Abstract
The incorporation of robots in the social fabric of our society has taken giant leaps, enabled by advances in artificial intelligence and big data. As these robots become increasingly adept at parsing through enormous datasets and making decisions where humans fall short, a significant challenge lies in the analysis of robot behavior. Capturing interactions between robots, humans and IoT devices in traditional structures such as graphs poses challenges in the storage and analysis of large data sets in dense graphs generated by frequent activities. This paper proposes a framework that uses the blockchain for the storage of robotic interactions, and the use of sheaf theory for analysis of these interactions. Applications of our framework for social robots and swarm robots incorporating imperfect information and irrationality on the blockchain sheaf are proposed. This work shows the application of such a framework for various blockchain applications on the spectrum of human-robot interaction, and identifies key challenges that arise as a result of using the blockchain for robotic applications.
Highlights
A common problem in decision-making is that of imperfect information
In the presence of imperfect information, the irrational actor resorts to biases and in the company of like actors, these biases have the potential to be amplified through collective herding or swarming behavior (Beni, 2004)
We propose a robot-learning framework for social robotics that is built upon two important qualities that make human actors “human”
Summary
A common problem in decision-making is that of imperfect information. Imperfect information limits the size of the potential action space for players, and the consequences of a player’s actions sets off a domino effect when confronted by the actions of other players who are operating under imperfect information. The analysis of such graphs with dense structures poses a distinct computational challenge, where increasing size of the network with heterogeneous data collection and processing needs creates dense graphs that cannot be efficiently analyzed for patterns This scenario is further complicated by the potential for smart contracts to bind with billions of IoT devices, robots, humans and software bots that will create large amounts of graph data. We propose the use of sheaf theory to derive important features about the shape of the action spaces created by human-robot and robot-robot interaction These features are stored on the blockchain, harnessing the blockchain’s ability to store data in a distributed, trusted immutable manner
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