The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI

After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two possible ways traditional reinforcement learning could be altered to remove this roadblock

AGI and the Knight-Darwin Law: why idealized AGI reproduction requires collaboration

Can an AGI create a more intelligent AGI? Under idealized assumptions, for a certain theoretical type of intelligence, our answer is: “Not without outside help”. This is a paper on the mathematical structure of AGI populations when parent AGIs create child AGIs. We argue that such populations satisfy a certain biological law. Motivated by observations of sexual reproduction in seemingly-asexual species, the Knight-Darwin Law states that it is impossible for one organism to asexually produce another, which asexually produces another, and so on forever: that any sequence of organisms (each one a child of the previous) must contain occasional multi-parent organisms, or must terminate. By proving that a certain measure (arguably an intelligence measure) decreases when an idealized parent AGI single-handedly creates a child AGI, we argue that a similar Law holds for AGIs

Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide Python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/

Exploring Social Hierarchy Computationally to Further Our Understanding of Social Organizations Within Their Environments

Hierarchy is ever-present across countless human societies, a seemingly inescapable reality of small organizations and national governments. However, there is a lot about hierarchy we don’t understand, and if we want to make better organizations and better society, it is crucial we learn more about it. This dissertation investigates three questions: 1) “What is hierarchy?” 2) “How is hierarchy useful?” 3) “How does hierarchy vary?” I find that social scientists do not all mean the same thing by hierarchy, even within the same fields; yet, they do consistently write of hierarchy as control (like boss-employee relations), hierarchy as rank (like social class relations), and hierarchy as nested structure (like cities in states), so future scholars can and should be clear in what they mean. Next, I use a computer simulation to show that control hierarchy can be useful in changing environments where workers see local views of change and managers see the big picture—a tension that is unavoidable in such environments. Hierarchy can make this tension useful if and only if the workers have autonomy to weigh the manager’s information about the environment in their decisions; if they must obey the manager no matter what, then they do very poorly in nearly all types of changing environments. Lastly, I use workforce data from US federal agencies to look at organizational structure and control hierarchy in agencies from 2004 to 2021. I find that hierarchy is similar across most agencies, suggesting that overall, hierarchy relates more to scale than function. However, agencies with offices spread across the nation are different from the others, with more and broader management at higher levels. I also find that agencies vary in their organizational structure in other ways, such as the number of distinct occupations they have, and the number of formal rules they must follow, in patterns that are predictable based on their mission statements and agency type; form does follow function. Overall, this dissertation shows that the use of computational techniques in the study of hierarchy can provide great insight, and help us understand organizations more generally