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  Right now, the swarm behaviors Davis is using are very basic. The human can command the swarm to fly in a formation, to land, or to attack enemy aircraft. The drones then sort themselves into position for landing or formation flying to “deconflict” their actions. For some tasks, such as landing, this is done relatively easily by altitude: lower planes land first. Other tasks, such as deconflicting air-to-air combat are trickier. It wouldn’t do any good, for example, for all of the drones in the swarm to go after the same enemy aircraft. They need to coordinate their behavior.

  The problem is analogous to that of outfielders calling a fly ball. It wouldn’t make sense to have the manager calling who should catch the ball from the dugout. The outfielders need to coordinate among themselves. “It’s one thing when you’ve got two humans that can talk to one another and one ball,” Davis explained. “It’s another thing when there’s fifty humans and fifty balls.” This task would be effectively impossible for humans, but a swarm can accomplish this very quickly, through a variety of methods. In centralized coordination, for example, individual swarm elements pass their data back to a single controller, which then issues commands to each robot in the swarm. Hierarchical coordination, on the other hand, decomposes the swarm into teams and squads much like a military organization, with orders flowing down the chain of command.

  Consensus-based coordination is a decentralized approach where all of the swarm elements communicate with one another simultaneously and collectively decide on a course of action. They could do this by using “voting” or “auction” algorithms to coordinate behavior. For example, each swarm element could place a “bid” on an “auction” to catch the fly ball. The individual that bids highest “wins” the auction and catches the ball, while the others move out of the way.

  Emergent coordination is the most decentralized approach and is how flocks of birds, colonies of insects, and mobs of people work, with coordinated action arising naturally from each individual making decisions based on those nearby. Simple rules for individual behavior can lead to very complex collective action, allowing the swarm to exhibit “collective intelligence.” For example, a colony of ants will converge on an optimal route to take food back to the nest over time because of simple behavior from each individual ant. As ants pick up food, they leave a pheromone trail behind them as they move back to the nest. If they come across an existing trail with stronger pheromones, they’ll switch to it. More ants will arrive back at the nest sooner via the faster route, leading to a stronger pheromone trail, which will then cause more ants to use that trail. No individual ant “knows” which trail is fastest, but collectively the colony converges on the fastest route.

  Swarm Command-and-Control Models

  Communication among elements of the swarm can occur through direct signaling, akin to an outfielder yelling “I got it!”; indirect methods such as co-observation, which is how schools of fish and herds of animals stay together; or by modifying the environment in a process called stigmergy, like ants leaving pheromones to mark a trail.

  The drones in Davis’s swarm communicate through a central Wi–Fi router on the ground. They avoid collisions by staying within narrow altitude windows that are automatically assigned by the central ground controller. Their attack behavior is uncoordinated, though. The “greedy shooter” algorithm simply directs each drone to attack the nearest enemy drone, regardless of what the other drones are doing. In theory, all the drones could converge on the same enemy drone, leaving other enemies untouched. It’s a terrible method for air-to-air combat, but Davis and his colleagues are still in the proof-of-concept stage. They have experimented with a more decentralized auction-based approach and found it to be very robust to disruptions, including up to a 90 percent communications loss within the swarm. As long as some communications are up, even if they’re spotty, the swarm will converge on a solution.

  The effect of fifty aircraft working together, rather than fighting individually or in wingman pairs as humans do today, would be tremendous. Coordinated behavior is the difference between a basketball team and five ball hogs all making a run at the basket themselves. It’s the difference between a bunch of lone wolves and a wolf pack.

  In 2016, the United States demonstrated 103 aerial drones flying together in a swarm that DoD officials described as “a collective organism, sharing one distributed brain for decision-making and adapting to each other like swarms in nature.” (Not to be outdone, a few months later China demonstrated a 119-drone swarm.) Fighting together, a drone swarm could be far more effective than the same number of drones fighting individually. No one yet knows what the best tactics will be for swarm combat, but experiments such as these are working to tease them out. New tactics might even be evolved by the machines themselves through machine learning or evolutionary approaches.

  Swarms aren’t merely limited to the air. In August 2014, the U.S. Navy Office of Naval Research (ONR) demonstrated a swarm of small boats on the James River in Virginia by simulating a mock strait transit in which the boats protected a high-value Navy ship against possible threats, escorting it through a simulated high-danger area. When directed by a human controller to investigate a potential threat, a detachment of uninhabited boats moved to intercept and encircle the suspicious vessel. The human controller simply directed them to intercept the designated suspicious ship; the boats moved autonomously, coordinating their actions by sharing information. This demonstration involved five boats working together, but the concept could be scaled up to larger numbers, just as in aerial drone swarms.

  Bob Brizzolara, who directed the Navy’s demonstration, called the swarming boats a “game changer.” It’s an often-overused term, but in this case, it’s not hyperbole—robotic boat swarms are highly valuable to the Navy as a potential way to guard against threats to its ships. In October 2000, the USS Cole was attacked by al-Qaida terrorists using a small explosive-laden boat while in port in Aden, Yemen. The blast killed seventeen sailors and cut a massive gash in the ship’s hull. Similar attacks continue to be a threat to U.S. ships, not just from terrorists but also from Iran, which regularly uses small high-speed craft to harass U.S. ships near the Straits of Hormuz. Robot boats could intercept suspicious vessels further away, putting eyes (and potentially weapons) on potentially hostile boats without putting sailors at risk.

  What the robot boats might do after they’ve intercepted a potentially hostile vessel is another matter. In a video released by the ONR, a .50 caliber machine gun is prominently displayed on the front of one of the boats. The video’s narrator makes no bones about the fact that the robot boats could be used to “damage or destroy hostile vessels,” but the demonstration didn’t involve firing any actual bullets, and didn’t include a consideration of what the rules of engagement actually would have been. Would a human be required to pull the trigger? When pressed by reporters following the demonstration, a spokesman for ONR explained that “there is always a human in the loop when it comes to the actual engagement of an enemy.” But the spokesman also acknowledged that “under this swarming demonstration with multiple [unmanned surface vehicles], ONR did not study the specifics of how the human-in-the-loop works for rules of engagement.”

  OODA Loop

  The Navy’s fuzzy answer to such a fundamental question reflects a tension in the military’s pursuit of more advanced robotics. Even as researchers and engineers move to incorporate more autonomy, there is an understanding that there are—or should be—limits on autonomy when it comes to the use of weapons. What exactly those limits are, however, is often unclear.

  REACHING THE LIMIT

  How much autonomy is too much? The U.S. Air Force laid out an ambitious vision for the future of robot aircraft in their Unmanned Aircraft Systems Flight Plan, 2009–2047. The report envisioned a future where an arms race in speed drove a desire for ever-faster automation, not unlike real-world competition in automated stock trading.

  In air combat, pilots talk about an observe, orient, decide, act (OODA)
loop, a cognitive process pilots go through when engaging enemy aircraft. Understanding the environment, deciding, and acting faster than the enemy allows a pilot to “get inside” the enemy’s OODA loop. While the enemy is still trying to understand what’s happening and decide on a course of action, the pilot has already changed the situation, resetting the enemy to square one and forcing him or her to come to grips with a new situation. Air Force strategist John Boyd, originator of the OODA loop, described the objective:

  Goal: Collapse adversary’s system into confusion and disorder by causing him to over and under react to activity that appears simultaneously menacing as well as ambiguous, chaotic, or misleading.

  If victory comes from completing this cognitive process faster, then one can see the advantage in automation. The Air Force’s 2009 Flight Plan saw tremendous potential for computers to exceed human decision-making speeds:

  Advances in computing speeds and capacity will change how technology affects the OODA loop. Today the role of technology is changing from supporting to fully participating with humans in each step of the process. In 2047 technology will be able to reduce the time to complete the OODA loop to micro or nanoseconds. Much like a chess master can outperform proficient chess players, [unmanned aircraft systems] will be able to react at these speeds and therefore this loop moves toward becoming a “perceive and act” vector. Increasingly humans will no longer be “in the loop” but rather “on the loop”—monitoring the execution of certain decisions. Simultaneously, advances in AI will enable systems to make combat decisions and act within legal and policy constraints without necessarily requiring human input.

  This, then, is the logical culmination of the arms race in speed: autonomous weapons that complete engagements all on their own. The Air Force Flight Plan acknowledged the gravity of what it was suggesting might be possible. The next paragraph continued:

  Authorizing a machine to make lethal combat decisions is contingent upon political and military leaders resolving legal and ethical questions. These include the appropriateness of machines having this ability, under what circumstances it should be employed, where responsibility for mistakes lies and what limitations should be placed upon the autonomy of such systems. . . . Ethical discussions and policy decisions must take place in the near term in order to guide the development of future [unmanned aircraft system] capabilities, rather than allowing the development to take its own path apart from this critical guidance.

  The Air Force wasn’t recommending autonomous weapons. It wasn’t even suggesting they were necessarily a good idea. What it was suggesting was that autonomous systems might have advantages over humans in speed, and that AI might advance to the point where machines could carry out lethal targeting and engagement decisions without human input. If that is true, then legal, ethical, and policy discussions should take place now to shape the development of this technology.

  At the time the Air Force Flight Plan was released in 2009, I was working in the Office of the Secretary of Defense as a civilian policy analyst focusing on drone policy. Most of the issues we were grappling with at the time had to do with how to manage the overwhelming demand for more drones from Iraq and Afghanistan. Commanders on the ground had a seemingly insatiable appetite for drones. Despite the thousands that had been deployed, they wanted more, and Pentagon senior leaders—particularly in the Air Force—were concerned that spending on drones was crowding out other priorities. Secretary of Defense Robert Gates, who routinely chastised the Pentagon for its preoccupation with future wars over the ongoing ones in Iraq and Afghanistan, strongly sided with warfighters in the field. His guidance was clear: send more drones. Most of my time was spent figuring out how to force the Pentagon bureaucracy to comply with the secretary’s direction and respond more effectively to warfighter needs, but when policy questions like this came up, eyes turned toward me.

  I didn’t have the answers they wanted. There was no policy on autonomy. Although the Air Force had asked for policy guidance in their 2009 Flight Plan, there wasn’t even a conversation under way.

  The 2011 DoD roadmap, which I was involved in writing, took a stab at an answer, even if it was a temporary one:

  Policy guidelines will especially be necessary for autonomous systems that involve the application of force. . . . For the foreseeable future, decisions over the use of force and the choice of which individual targets to engage with lethal force will be retained under human control in unmanned systems.

  It didn’t say much, but it was the first official DoD policy statement on lethal autonomy. Lethal force would remain under human control for the “foreseeable future.” But in a world where AI technology is racing forward at a breakneck pace, how far into the future can we really see?

  2

  THE TERMINATOR AND THE ROOMBA

  WHAT IS AUTONOMY?

  Autonomy is a slippery word. For one person, “autonomous robot” might mean a household Roomba that vacuums your home while you’re away. For another, autonomous robots conjure images from science fiction. Autonomous robots could be a good thing, like the friendly—if irritating—C-3PO from Star Wars, or could lead to rogue homicidal agents, like those Skynet deploys against humanity in the Terminator movies.

  Science fiction writers have long grappled with questions of autonomy in robots. Isaac Asimov created the now-iconic Three Laws of Robotics to govern robots in his stories:

  1

  A robot may not injure a human being or, through inaction, allow a human being to come to harm.

  2

  A robot must obey orders given by human beings except where such orders would conflict with the first law.

  3

  A robot must protect its own existence as long as such protection does not conflict with the first or second law.

  In Asimov’s stories, these laws embedded within the robot’s “positronic brain” are inviolable. The robot must obey. Asimov’s stories often explore the consequences of robots’ strict obedience of these laws, and loopholes in the laws themselves. In the Asimov-inspired movie I, Robot (spoiler alert), the lead robot protagonist, Sonny, is given a secret secondary processor that allows him to override the Three Laws, if he desires. On the outside, Sonny looks the same as other robots, but the human characters can instantly tell there is something different about him. He dreams. He questions them. He engages in humanlike dialogue and critical thought of which the other robots are incapable. There is something unmistakably human about Sonny’s behavior.

  When Dr. Susan Calvin discovers the source of Sonny’s apparent anomalous conduct, she finds it hidden in his chest cavity. The symbolism in the film is unmistakable: unlike other robots who are slaves to logic, Sonny has a “heart.”

  Fanciful as it may be, I, Robot’s take on autonomy resonates. Unlike machines, humans have the ability to ignore instructions and make decisions for themselves. Whether robots can ever have something akin to human free will is a common theme in science fiction. In I, Robot’s climactic scene, Sonny makes a choice to save Dr. Calvin, even though it means risking the success of their mission to defeat the evil AI V.I.K.I., who has taken over the city. It’s a choice motivated by love, not logic. In the Terminator movies, when the military AI Skynet becomes self-aware, it makes a different choice. Upon determining that humans are a threat to its existence, Skynet decides to eliminate them, starting global nuclear war and initiating “Judgment Day.”

  THE THREE DIMENSIONS OF AUTONOMY

  In the real world, machine autonomy doesn’t require a magical spark of free will or a soul. Autonomy is simply the ability for a machine to perform a task or function on its own.

  The DoD unmanned system roadmaps referred to “levels” or a “spectrum” of autonomy, but those classifications are overly simplistic. Autonomy encompasses three distinct concepts: the type of task the machine is performing; the relationship of the human to the machine when performing that task; and the sophistication of the machine’s decision-making when performing the task. This mean
s there are three different dimensions of autonomy. These dimensions are independent, and a machine can be “more autonomous” by increasing the amount of autonomy along any of these spectrums.

  The first dimension of autonomy is the task being performed by the machine. Not all tasks are equal in their significance, complexity, and risk: a thermostat is an autonomous system in charge of regulating temperature, while Terminator’s Skynet was given control over nuclear weapons. The complexity of decisions involved and the consequences if the machine fails to perform the task appropriately are very different. Often, a single machine will perform some tasks autonomously, while humans are in control of other tasks, blending human and machine control within the system. Modern automobiles have a range of autonomous features: automatic braking and collision avoidance, antilock brakes, automatic seat belt retractors, adaptive cruise control, automatic lane keeping, and self-parking. Some autonomous functions, such as autopilots in commercial airliners, can be turned on or off by a human user. Other autonomous functions, like airbags, are always ready and decide for themselves when to activate. Some autonomous systems may be designed to override the human user in certain situations. U.S. fighter aircraft have been modified with an automatic ground collision avoidance system (Auto-GCAS). If the pilot becomes disoriented and is about to crash, Auto-GCAS will take control of the aircraft at the last minute to pull up and avoid the ground. The system has already saved at least one aircraft in combat, rescuing a U.S. F-16 in Syria.