Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilitates an understanding of complicated phenomena that cannot be solved analytically. Agent-Based Modeling and Simulation with Swarm provides the methodology for a multi-agent-based modeling approach that integrates computational techniques such as artificial life, cellular automata, and bio-inspired optimization. Each chapter gives an overview of the problem, explores state-of-the-art technology in the field, and discusses multi-agent frameworks. The author describes step by step how to assemble algorithms for generating a simulation model, program, method for visualization, and further research tasks.
Reinforcement learning (RL) is often touted as a promising approach for costly and risk-sensitive applications, yet practicing and learning in those domains directly is expensive. It costs time (e.g., OpenAI's Dota2 project used 10,000 years of experience), it costs money (e.g., "inexpensive" robotic arms used in research typically cost $10,000 to $30,000), and it could even be dangerous to humans. How can an intelligent agent learn to solve tasks in environments in which it cannot practice? For many tasks, such as assistive robotics and self-driving cars, we may have access to a different practice area, which we will call the source domain. While the source domain has different dynamics than the target domain, experience in the source domain is much cheaper to collect.
When the average person thinks about AI and robots what often comes to mind are post-apocalyptic visions of scary, super-intelligent machines taking over the world, or even the universe. The Terminator movie series is a good reflection of this fear of AI, with the core technology behind the intelligent machines powered by Skynet, referred to as an "artificial neural network-based conscious group mind and artificial general superintelligence system". However, the AI of today looks nothing like the worrisome science fiction representation. Rather, AI is performing many tedious and manual tasks and providing value from recognition and conversation systems to predictive analytics pattern matching and autonomous systems. In that context, the fact that governments and military organizations are investing heavily in AI shouldn't be as much concerning as it is intriguing. The ways that machine learning and AI are being implemented are both mundane from the perspective of enabling humans to do their existing tasks better, and very interesting seeing how machines are being made more intelligent to give humans better understanding and control of the environment around them.
Facebook researchers have developed a general AI framework called Recursive Belief-based Learning (ReBeL) that they say achieves better-than-human performance in heads-up, no-limit Texas hold'em poker while using less domain knowledge than any prior poker AI. They assert that ReBeL is a step toward developing universal techniques for multi-agent interactions -- in other words, general algorithms that can be deployed in large-scale, multi-agent settings. Potential applications run the gamut from auctions, negotiations, and cybersecurity to self-driving cars and trucks. Combining reinforcement learning with search at AI model training and test time has led to a number of advances. Reinforcement learning is where agents learn to achieve goals by maximizing rewards, while search is the process of navigating from a start to a goal state.
Just like natural evolution that transformed all living creatures throughout history, machines can evolve and behave the same way! Unlike what most people would think, AI is not a new technology. However, it has undoubtedly evolved tremendously over the past years with the advancement in the training of deep artificial neural networks, primarily driven by the increase in available compute power which is necessary to train such networks for meaningful results. Swarm intelligence (SI), a sub-field of artificial intelligence, is the collective behavior of decentralized, self-organized systems. It does not require as much compute power as that needed for Deep Learning, but it can be employed in specific cases as a simple and efficient solution.
In recent years, many sectors have experienced significant progress in automation, associated with the growing advances in artificial intelligence and machine learning. There are already automated robotic weapons, which are able to evaluate and engage with targets on their own, and there are already autonomous vehicles that do not need a human driver. It is argued that the use of increasingly autonomous systems (AS) should be guided by the policy of human control, according to which humans should execute a certain significant level of judgment over AS. While in the military sector there is a fear that AS could mean that humans lose control over life and death decisions, in the transportation domain, on the contrary, there is a strongly held view that autonomy could bring significant operational benefits by removing the need for a human driver. This article explores the notion of human control in the United States in the two domains of defense and transportation.
One of the ultimate goals of artificial intelligence is the ability for machines to operate on their own, with little or any human interaction. This idea of autonomous systems makes up one of the seven patterns of AI that represents the common ways that organizations are applying AI. While some of the patterns are focused on predictive analytics or conversational patterns, or systems that can recognize things in the world around us, those patterns still involve human interaction. After all, we need humans to be involved in conversational or recognition systems. However, the autonomous pattern is much more complicated as we're asking a machine to do something in the real world without a human in the loop.
Despite recent advances in artificial intelligence (AI) research, human children are still by far the best learners we know of, learning impressive skills like language and high-level reasoning from very little data. Children's learning is supported by highly efficient, hypothesis-driven exploration: in fact, they explore so well that many machine learning researchers have been inspired to put videos like the one below in their talks to motivate research into exploration methods. However, because applying results from studies in developmental psychology can be difficult, this video is often the extent to which such research actually connects with human cognition. Why is directly applying research from developmental psychology to problems in AI so hard? For one, taking inspiration from developmental studies can be difficult because the environments that human children and artificial agents are typically studied in can be very different.
In the opening pages of Burn-In , an FBI agent conducts close-quarters surveillance of a suspected terrorist bomber in Washington, D.C. Simultaneously, in New Jersey, an elderly gentleman listens attentively to the enthusiastic technological prognostications of a world-famous computer scientist and mathematician from the back of a hallowed lecture hall at Princeton University. Moments later, he bludgeons the speaker to death with his cane. In this, their second novel, coauthors Peter Warren Singer and August Cole—both renowned technology and policy experts—come close to perfecting the genre of educational and informative techno-thriller. Like their first such collaboration ([ 1 ]), this latest entry portrays a world in which conventional aspects of domestic security and law enforcement—combating terrorism, managing protests and social upheavals, tracking a serial killer, providing a secure environment on college campuses—all occur within a transformative technological context that both enables and simultaneously disrupts these myriad objectives. As the narrative unfolds, a complex tapestry of emergent, disruptive technologies is revealed. Far from the fanciful inventions that typically populate science fiction, the systems described herein are currently available or under development for imminent deployment. The D.C. traffic congestion with which agent Lara Keegan and her partner have to contend, for example, is mostly composed of driverless vehicles, their complex operational algorithms engaged in competitive maneuvering for even the slightest comparative advantage. If the agents invoke the emergency override protocol granted to law enforcement personnel and cause the other vehicles to move aside, the surveillance drones buzzing overhead will immediately transmit this activity to the news outlets that operate them, alerting the terrorist to their presence. Keegan's field of vision, meanwhile, is networked into an operations command center via virtual reality glasses, which display real-time data on the suspect's location. These “viz glasses” continuously exchange data with other law enforcement personnel, while simultaneously performing facial scans of the surrounding crowds, subjecting each passerby to massive digital analysis. Once apprehended, despite his uncooperative silence, the suspect's identity is unmasked by a Tactical Autonomous Mobility System (TAMS), a military robot whose combat utility proved minimal and is now being tested for possible use in domestic law enforcement scenarios. Keegan, we learn, has been selected to field-test this robotic deep-learning technology system because of her prior experience managing the deployment and “force mix” of unmanned systems for the Marine Corps in Afghanistan. In technology circles, what she has been asked to undertake is known as a burn-in, a lengthy trial run of any new technological breakthrough, designed to push it to its limits of reliable functionality. The novel also contains ample instances of what the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation dub the ethical, legal, and social implications (ELSI) of technological development and diffusion. Just before his death, for example, the Princeton computer scientist boasts to his elderly guest how his use of Linux open-source software to develop complex machine-learning algorithms has made artificial intelligence (AI) universally available and affordable for every conceivable purpose. As his killer peels off an AI-designed silicon facial mask (manufactured on a 3D printer to confuse the university's AI-assisted security and surveillance system), he reveals himself to be a former DARPA engineer whose wife and son were tragically killed in a Metro crash caused by dangerous emergent behaviors in one of the scientist's AI-governed public transportation systems. This narrative thread, and many others throughout the book, illustrate what coauthor Peter Warren Singer identified in his widely acclaimed book Wired for War (published in 2009) as a key constituent of technological innovation and advance: “Anything that can go wrong, will—at the worst possible moment.” The aim of this work of fiction is not merely to engage and entertain but also to educate and inform readers about the vast array of automated and increasingly intelligent autonomous systems that are proliferating in availability and use. The authors provide detailed documentation of the actual features and current use of these systems, together with a companion educational guide to help instructors use the novel to teach about the profound depths of the robotic and AI revolution that is taking place all around us. 1. [↵]1. P. W. Singer, 2. A. Cole , Ghost Fleet: A Novel of the Next World War (Houghton Mifflin Harcourt, 2015). : #ref-1 : #xref-ref-1-1 "View reference 1 in text"