Navigation in dynamic environments requires autonomous systems to reason about uncertainties in the behavior of other agents. In this paper, we introduce a unified framework that combines trajectory planning with multimodal predictions and active probing to enhance decision-making under uncertainty. We develop a novel risk metric that seamlessly integrates multimodal prediction uncertainties through mixture models. When these uncertainties follow a Gaussian mixture distribution, we prove that our risk metric admits a closed-form solution, and is always finite, thus ensuring analytical tractability. To reduce prediction ambiguity, we incorporate an active probing mechanism that strategically selects actions to improve its estimates of behavioral parameters of other agents, while simultaneously handling multimodal uncertainties. We extensively evaluate our framework in autonomous navigation scenarios using the MetaDrive simulation environment. Results demonstrate that our active probing approach successfully navigates complex traffic scenarios with uncertain predictions. Additionally, our framework shows robust performance across diverse traffic agent behavior models, indicating its broad applicability to real-world autonomous navigation challenges. Code and videos are available at https://darshangm.github.io/papers/active-probing-multimodal-predictions/.

The development of techniques that can be used to analyze and detect animal behavior is a crucial activity for the livestock sector, as it is possible to monitor the stress and animal welfare and contributes to decision making in the farm. Thus, the development of applications can assist breeders in making decisions to improve production performance and reduce costs, once the animal behavior is analyzed by humans and this can lead to susceptible errors and time consumption. Aggressiveness in pigs is an example of behavior that is studied to reduce its impact through animal classification and identification. However, this process is laborious and susceptible to errors, which can be reduced through automation by visually classifying videos captured in controlled environment. The captured videos can be used for training and, as a result, for classification through computer vision and artificial intelligence, employing neural network techniques. The main techniques utilized in this study are variants of transformers: STAM, TimeSformer, and ViViT, as well as techniques using convolutions, such as ResNet3D2, Resnet(2+1)D, and CnnLstm. These techniques were employed for pig video classification with the objective of identifying aggressive and non-aggressive behaviors. In this work, various techniques were compared to analyze the contribution of using transformers, in addition to the effectiveness of the convolution technique in video classification. The performance was evaluated using accuracy, precision, and recall. The TimerSformer technique showed the best results in video classification, with median accuracy of 0.729.

The rise of social media platforms has led to an increase in cyber-aggressive behavior, encompassing a broad spectrum of hostile behavior, including cyberbullying, online harassment, and the dissemination of offensive and hate speech. These behaviors have been associated with significant societal consequences, ranging from online anonymity to real-world outcomes such as depression, suicidal tendencies, and, in some instances, offline violence. Recognizing the societal risks associated with unchecked aggressive content, this paper delves into the field of Aggression Content Detection and Behavioral Analysis of Aggressive Users, aiming to bridge the gap between disparate studies. In this paper, we analyzed the diversity of definitions and proposed a unified cyber-aggression definition. We examine the comprehensive process of Aggression Content Detection, spanning from dataset creation, feature selection and extraction, and detection algorithm development. Further, we review studies on Behavioral Analysis of Aggression that explore the influencing factors, consequences, and patterns associated with cyber-aggressive behavior. This systematic literature review is a cross-examination of content detection and behavioral analysis in the realm of cyber-aggression. The integrated investigation reveals the effectiveness of incorporating sociological insights into computational techniques for preventing cyber-aggressive behavior. Finally, the paper concludes by identifying research gaps and encouraging further progress in the unified domain of socio-computational aggressive behavior analysis.

If you've spent any time playing Dead Island 2, chances are you've noticed the game's progressive damage system. The Fully Locational Evisceration System for Humanoids, or FLESH, as developer Dambuster Studios call it, is a procedural tool that makes dismembering, melting, or burning zombies look more realistic, as signs of trauma correspond to the attacks you perform, visibly chewing through skin, muscle, organs, and bone. Of course, Dead Island 2 applies all this gore to schlocky, slapstick effect. But FLESH may make you wonder how such gruesome detail might translate to games with more serious themes. Questions around violence in games have a long history, spanning tabloid moral panics to concerted academic research.

Artificial intelligence (AI) machine learning is rapidly being deployed to help accelerate neuroscience, psychology, and psychiatry research. A new study published in Molecular Psychiatry by researchers affiliated with Yale University shows how AI machine learning can identify patterns of neural connections in the brain associated with aggressive behavior in children. According to the Yale researchers, this study is a first of its kind. "Disruptions in frontoparietal networks supporting emotion regulation have been long implicated in maladaptive childhood aggression," wrote the researchers. "However, the association of connectivity between large-scale functional networks with aggressive behavior has not been tested."

This is the second part of an article discussing new areas of game theory that are influencing deep reinforcement learning systems. The first part focused on types of games that we are actively seeing in multi-agent reinforcement learning systems. Today, I would like to cover three new areas of deep learning theory that can influence new generations of reinforcement learning systems. Game theory plays a fundamental factor in modern artificial intelligence(AI) solutions. Specifically, deep reinforcement learning(DRL) is an area of AI that embraced game theory as a first-class citize.

Game theory plays a fundamental factor in modern artificial intelligence(AI) solutions. Specifically, deep reinforcement learning(DRL) is an area of AI that embraced game theory as a first-class citize. From single-agent programs to complex multi-agent DRL environments, gamifying dynamics are present across the lifecycle of AI programs. The fascinating thing is that the rapid evolution of DRL has also triggered a renewed interesting in game theory research. The relationship between game theory and DRL seems trivial.

In March, a gunman walked into two mosques in Christchurch, New Zealand, opened fire, and killed dozens of worshippers. According to a police official, the suspected gunman was arrested 36 minutes after police were called to the scene. Now, a tech company believes its smart security cameras can prevent attacks like the tragedy in Christchurch, and says it plans to install its AI-powered systems in mosques around the world. Athena Security, the tech company behind the security system, and Al-Ameri International Trading announced the Keep Mosques Safe initiative last week. Al-Ameri International Trading, along with several Islamic non-profit groups, will fund the Keep Mosques Safe effort.

Here are 10 games based on real life locations that not only offer a great gaming experience, but inspire travel as well! Electronic Arts shares dropped on Thursday after the company announced that it would be updating its outlook as well as its "Battlefield V" launch date. The latest in the long-standing debate over violent video games: They do cause players to become more physically aggressive. An international study looking at more than 17,000 adolescents, ages nine to 19, from 2010 to 2017, found playing violent video games led to increased physical aggression over time. The analysis of 24 studies from countries including the U.S., Canada, Germany and Japan found those who played violent games such as "Grand Theft Auto," "Call of Duty" and "Manhunt" were more likely to exhibit behavior such as being sent to the principal's office for fighting or hitting a non-family member.

Public debate on the effects of violent video games can become especially contentious in the wake of a rampage shooting, such as the recent killing of nine people in Munich. If it is later discovered the perpetrator was a fan of violent video games, as was the Munich killer, it is tempting to think that perhaps violent games'caused' the rampage shooting. But rampage shootings are rare and complex events caused by multiple factors acting together. One can't accurately predict a rampage shooting based on exposure to violent video games or any other single factor. Laboratory experiments are used to make firm and causal conclusions about violent video game effects.