An Abstract Formal Basis for Digital Crowds

Crowdsourcing, together with its related approaches, has become very popular in recent years. All crowdsourcing processes involve the participation of a digital crowd, a large number of people that access a single Internet platform or shared service. In this paper we explore the possibility of applying formal methods, typically used for the verification of software and hardware systems, in analysing the behaviour of a digital crowd. More precisely, we provide a formal description language for specifying digital crowds. We represent digital crowds in which the agents do not directly communicate with each other. We further show how this specification can provide the basis for sophisticated formal methods, in particular formal verification.Comment: 32 pages, 4 figure

Towards Verifiably Ethical Robot Behaviour

Ensuring that autonomous systems work ethically is both complex and difficult. However, the idea of having an additional `governor' that assesses options the system has, and prunes them to select the most ethical choices is well understood. Recent work has produced such a governor consisting of a `consequence engine' that assesses the likely future outcomes of actions then applies a Safety/Ethical logic to select actions. Although this is appealing, it is impossible to be certain that the most ethical options are actually taken. In this paper we extend and apply a well-known agent verification approach to our consequence engine, allowing us to verify the correctness of its ethical decision-making.Comment: Presented at the 1st International Workshop on AI and Ethics, Sunday 25th January 2015, Hill Country A, Hyatt Regency Austin. Will appear in the workshop proceedings published by AAA

Explaining BDI Agent Behaviour Through Dialogue

This work arose out of conversations at a Lorentz Workshop on the Dynamics of Multi-Agent Systems (2018). Thanks are due Koen Hindriks and Vincent Koeman for their input. The work was supported by the UKRI/EPSRC RAIN [EP/R026084], SSPEDI [EP/P011829/1 ] and FAIR-SPACE [EP/R026092] Robotics and AI Hubs and the Trustworthy Autonomous Systems Verifiability Node [EP/V026801/1]. Both authors contributed equally to the work, and author names are listed in alphabetical order.Peer reviewedPublisher PD

Agent Based Approaches to Engineering Autonomous Space Software

Current approaches to the engineering of space software such as satellite control systems are based around the development of feedback controllers using packages such as MatLab's Simulink toolbox. These provide powerful tools for engineering real time systems that adapt to changes in the environment but are limited when the controller itself needs to be adapted. We are investigating ways in which ideas from temporal logics and agent programming can be integrated with the use of such control systems to provide a more powerful layer of autonomous decision making. This paper will discuss our initial approaches to the engineering of such systems.Comment: 3 pages, 1 Figure, Formal Methods in Aerospac

Palladium nanoparticles from desulfovibrio alaskensis G20 catalyze biocompatible sonogashira and biohydrogenation cascades

[Image: see text] Transition-metal nanoparticles produced by living bacteria are emerging as novel catalysts for sustainable synthesis. However, the scope of their catalytic activity and their ability to be integrated within metabolic pathways for the bioproduction of non-natural small molecules has been underexplored. Herein we report that Pd nanoparticles synthesized by the sulfate-reducing bacterium Desulfovibrio alaskensis G20 (DaPdNPs) catalyze the Sonogashira coupling of phenyl acetylenes and aryl iodides, and the subsequent one-pot hydrogenation to bibenzyl derivatives using hydrogen gas generated from d-glucose by engineered Escherichia coli DD-2. The formal hydroarylation reaction is biocompatible, occurs in aqueous media at ambient temperature, and affords products in 70–99% overall yield. This is the first reported microbial nanoparticle to catalyze the Sonogashira reaction and the first demonstration that these biogenic catalysts can be interfaced with the products of engineered metabolism for small molecule synthesis

On proactive, transparent and verifiable ethical reasoning for robots

Previous work on ethical machine reasoning has largely been theoretical, and where such systems have been implemented it has in general been only initial proofs of principle. Here we address the question of desirable attributes for such systems to improve their real world utility, and how controllers with these attributes might be implemented. We propose that ethically-critical machine reasoning should be proactive, transparent and verifiable. We describe an architecture where the ethical reasoning is handled by a separate layer, augmenting a typical layered control architecture, ethically moderating the robot actions. It makes use of a simulation-based internal model, and supports proactive, transparent and verifiable ethical reasoning. To do so the reasoning component of the ethical layer uses our Python based Beliefs, Desires, Intentions (BDI) implementation. The declarative logic structure of BDI facilitates both transparency, through logging of the reasoning cycle, and formal verification methods. To prove the principles of our approach we use a case study implementation to experimentally demonstrate its operation. Importantly, it is the first such robot controller where the ethical machine reasoning has been formally verified