Reinforcement learning of normative monitoring intensities

Choosing actions within norm-regulated environments involves balancing achieving one’s goals and coping with any penalties for non-compliant behaviour. This choice becomes more complicated in environments where there is uncertainty. In this paper, we address the question of choosing actions in environments where there is uncertainty regarding both the outcomes of agent actions and the intensity of monitoring for norm violations. Our technique assumes no prior knowledge of probabilities over action outcomes or the likelihood of norm violations being detected by employing reinforcement learning to discover both the dynamics of the environment and the effectiveness of the enforcer. Results indicate agents become aware of greater rewards for violations when enforcement is lax, which gradually become less attractive as the enforcement is increased

OWL-POLAR : semantic policies for agent reasoning

The original publication is available at www.springerlink.comPostprin

Development of a digital tool to overcome the challenges of rural food SMEs

It has been recognised that throughout the UK, rural economies have a significant potential for growth but despite the potential for growth, many rural businesses face barriers that prohibit their expansion. In this study, we focus on one particular group of rural small- to medium-sized enterprises (SMEs): food and drink producers. Through user engagement activities, we identify the issues and needs associated with distributing products to the market, in order to understand the main issues which prevent rural food and drink SMEs from expansion, and to establish the requirements for a digital solution to this challenge

Distributed production control based on emergent agents

status: publishe