Memory in biological beings is as complex as the rational complexity of that concrete being requires. Clearly, memory helps to conform knowledge bases to serve the needs of the specific natural being. To analogize from Robotics concepts, it seems that the degrees of freedom in the biological being’s memory are higher or lower depending upon the rationality of each living being. Robots and artificial systems appear to require analogous structures. That is, to build a reactive system, the requirement of memory is not highly demanding with respect to the degrees of freedom. However, the required degrees of freedom seems to grow as the ability of the artificial system to deliberate increases. Consequently, to design artificial systems that would implement cognitive abilities, it is required to rethink memory structures. When designing a Cognitive Artificial System, memory systems should be thought of as highly accessible discrete units. In addition, these systems would require designs in the form of distributed architectures with non-linear features, such as those of human thought. In addition, they should allow for complex mixed types of data (text, images, time or so). Blockchain has attracted great interest for a few years now, especially since the appearance of Bitcoin. A blockchain is a distributed ledger that combines an append-only data structure designed to be resistant to modifications, with a consensus protocol [ 1 , 2 ]. This innovation can be thought of as a sequence of containers, the blocks, that store two things: the information of a “system” and the “service” that such system provides [ 2 ], and it provides an interesting starting point to rethink memory systems in robots.
Joseph D. Monaco, Grace M. Hwang, Kevin Schultz, Kechen Zhang
The rise of mobile multi-agent robotic platforms is outpacing control paradigms for tasks that require operating in complex, realistic environments. To leverage inertial, energetic, and cost benefits of small-scale robots, critical future applications may depend on coordinating large numbers of agents with minimal onboard sensing and communication resources. In this article, we present the perspective that adaptive and resilient autonomous control of swarms of minimal agents might follow from a direct analogy with the neural circuits of spatial cognition in rodents. We focus on spatial neurons such as place cells found in the hippocampus. Two major emergent hippocampal phenomena, self-stabilizing attractor maps and temporal organization by shared oscillations, reveal theoretical solutions for decentralized self-organization and distributed communication in the brain. We consider that autonomous swarms of minimal agents with low-bandwidth communication are analogous to brain circuits of oscillatory neurons with spike-based propagation of information. The resulting notion of `neural swarm control' has the potential to be scalable, adaptive to dynamic environments, and resilient to communication failures and agent attrition. We illustrate a path toward extending this analogy into multi-agent systems applications and discuss implications for advances in decentralized swarm control.
Reports on the concept of blockchains, a new form of information technology that could have several important future applications. One is blockchain thinking, formulating thinking as a blockchain process. This could have benefits for both artificial intelligence and human enhancement, and their potential integration. Blockchain thinking is outlined here as an input-processing-output computational system.