and a link to a paper in Frontiers in Robotics and AI
Empowerment As Replacement for the Three Laws of Robotics
Christoph Salge1,2 and Daniel Polani1*
1 SEPIA Unit, Adaptive Systems Research Group, School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom,
2 Game Innovation Lab, Department of Computer Science and Engineering, New York University, Brooklyn, NY, United States
The greater ubiquity of robots creates a need for generic guidelines for robot behavior. We focus less on how a robot can technically achieve a predefined goal and more on what a robot should do in the first place. Particularly, we are interested in the question how a heuristic should look like, which motivates the robot’s behavior in interaction with human agents. We make a concrete, operational proposal as to how the information-theoretic concept of empowerment can be used as a generic heuristic to quantify concepts, such as self-preservation, protection of the human partner, and responding to human actions. While elsewhere we studied involved single-agent scenarios in detail, here, we present proof-of-principle scenarios demonstrating how empowerment interpreted in light of these perspectives allows one to specify core concepts with a similar aim as Asimov’s Three Laws of Robotics in an operational way. Importantly, this route does not depend on having to establish an explicit verbalized understanding of human language and conventions in the robots. Also, it incorporates the ability to take into account a rich variety of different situations and types of robotic embodiment.
the paper is outside Nature Communications paywall
Optimal coding and neuronal adaptation in economic decisions
Aldo Rustichini1, Katherine E. Conen2, Xinying Cai2,5 & Camillo Padoa-Schioppa
During economic decisions, offer value cells in orbitofrontal cortex (OFC) encode the values of offered goods. Furthermore, their tuning functions adapt to the range of values available in any given context. A fundamental and open question is whether range adaptation is beha- viorally advantageous. Here we present a theory of optimal coding for economic decisions. We propose that the representation of offer values is optimal if it ensures maximal expected payoff. In this framework, we examine offer value cells in non-human primates. We show that their responses are quasi-linear even when optimal tuning functions are highly non-linear. Most importantly, we demonstrate that for linear tuning functions range adaptation max- imizes the expected payoff. Thus value coding in OFC is functionally rigid (linear tuning) but parametrically plastic (range adaptation with optimal gain). Importantly, the benefit of range adaptation outweighs the cost of functional rigidity. While generally suboptimal, linear tuning may facilitate transitive choices.