introduces a method for designing spatially intelligent robot swarm behaviors to localize concealed radio emitters. We use differential evolution to generate geometric patrol routes that localize unknown signals independently of emitter parameters, a key challenge in electromagnetic surveillance. Patrol shape and antenna type are shown to influence information gain, which in turn determines the effective triangulation coverage. We simulate a four-robot swarm across eight configurations, assigning pre-generated patrol routes based on a specified patrol shape and sensing capability (antenna type: omnidirectional or directional). An emitter is placed within the map for each trial, with randomized position, transmission power and frequency. Results show that omnidirectional localization success rates are driven primarily by source location rather than signal properties, with failures occurring most often when sources are placed in peripheral areas of the map. Directional antennas are able to overcome this limitation due to their higher gain and directivity, with an average detection success rate of 98.75% compared to 80.25% for omnidirectional. Average localization errors range from 1.01-1.30 m for directional sensing and 1.67-1.90 m for omnidirectional sensing; while directional sensing also benefits from shorter patrol edges. These results demonstrate that a swarm's ability to predict electromagnetic phenomena is directly dependent on its physical interaction with the environment. Consequently, spatial intelligence, realized here through optimized patrol routes and antenna selection, is a critical design consideration for effective robotic surveillance.

Individual differences in learning behavior within social groups, whether in humans, other animals, or among robots, can have significant effects on collective task performance. This is because it can affect individuals' response to the environment and their interactions with each other. In recent years there has been rising interest in the question of how individual differences, whether in learning or other traits, affect collective outcomes: studied, for example, in social insect foraging behavior. Multi-robot, 'swarm' systems have a heritage of bioinspiration from such examples, and here we consider whether heterogeneity in a learning behavior called latent inhibition (LI) may be useful for a team of patrolling robots tasked with environmental monitoring and anomaly detection. Individuals with high LI can be seen as better at learning to be inattentive to irrelevant or unrewarding stimuli, while low LI individuals might be seen as 'distractible' and yet, more positively, more exploratory. We introduce a simple model of the effects of LI as the probability of re-searching a location for a reward (anomalous reading) where it has previously been found to be unrewarding (irrelevant). In simulated patrols, we find that a negatively skewed distribution of mostly high LI robots, and just a single low LI robot, is collectively most effective at monitoring dynamic environments. These results are an example of 'functional heterogeneity' in 'swarm engineering' and could inform predictions for ecological distributions of learning traits within social groups.

When it comes to predicting when and where crimes will occur, a growing number of communities in Japan are turning to artificial intelligence. Some municipalities have already started to fine tune citizen patrol routes based on data gathered by AI in a bid to prevent crime, while other communities are considering similar steps. Nagoya began to use such a system last July, after a successful test resulted in the detection of crimes. The system currently in use was developed by Tokyo-based Singular Perturbations Inc., a company founded by experts in mathematics and statistics. It allows citizens on patrol to download an app with recommended patrol routes, which are generated following an AI analysis.

A well-crafted police patrol route design is vital in providing community safety and security in the society. Previous works have largely focused on predicting crime events with historical crime data. The usage of large-scale mobility data collected from Location-Based Social Network, or check-ins, and Point of Interests (POI) data for designing an effective police patrol is largely understudied. Given that there are multiple police officers being on duty in a real-life situation, this makes the problem more complex to solve. In this paper, we formulate the dynamic crime patrol planning problem for multiple police officers using check-ins, crime, incident response data, and POI information. We propose a joint learning and non-random optimisation method for the representation of possible solutions where multiple police officers patrol the high crime risk areas simultaneously first rather than the low crime risk areas. Later, meta-heuristic Genetic Algorithm (GA) and Cuckoo Search (CS) are implemented to find the optimal routes. The performance of the proposed solution is verified and compared with several state-of-art methods using real-world datasets.

The illegal animal trade is a global scourge but a lucrative one, worth $8 to 10 billion annually, according to the United Nations Office on Drugs and Crime (UNODC) -- trailing only human, drug and weapons trafficking in value. With so much money to be made, conservationists and wildlife rangers face overwhelming odds against well-organized poaching operations fueled by incessant demand for illicit animal products. The results of this protracted conflict have been nothing short of devastating for the species caught in the middle. At the start of the 20th century, more than 100,000 tigers are estimated to have roamed throughout Southeast Asia. Today, due to a combination of habitat loss and aggressive poaching, fewer than 4,000 currently remain in the wild.

"The most challenging problems AI may help us solve--from fighting terrorists to serving vulnerable populations--will involve government," according to "The Future Has Begun," a report on the impact of AI on government by the Partnership for Public Service and the IBM Center for the Business of Government. "More immediately, though not less consequentially, AI will change the way public servants do their jobs." A decade-long collaboration between the University of Southern California and Los Angeles International Airport produced an AI-enabled system aimed at helping law-enforcement units deploy their limited staff more effectively. After analyzing potential targets, the system recommends randomized police patrol routes and schedules so terrorists can't anticipate where and when they will run into security checkpoints. The system has since been used by the U.S. Coast Guard to randomize boat patrol routes in major ports and by the Transportation Security Administration to assign air marshals to flights. More recently, another version of the AI system has been developed to help rangers fight wildlife poachers around the world.

The explosion of artificial intelligence (AI) is not just a boon for business. It is also helping solve some of the world's biggest social problems, from reducing crime to eradicating disease and tackling climate change. The amount of available data and technology that can process it intelligently has snowballed as the internet has increasingly integrated with our lives through tablets, phones and wearables. The advent of the internet of things – the extension of internet connectivity into everyday objects – has taken this even further. These advances have enabled a wide range of bodies, including companies, governments and non-governmental organisations, to start working together to use AI for social good and has already produced some groundbreaking results in vital areas.

Algorithms are a new tool in the fight against the trade of black market ivory tusks, pangolin scales and tiger skins. A group of researchers at the University of Southern California is working on technology to help rangers stay a step ahead of poachers. The Teamcore lab at USC's Center for Artificial Intelligence in Society is working on an AI-driven application called PAWS, short for Protection Assistant for Wildlife Security, which aims to equip wildlife defenders with optimized patrol routes. The illegal wildlife trade is considered the fourth most profitable criminal enterprise in the world, after drugs, weapons and human trafficking, U.K. Foreign Secretary Jeremy Hunt announced at the 2018 Wildlife Illegal Trade Conference in mid-October. Poaching continues to threaten the survival of species around the globe.

There are many contributing factors; disease, urbanisation, and climate change to name a few. All of these are synonymous with humans and the devastation we're causing to the planet. Poaching is playing a huge part in this. According to a recent article in WWF magazine's Spring Edition, the illegal ivory trade is the highest it's been for 20 years. A staggering 20,000 African elephants are killed each year, that's 55 a day.

The newest member of Yelp's security team awakes just after 8 pm, ready to begin its rounds. It traverses the lobby, gliding over polished concrete toward a small recess in the corner, where it inspects the emergency exit tucked inside. Last year, burglars tried to breach the office by rending the door from the building, frame and all. The low-resolution camera mounted in the lobby saw nothing. "We couldn't see what was going on inside the alcove," says Rick Lee, Yelp's head of security, who joined me on my late-night visit to one of the company's San Francisco offices.