Low-Cost Surface Classification System Supported by Deep Neural Models

Determining the surface on which a vehicle is moving is vital information for im-proving active safety systems. Performing the surface classification or estimating adherence through tire slippage can lead to late action in possible risk situations. Currently, approaches based on image, sound, or vibration analysis are emerging as a viable alternative, though sometimes complex. This work proposes a methodology based on the use of low-cost accelerometers combined with Deep Learning tech-niques. The performance of the proposed system is evaluated with real tests, where high percentages of accuracy are obtained in the classification task.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Applying IRON to a Virtual Community Scenario

Normative systems (norms) have been widely proposed as a technique for coordinating multi-agent systems (MAS). The automated synthesis of norms is a complex problem that remains open. IRON (Intelligent Robust On-line Norm synthesis mechanism) is a novel mechanism for the on-line automated synthesis of norms for MASs. IRON produces conflict-free norms that characterise necessary conditions for coordination, without over-regulation. In the past, IRON successfully regulated a traffic scenario even in the presence of non-compliant agents. In this paper, we apply IRON to synthesise norms for a virtual community scenario, where agents are users that share contents within the community. As a result, IRON synthesises norms that prevent users from uploading undesirable contents (i.e., those that users complain about). © 2013 The authors and IOS Press. All rights reserved.This work was funded by AT (CONSOLIDER CSD2007-0022), EVE (TIN2009-14702-C02-01/02), COR (TIN2012-38876-C02-01/02), MECER (201250E053) and the Generalitat of Catalunya (2009-SGR-1434).Peer Reviewe

On the dominant set selection problem and its application to value alignment

Decision makers can often be confronted with the need to select a subset of objects from a set of candidate objects by just counting on preferences regarding the objects' features. Here we formalise this problem as the dominant set selection problem. Solving this problem amounts to finding the preferences over all possible sets of objects. We accomplish so by: (i) grounding the preferences over features to preferences over the objects themselves; and (ii) lifting these preferences to preferences over all possible sets of objects. This is achieved by combining lex-cel -a method from the literature¿with our novel anti-lex-cel method, which we formally (and thoroughly) study. Furthermore, we provide a binary integer program encoding to solve the problem. Finally, we illustrate our overall approach by applying it to the selection of value-aligned norm systems

Instilling moral value alignment by means of multi-objective reinforcement learning

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. Here, we propose a novel way of tackling the value alignment problem as a two-step process. The first step consists on formalising moral values and value aligned behaviour based on philosophical foundations. Our formalisation is compatible with the framework of (Multi-Objective) Reinforcement Learning, to ease the handling of an agent's individual and ethical objectives. The second step consists in designing an environment wherein an agent learns to behave ethically while pursuing its individual objective. We leverage on our theoretical results to introduce an algorithm that automates our two-step approach. In the cases where value-aligned behaviour is possible, our algorithm produces a learning environment for the agent wherein it will learn a value-aligned behaviour

Safety Issues in Buckling of Steel Structures by Improving Accuracy of Historical Methods

Buckling of structural elements is a phenomenon that has great consequences on the bearing capacity of structures. Historically, there have been serious buckling-related structural accidents that have resulted in loss of human lives and high material costs. In this article, an attempt is made to perform a historical analysis of the diverse models that experts have been using in designing and calculating compression buckling of simple metallic elements in the last 275 years. The analysis covers the lapse from the mid-18th century, in which the pioneers in this classic field of structural design are located, up to the present, highlighting the main standards that have been applied to steel structural analysis in the past and at present all over the world. What the study tries to provide is an overall view and a sense of continuity of the methods used for improving structural safety regarding buckling failures in the last three centuries. Each analyzed buckling model is compared with the results of a numerical finite element model of compressed steel columns. Finally, the conclusion reached is that in the last one hundred years, the convergence of solutions proposed in the field is gradually greater and more accurate

Synthesising Liberal Normative Systems

Norms have been extensively studied to coordinate multi-agent systems, and the literature has investigated two general approaches to norm synthesis: off-line (synthesising norms at design-time) and on-line (run-time synthesis). On-line synthesis is generally recognised to be appropriate for open systems, where aspects of the system remain unknown at design-time. In this paper we present LION, an algorithm aimed at synthesising liberal normative systems. lion's normative systems respect the agents' autonomy to the greatest possible extent, constraining their behaviour when only necessary to avoid undesirable system states, lion's norm synthesis is also driven by the need to construct compact normative systems. The key to the success of lion in this multi-objective synthesis process is that it learns about and exploits norm synergies. More precisely, lion can learn when norms are either substitutable or complementary. We show empirically that LION significantly outperforms the state of the art by synthesising normative systems that are more liberal while maintaining representation compactness. Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems.Work funded by projects AT (CSD2007-0022), COR (TIN2012-38876-C02-01, TIN2012-38876-C02-02), and 2009-SGR-1434. Mike Wooldridge was supported by the ERC under Advanced Grant 291528 (“RACE")Peer reviewe

Minimality and Simplicity in the On-line Automated Synthesis of Normative Systems

Much previous research has investigated explicit, machine-process-able norms as a means to facilitate coordination in open multi-agent systems. This research can typically be classified as considering either offline design (norms are synthesised at design time) or online design. Online synthesis techniques aim to construct norms for a system while that system is actually running. A promising recent approach to on-line norm synthesis has been proposed but it suffers from serious drawbacks: (i) it needs too much information; (ii) it ignores issues of compactness in terms of minimality (ensuring that norms are not superfluous) and simplicity (ensuring that agents can process norms with little computational effort). To overcome these drawbacks, we propose an optimistic approach which, even though it uses less information, is able to explore more norms and synthesises sets of norms which are more compact. We present experimental evidence of the quality of our approach. Copyright © 2014, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.Peer Reviewe

Automated Synthesis of Compact Normative Systems

Most normative systems make use of explicit representations of norms (namely, obligations, prohibitions, and permissions) and associated mechanisms to support the self-regulation of open societies of self-interested and autonomous agents. A key problem in research on normative systems is that of how to synthesise effective and efficient norms. Manually designing norms is time consuming and error prone. An alternative is to automatically synthesise norms. However, norm synthesis is a computationally complex problem. We present a novel online norm synthesis mechanism, designed to synthesise compact normative systems. It yields normative systems composed of concise (simple) norms that effectively coordinate a multiagent system (MAS) without lapsing into overregulation. Our mechanism is based on a central authority that monitors a MAS, searching for undesired states. After detecting undesirable states, the central authority then synthesises norms aimed to avoid them in the future. We demonstrate the effectiveness of our approach through experimental results

CoDesigning Participatory Tools for a New Age: A Proposal for Combining Collective and Artificial Intelligences

In the context of a citizen lab, this article describes how a vanguard of activists, designers, scholars and participation practitioners were involved in a participatory prototyping process. CoGovern was designed as an online participation tool whose focus is to incorporate citizen preferences in local policy making. It is aimed at supporting informed and transparent participatory processes while reducing the ability of sponsoring authorities to 'cherry-pick' policy proposals and avoid providing explanations. This article proposes a decision-making process that incorporates artificial intelligence techniques into a collective decision process and whose result is mainly based on standard optimization techniques rather than vote-counting

Extending NormLab to Spur Research on Norm Synthesis

On-line norm synthesis is a widely used approach to facilitate coordination in MASs. In [2] we introduced NormLab, a computational framework to support research on on-line norm synthesis. That framework provides functionalities to model, simulate and analyse norm synthesis algorithms in an agent-based simulation environment. Here we present several extensions to that work, providing a benchmark for research on norm synthesis in MAS.Work funded by projects AT (CSD2007-0022), COR (TIN2012-38876-C02-01/02), and 2009-SGR-1434. Mike Wooldridge was supported by the ERC under Advanced Grant 291528 (“RACE").Peer reviewe