Planner evaluation in closed-loop simulation often uses rule-based traffic agents, whose simplistic and passive behavior can hide planner deficiencies and bias rankings. Widely used IDM agents simply follow a lead vehicle and cannot react to vehicles in adjacent lanes, hindering tests of complex interaction capabilities. We address this issue by integrating the state-of-the-art learned traffic agent model SMART into nuPlan. Thus, we are the first to evaluate planners under more realistic conditions and quantify how conclusions shift when narrowing the sim-to-real gap. Our analysis covers 14 recent planners and established baselines and shows that IDM-based simulation overestimates planning performance: nearly all scores deteriorate. In contrast, many planners interact better than previously assumed and even improve in multi-lane, interaction-heavy scenarios like lane changes or turns. Methods trained in closed-loop demonstrate the best and most stable driving performance. However, when reaching their limits in augmented edge-case scenarios, all learned planners degrade abruptly, whereas rule-based planners maintain reasonable basic behavior. Based on our results, we suggest SMART-reactive simulation as a new standard closed-loop benchmark in nuPlan and release the SMART agents as a drop-in alternative to IDM at https://github.com/shgd95/InteractiveClosedLoop.

Firm competition and collusion involve complex dynamics, particularly when considering communication among firms. Such issues can be modeled as problems of complex systems, traditionally approached through experiments involving human subjects or agent-based modeling methods. We propose an innovative framework called Smart Agent-Based Modeling (SABM), wherein smart agents, supported by GPT-4 technologies, represent firms, and interact with one another. We conducted a controlled experiment to study firm price competition and collusion behaviors under various conditions. SABM is more cost-effective and flexible compared to conducting experiments with human subjects. Smart agents possess an extensive knowledge base for decision-making and exhibit human-like strategic abilities, surpassing traditional ABM agents. Furthermore, smart agents can simulate human conversation and be personalized, making them ideal for studying complex situations involving communication. Our results demonstrate that, in the absence of communication, smart agents consistently reach tacit collusion, leading to prices converging at levels higher than the Bertrand equilibrium price but lower than monopoly or cartel prices. When communication is allowed, smart agents achieve a higher-level collusion with prices close to cartel prices. Collusion forms more quickly with communication, while price convergence is smoother without it. These results indicate that communication enhances trust between firms, encouraging frequent small price deviations to explore opportunities for a higher-level win-win situation and reducing the likelihood of triggering a price war. We also assigned different personas to firms to analyze behavioral differences and tested variant models under diverse market structures. The findings showcase the effectiveness and robustness of SABM and provide intriguing insights into competition and collusion.

AI models can benefit as much from soft data such as personal anecdotes as much as hard data. It's well known among data science circles that the more diverse your set of training data, the more accurate your model will be. This includes structured, unstructured, and semistructured data. However, not all data is treated equally, especially when it comes to unstructured data. Soft data such as collective memory and personal anecdotes can be challenging to access, but they can help build better decision-making systems.

Unmanned aerial vehicles (UAVs) can be deployed to monitor very large areas without the need for network infrastructure. UAVs communicate with each other during flight and exchange information with each other. However, such communication poses security challenges due to its dynamic topology. To solve these challenges, the proposed method uses two phases to counter malicious UAV attacks. In the first phase, we applied a number of rules and principles to detect malicious UAVs. In this phase, we try to identify and remove malicious UAVs according to the behavior of UAVs in the network in order to prevent sending fake information to the investigating UAVs. In the second phase, a mobile agent based on a three-step negotiation process is used to eliminate malicious UAVs. In this way, we use mobile agents to inform our normal neighbor UAVs so that they do not listen to the data generated by the malicious UAVs. Therefore, the mobile agent of each UAV uses reliable neighbors through a three-step negotiation process so that they do not listen to the traffic generated by the malicious UAVs. The NS-3 simulator was used to demonstrate the efficiency of the SAUAV method. The proposed method is more efficient than CST-UAS, CS-AVN, HVCR, and BSUM-based methods in detection rate, false positive rate, false negative rate, packet delivery rate, and residual energy.

Man-made brainpower (AI) will soon be at the core of each major technological framework on the planet to manage and get to your strategic information. Only a couple of uses are cyber and homeland security, anti-money laundering, payments, financial markets, biotech, healthcare, marketing, natural language processing (NLP), computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). Artificial Intelligence is turning into a significant staple of innovation, scarcely any individuals comprehend the advantages and weaknesses of AI and Machine Learning innovations. While machine intelligence is sure to assume a key role in the making of cutting edge frameworks in a wide assortment of industry areas sooner rather than later, it is especially applicable in quickly developing businesses, for example, ICT, manufacturing and transportation. Over the globe, mobile operators are preparing to deploy the fifth era of 3GPP mobile wireless networks (5G).

You may have read previously on this blog that Brighterion collaborated with PYMNTS.com to analyze how AI and ML are being used by financial institutions (FIs) across the U.S. We surveyed 200 financial executives from banks and credit unions with assets ranging from $1 billion to over $100 billion to get a feel for the industry as a whole. In a series of reports, we've presented analysis of the 12,000 collected data points. The top four use cases for learning systems were supporting banking services (79.1 percent), enhancing payments services (53.7 percent), customer life cycle management (46.2 percent) and credit underwriting (42.5). Banks also reported using machine learning for compliance and regulation, preventing internal fraud, merchant services, collections and supplier onboarding. These findings also revealed a gap: financial institutions aren't accessing true AI with these tools.

You may have read previously on this blog that Brighterion collaborated with PYMNTS.com to analyze how AI and ML are being used by financial institutions (FIs) across the U.S. We surveyed 200 financial executives from banks and credit unions with assets ranging from $1 billion to over $100 billion to get a feel for the industry as a whole. In a series of reports, we've presented analysis of the 12,000 collected data points. The top four use cases for learning systems were supporting banking services (79.1 percent), enhancing payments services (53.7 percent), customer life cycle management (46.2 percent) and credit underwriting (42.5). Banks also reported using machine learning for compliance and regulation, preventing internal fraud, merchant services, collections and supplier onboarding. These findings also revealed a gap: financial institutions aren't accessing true AI with these tools.

There are two fundamental aspects which define the relationship and overlap between AI and the Internet of Things, and they're quite different. The first is reasonably obvious. The Internet of Things enables the smart automation of many objects. With IoT, you are giving devices, vehicles, buildings the ability to host algorithms and perform functions which can only be driven by software. When you examine the tasks that these objects need to perform, the tasks frequently involve processes that are similar to the cognitive process.

Dr. Akli Adjaoute, CEO of Brighterion, wrote this AI-focused piece as part of our 2018 year-end eBook. On Dec. 3, 2018, the U.S. Treasury's FinCEN and Federal Banking agencies issued a joint statement encouraging innovative industry approaches to combating money laundering, terrorist financing and other illicit financial threats. As a result, anti-money laundering (AML) has been occupying the headlines as of late. The financial industry has paid $321 billion in fines just through the end of last year, as estimated by Boston Consulting Group. JPMorgan had to pay more than $2 billion in fines due to violation of the Bank Secrecy Act, tied in part to the infamous Bernie Madoff scheme.

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed.