182,554 research outputs found

    Corporate payments networks and credit risk rating

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    Aggregate and systemic risk in complex systems are emergent phenomena depending on two properties: the idiosyncratic risks of the elements and the topology of the network of interactions among them. While a significant attention has been given to aggregate risk assessment and risk propagation once the above two properties are given, less is known about how the risk is distributed in the network and its relations with the topology. We study this problem by investigating a large proprietary dataset of payments among 2.4M Italian firms, whose credit risk rating is known. We document significant correlations between local topological properties of a node (firm) and its risk. Moreover we show the existence of an homophily of risk, i.e. the tendency of firms with similar risk profile to be statistically more connected among themselves. This effect is observed when considering both pairs of firms and communities or hierarchies identified in the network. We leverage this knowledge to show the predictability of the missing rating of a firm using only the network properties of the associated node

    Building Machines That Learn and Think Like People

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    Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

    Model checking multi-agent systems

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    A multi-agent system (MAS) is usually understood as a system composed of interacting autonomous agents. In this sense, MAS have been employed successfully as a modelling paradigm in a number of scenarios, especially in Computer Science. However, the process of modelling complex and heterogeneous systems is intrinsically prone to errors: for this reason, computer scientists are typically concerned with the issue of verifying that a system actually behaves as it is supposed to, especially when a system is complex. Techniques have been developed to perform this task: testing is the most common technique, but in many circumstances a formal proof of correctness is needed. Techniques for formal verification include theorem proving and model checking. Model checking techniques, in particular, have been successfully employed in the formal verification of distributed systems, including hardware components, communication protocols, security protocols. In contrast to traditional distributed systems, formal verification techniques for MAS are still in their infancy, due to the more complex nature of agents, their autonomy, and the richer language used in the specification of properties. This thesis aims at making a contribution in the formal verification of properties of MAS via model checking. In particular, the following points are addressed: • Theoretical results about model checking methodologies for MAS, obtained by extending traditional methodologies based on Ordered Binary Decision Diagrams (OBDDS) for temporal logics to multi-modal logics for time, knowledge, correct behaviour, and strategies of agents. Complexity results for model checking these logics (and their symbolic representations). • Development of a software tool (MCMAS) that permits the specification and verification of MAS described in the formalism of interpreted systems. • Examples of application of MCMAS to various MAS scenarios (communication, anonymity, games, hardware diagnosability), including experimental results, and comparison with other tools available

    Machine Ethics, Ethics for Machines: Context-Based Modeling for Machines Making Ethical Decisions

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    Machine ethics is an emerging, interdisciplinary field that focuses on if – and if so, how – machines can make ethical decisions autonomously. Through a close study of two positions on whether or not machines can be moral agents, this project sheds light on a clash of assumptions that is keeping the field of machine ethics in limbo. After making this clash of assumptions clear, I raise two questions which get at the scope of machine ethics itself: 1) What makes ethical decision-making different from other kinds of decision-making? 2) To what extent can machines engage with ethics and make ethical decisions? I address the first question by arguing that ethics is distinct because it requires the ability to understand and participate in human conventions. I address the second question by arguing that ethics has always been informed by our humanity, but machine ethics is an opportunity to expand our understanding of ethics so that machines can engage with it insofar as they are machines. This project aims to contribute to machine ethics by proposing a major shift in perspective, from a focus on human abilities to a focus on machines and their own radically novel abilities

    Enabling Modular Autonomous Feedback-Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

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    Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high-throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein

    Dynamic networks of heterogeneous timed machines

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    International audienceWe present an algebra of discrete timed input/output automata that may execute in the context of different clock granularities – which we call timed machines; this algebra includes a refinement operator through which a machine can be extended with new states and transitions in order to accommodate a finer clock granularity as required to interoperate with other machines, and an extension of the traditional product of timed input–output automata to the situation in which the granularities of the two machines are not the same. Over this algebra, we then define an algebra of networks of timed machines that includes operations through which networks can be modified at run time, thus offering a model for systems of interconnected components that can dynamically bind to other systems and, therefore, cannot be adjusted at design time to ensure that they operate in a timed homogeneous setting. We investigate important properties of timed machines such as consistency – in the sense that a machine can be ensured to generate a non-empty language, and feasibility – in the sense that a machine can be ensured to generate a non-empty language no matter what inputs it receives, and propose techniques for checking if timed machines are consistent or are feasible. We generalise those properties to networks of timed machines, and investigate how consistency and feasibility of networks can be proved through properties that can be checked at design time without having to compute, at run time, the product of the machines that operate on those networks, which would not be practical

    Design and Analysis of a Logless Dynamic Reconfiguration Protocol

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    Distributed replication systems based on the replicated state machine model have become ubiquitous as the foundation of modern database systems. To ensure availability in the presence of faults, these systems must be able to dynamically replace failed nodes with healthy ones via dynamic reconfiguration. MongoDB is a document oriented database with a distributed replication mechanism derived from the Raft protocol. In this paper, we present MongoRaftReconfig, a novel dynamic reconfiguration protocol for the MongoDB replication system. MongoRaftReconfig utilizes a logless approach to managing configuration state and decouples the processing of configuration changes from the main database operation log. The protocol's design was influenced by engineering constraints faced when attempting to redesign an unsafe, legacy reconfiguration mechanism that existed previously in MongoDB. We provide a safety proof of MongoRaftReconfig, along with a formal specification in TLA+. To our knowledge, this is the first published safety proof and formal specification of a reconfiguration protocol for a Raft-based system. We also present results from model checking its safety properties on finite protocol instances. Finally, we discuss the conceptual novelties of MongoRaftReconfig, how it can be understood as an optimized and generalized version of the single server reconfiguration algorithm of Raft, and present an experimental evaluation of how its optimizations can provide performance benefits for reconfigurations.Comment: 35 pages, 2 figure
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