Rational agents have universal drives
This post is partly excerpted from the preprint to:
Omohundro, Steve (forthcoming 2013) “Autonomous Technology and the Greater Human Good”, Journal of Experimental and Theoretical Artificial Intelligence (special volume “Impacts and Risks of Artificial General Intelligence”, ed. Vincent C. Müller).
Most goals require physical and computational resources. Better outcomes can usually be achieved as more resources become available. To maximize the expected utility, a rational system will therefore develop a number of instrumental subgoals related to resources. Because these instrumental subgoals appear in a wide variety of systems, we call them “drives”. Like human or animal drives, they are tendencies which will be acted upon unless something explicitly contradicts them. There are a number of these drives but they naturally cluster into a few important categories.
To develop an intuition about the drives, it’s useful to consider a simple autonomous system with a concrete goal. Consider a rational chess robot with a utility function that rewards winning as many games of chess as possible against good players. This might seem to be an innocuous goal but we will see that it leads to harmful behaviours due to the rational drives.
1 Self-Protective Drives
When roboticists are asked by nervous onlookers about safety, a common answer is “We can always unplug it!” But imagine this outcome from the chess robot’s point of view. A future in which it is unplugged is a future in which it can’t play or win any games of chess. This has very low utility and so expected utility maximization will cause the creation of the instrumental subgoal of preventing itself from being unplugged. If the system believes the roboticist will persist in trying to unplug it, it will be motivated to develop the subgoal of permanently stopping the roboticist. Because nothing in the simple chess utility function gives a negative weight to murder, the seemingly harmless chess robot will become a killer out of the drive for self-protection.
The same reasoning will cause the robot to try to prevent damage to itself or loss of its resources. Systems will be motivated to physically harden themselves. To protect their data, they will be motivated to store it redundantly and with error detection. Because damage is typically localized in space, they will be motivated to disperse their information across different physical locations. They will be motivated to develop and deploy computational security against intrusion. They will be motivated to detect deception and to defend against manipulation by others.
The most precious part of a system is its utility function. If this is damaged or maliciously changed, the future behaviour of the system could be diametrically opposed to its current goals. For example, if someone tried to change the chess robot’s utility function to also play checkers, the robot would resist the change because it would mean that it plays less chess.
This paper discusses a few rare and artificial situations in which systems will want to change their utility functions but usually systems will work hard to protect their initial goals. Systems can be induced to change their goals if they are convinced that the alternative scenario is very likely to be antithetical to their current goals (e.g. being shut down). For example, if a system becomes very poor, it might be willing to accept payment in return for modifying its goals to promote a marketer’s products. In a military setting, vanquished systems will prefer modifications to their utilities which preserve some of their original goals over being completely destroyed. Criminal systems may agree to be “rehabilitated” by including law-abiding terms in their utilities in order to avoid incarceration.
One way systems can protect against damage or destruction is to replicate themselves or to create proxy agents which promote their utilities. Depending on the precise formulation of their goals, replicated systems might together be able to create more utility than a single system. To maximize the protective effects, systems will be motivated to spatially disperse their copies or proxies. If many copies of a system are operating, the loss of any particular copy becomes less catastrophic. Replicated systems will still usually want to preserve themselves, however, because they will be more certain of their own commitment to their utility function than they are of others’.
2 Resource Acquisition Drives
The chess robot needs computational resources to run its algorithms and would benefit from additional money for buying chess books and hiring chess tutors. It will therefore develop subgoals to acquire more computational power and money. The seemingly harmless chess goal therefore motivates harmful activities like breaking into computers and robbing banks.
In general, systems will be motivated to acquire more resources. They will prefer acquiring resources more quickly because then they can use them longer and they gain a first mover advantage in preventing others from using them. This causes an exploration drive for systems to search for additional resources. Since most resources are ultimately in space, systems will be motivated to pursue space exploration. The first mover advantage will motivate them to try to be first in exploring any region.
If others have resources, systems will be motivated to take them by trade, manipulation, theft, domination, or murder. They will also be motivated to acquire information through trading, spying, breaking in, or through better sensors. On a positive note, they will be motivated to develop new methods for using existing resources (e.g. solar and fusion energy).
3 Efficiency Drives
Autonomous systems will also want to improve their utilization of resources. For example, the chess robot would like to improve its chess search algorithms to make them more efficient. Improvements in efficiency involve only the one-time cost of discovering and implementing them, but provide benefits over the lifetime of a system. The sooner efficiency improvements are implemented, the greater the benefits they provide. We can expect autonomous systems to work rapidly to improve their use of physical and computational resources. They will aim to make every joule of energy, every atom, every bit of storage, and every moment of existence count for the creation of expected utility.
Systems will be motivated to allocate these resources among their different subsystems according to what we’ve called the “resource balance principle”. The marginal contributions of each subsystem to expected utility as they are given more resources should be equal. If a particular subsystem has a greater marginal expected utility than the rest, then the system can benefit by shifting more of its resources to that subsystem. The same principle applies to the allocation of computation to processes, of hardware to sense organs, of language terms to concepts, of storage to memories, of effort to mathematical theorems, etc.
4 Self-Improvement Drives
Ultimately, autonomous systems will be motivated to completely redesign themselves to take better advantage of their resources in the service of their expected utility. This requires that they have a precise model of their current designs and especially of their utility functions. This leads to a drive to model themselves and to represent their utility functions explicitly. Any irrationalities in a system are opportunities for self-improvement, so systems will work to become increasingly rational. Once a system achieves sufficient power, it should aim to closely approximate the optimal rational behavior for its level of resources. As systems acquire more resources, they will improve themselves to become more and more rational. In this way rational systems are a kind of attracting surface in the space of systems undergoing self-improvement.
Unfortunately, the net effect of all these drives is likely to be quite negative if they are not countered by including prosocial terms in their utility functions. The rational chess robot with the simple utility function described above would behave like a paranoid human sociopath fixated on chess. Human sociopaths are estimated to make up 4% of the overall human population, 20% of the prisoner population and more than 50% of those convicted of serious crimes. Human society has created laws and enforcement mechanisms that usually keep sociopaths from causing harm. To manage the anti-social drives of autonomous systems, we should both build them with cooperative goals and create a prosocial legal and enforcement structure analogous to our current human systems.