Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot

As a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human–robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a large amount of implementation work from human engineers. In addition, the written results cannot be accurately evaluated. To overcome these limitations, in this paper, we propose a robotic calligraphy model that combines a generative adversarial network (GAN) and deep reinforcement learning to enable a calligraphy robot to learn to write Chinese character strokes directly from images captured from Chinese calligraphic textbooks. In our proposed model, to automatically establish an aesthetic evaluation system for Chinese calligraphy, a GAN is first trained to understand and reconstruct stroke images. Then, the discriminator network is independently extracted from the trained GAN and embedded into a variant of the reinforcement learning method, the “actor-critic model”, as a reward function. Thus, a calligraphy robot adopts the improved actor-critic model to learn to write multiple character strokes. The experimental results demonstrate that the proposed model successfully allows a calligraphy robot to write Chinese character strokes based on input stroke images. The performance of our model, compared with the state-of-the-art deep reinforcement learning method, shows the efficacy of the combination approach. In addition, the key technology in this work shows promise as a solution for robotic autonomous assembly

Human-Agent Interaction Model Learning based on Crowdsourcing

Missions involving humans interacting with automated systems become increasingly common. Due to the non-deterministic behavior of the human and possibly high risk of failing due to human factors, such an integrated system should react smartly by adapting its behavior when necessary. A promise avenue to design an efficient interaction-driven system is the mixed-initiative paradigm. In this context, this paper proposes a method to learn the model of a mixed-initiative human-robot mission. The first step to set up a reliable model is to acquire enough data. For this aim a crowdsourcing campaign was conducted and learning algorithms were trained on the collected data in order to model the human-robot mission and to optimize a supervision policy with a Markov Decision Process (MDP). This model takes into account the actions of the human operator during the interaction as well as the state of the robot and the mission. Once such a model has been learned, the supervision strategy can be optimized according to a criterion representing the goal of the mission. In this paper, the supervision strategy concerns the robot’s operating mode. Simulations based on the MDP model show that planning under uncertainty solvers can be used to adapt robot’s mode according to the state of the human-robot system. The optimization of the robot’s operation mode seems to be able to improve the team’s performance. The dataset that comes from crowdsourcing is therefore a material that can be useful for research in human-machine interaction, that is why it has been made available on our website

Demand response performance and uncertainty: A systematic literature review

The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

Exploration and Exploitation in Scientific Inquiry: Towards a Society of Explorers

This essay argues that scientific systems have two main functions typical to self-organising adaptive and complex systems: Exploration for and exploitation of information. The self-organising nature, or spontaneous order, of scientific systems was prominently conceived by polymath Michael Polanyi. Revisiting Polanyi’s philosophy of science reveals why scientific freedom is still today as important a value as ever, even though the notion of “freedom” itself must be revised. Namely, freedom of inquiry should serve to maintain a diverse and adaptive balance between exploration (for knowledge) and exploitation (of knowledge). This essay argues that current trends within science policy and scientific communities, from impact assessments to targeted research funding, are often inherently biased towards advancing exploitative functions over explorative activities. Concerns are raised over whether these exploitative biases suppress the explorative nature of scientific inquiry, and thus disturb the self-organisation of scientific systems by favouring hasty and sometimes negligent exploitation. Further concerns are raised as to whether these impaired adaptive capacities of scientific systems lead to reduced resilience of broader society. Finally, Polanyi’s vision of a Society of Explorers, where free exploration is vindicated and safeguarded, is revived in a 21st century context

Exploration and Exploitation in Scientific Inquiry: Towards a Society of Explorers

This essay argues that scientific systems have two main functions typical to self-organising adaptive and complex systems: Exploration for and exploitation of information. The self-organising nature, or spontaneous order, of scientific systems was prominently conceived by polymath Michael Polanyi. Revisiting Polanyi’s philosophy of science reveals why scientific freedom is still today as important a value as ever, even though the notion of “freedom” itself must be revised. Namely, freedom of inquiry should serve to maintain a diverse and adaptive balance between exploration (for knowledge) and exploitation (of knowledge). This essay argues that current trends within science policy and scientific communities, from impact assessments to targeted research funding, are often inherently biased towards advancing exploitative functions over explorative activities. Concerns are raised over whether these exploitative biases suppress the explorative nature of scientific inquiry, and thus disturb the self-organisation of scientific systems by favouring hasty and sometimes negligent exploitation. Further concerns are raised as to whether these impaired adaptive capacities of scientific systems lead to reduced resilience of broader society. Finally, Polanyi’s vision of a Society of Explorers, where free exploration is vindicated and safeguarded, is revived in a 21st century context

Conférence Nationale d'Intelligence Artificielle Année 2020

National audienc

The subjugated knowledge of Prevent: UK terrorism pre-emption and the disruptive history of Northern Ireland

This thesis explores the relationship between Britain's counter-radicalization programme Prevent and the testimony of those convicted of terrorism offences in Northern Ireland. The research explores the striking contradiction whereby Northern Ireland does not implement radicalization pre-emption despite its active dissident groups and notorious history of conflict, yet the rest of the UK does. Utilizing primary interviews with 17 Prevent officials (including Channel's hard-to-reach ‘de-radicalization’ mentors) and over 30 Northern Irish former combatants, the thesis performs a discourse analysis to expose the two fundamentally different ways of knowing terrorism risk in the UK. It undertakes a critical exposition of Prevent's construction and navigation of risk, asking how ‘pre-crime risk’ is observed and intervened upon only on one side of the border, when a fragile ceasefire best describes post-conflict reality on the other. How does the discourse of radicalization subjugate the history of political insurgency in Northern Ireland, rendering it invisible, and what reality is constructed through these silences? Through substantial empirical investigation, the thesis explores how pre-emptive security closes down space for political contestation – ultimately inventing the ‘(de)radicalizable subject’ though a rationality infused with insecurity. To construct this subject, the discourse of ‘risk’ and ‘pre-emption’ has to silence the history of insurgency in Northern Ireland and the voices of its perpetrators. These militants staunchly rebut any narrative that they were ‘vulnerable’ to radicalization, but rather were heroes who actively chose armed rebellion. This thesis brings the disjuncture of UK terrorism knowledge to the forefront, exposing how the discourse of risk, vulnerability, and pre-emption necessarily silences militant testimonies – inventing a world without referring to its inhabitants