In this work, we investigate how autonomous agents, organized into tribes, learn to use communication signals to coordinate their activities and enhance their collective efficiency. Using the NEC-DAC (Neurally Encoded Culture - Distributed Autonomous Communicators) system, where each agent is equipped with its own neural network for decision-making, we demonstrate how these agents develop a shared behavioral system -- akin to a culture -- through learning and signalling. Our research focuses on the self-organization of culture within these tribes of agents and how varying communication strategies impact their fitness and cooperation. By analyzing different social structures, such as authority hierarchies, we show that the culture of cooperation significantly influences the tribe's performance. Furthermore, we explore how signals not only facilitate the emergence of culture but also enable its transmission across generations of agents. Additionally, we examine the benefits of coordinating behavior and signaling within individual agents' neural networks.

I am on the fence for this paper. I like that the approach is simple. I like that it was tested in different domains. I like that several different views of performance are included. Nevertheless I have many questions, that need resolution before I can increase my score.

In science, macro level descriptions of the causal interactions within complex, dynamical systems are typically deemed convenient, but ultimately reducible to a complete causal account of the underlying micro constituents. Yet, such a reductionist perspective is hard to square with several issues related to autonomy and agency: (1) agents require (causal) borders that separate them from the environment, (2) at least in a biological context, agents are associated with macroscopic systems, and (3) agents are supposed to act upon their environment. Integrated information theory (IIT) (Oizumi et al., 2014) offers a quantitative account of causation based on a set of causal principles, including notions such as causal specificity, composition, and irreducibility, that challenges the reductionist perspective in multiple ways. First, the IIT formalism provides a complete account of a system's causal structure, including irreducible higher-order mechanisms constituted of multiple system elements. Second, a system's amount of integrated information ($\Phi$) measures the causal constraints a system exerts onto itself and can peak at a macro level of description (Hoel et al., 2016; Marshall et al., 2018). Finally, the causal principles of IIT can also be employed to identify and quantify the actual causes of events ("what caused what"), such as an agent's actions (Albantakis et al., 2019). Here, we demonstrate this framework by example of a simulated agent, equipped with a small neural network, that forms a maximum of $\Phi$ at a macro scale.

In my last blog post, I introduced readers to William Grey Walter (1910 – 1977), a renowned neurophysiologist, cybernetician, and robotician. His futuristic aim was to construct mechanical models--robots--that were capable of realistically simulating the behavior of living beings. Grey Walter's most famous robotic creations were his Cybernetic Tortoises. Elmer and Elsie, his first two robots, were constructed between 1948 and 1949. They appeared to exhibit intelligent action: they were goal-directed (they moved toward light and stopped doing so when they reached the light) and they avoided obstacles that blocked their way to the goal. In a truly remarkable coincidence, robotic tortoises have very recently made the news!

Classical planning has been notably successful in synthesizing finite plans to achieve states where propositional goals hold. In the last few years, classical planning has also been extended to incorporate temporally extended goals, expressed in temporal logics such as LTL, to impose restrictions on the state sequences generated by finite plans. In this work, we take the next step and consider the computation of infinite plans for achieving arbitrary LTL goals. We show that infinite plans can also be obtained efficiently by calling a classical planner once over a classical planning encoding that represents and extends the composition of the planning domain and the Buchi automaton representing the goal. This compilation scheme has been implemented and a number of experiments are reported.