32,957 research outputs found
An evolutionary model for constructing robust trust networks.
ABSTRACT In reputation systems for multiagent-based e-marketplaces, buying agents model the reputation of selling agents based on ratings shared by other buyers (called advisors). With the existence of unfair rating attacks from dishonest advisors, the effectiveness of reputation systems thus heavily relies on whether buyers can accurately determine which advisors to include in trust networks and their trustworthiness. In this paper, we propose a novel multiagent evolutionary trust model (MET) where each buyer evolves its trust network. In each generation, each buyer acquires trust network information from its advisors and generates a candidate trust network using evolutionary operators. Only trust networks providing more accurate seller reputation estimation shall survive to the next generation. Experimental results demonstrate MET is more robust than the state-ofthe-art trust models against various unfair rating attacks
Agent-Based Computational Economics
Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.
Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems.
Despite the efforts that bioengineers have exerted in designing and constructing biological processes that function according to a predetermined set of rules, their operation remains fundamentally circumstantial. The contextual situation in which molecules and single-celled or multi-cellular organisms find themselves shapes the way they interact, respond to the environment and process external information. Since the birth of the field, synthetic biologists have had to grapple with contextual issues, particularly when the molecular and genetic devices inexplicably fail to function as designed when tested in vivo. In this review, we set out to identify and classify the sources of the unexpected divergences between design and actual function of synthetic systems and analyze possible methodologies aimed at controlling, if not preventing, unwanted contextual issues
Towards the Evolution of Novel Vertical-Axis Wind Turbines
Renewable and sustainable energy is one of the most important challenges
currently facing mankind. Wind has made an increasing contribution to the
world's energy supply mix, but still remains a long way from reaching its full
potential. In this paper, we investigate the use of artificial evolution to
design vertical-axis wind turbine prototypes that are physically instantiated
and evaluated under approximated wind tunnel conditions. An artificial neural
network is used as a surrogate model to assist learning and found to reduce the
number of fabrications required to reach a higher aerodynamic efficiency,
resulting in an important cost reduction. Unlike in other approaches, such as
computational fluid dynamics simulations, no mathematical formulations are used
and no model assumptions are made.Comment: 14 pages, 11 figure
Multi-Fidelity Methods for Optimization: A Survey
Real-world black-box optimization often involves time-consuming or costly
experiments and simulations. Multi-fidelity optimization (MFO) stands out as a
cost-effective strategy that balances high-fidelity accuracy with computational
efficiency through a hierarchical fidelity approach. This survey presents a
systematic exploration of MFO, underpinned by a novel text mining framework
based on a pre-trained language model. We delve deep into the foundational
principles and methodologies of MFO, focusing on three core components --
multi-fidelity surrogate models, fidelity management strategies, and
optimization techniques. Additionally, this survey highlights the diverse
applications of MFO across several key domains, including machine learning,
engineering design optimization, and scientific discovery, showcasing the
adaptability and effectiveness of MFO in tackling complex computational
challenges. Furthermore, we also envision several emerging challenges and
prospects in the MFO landscape, spanning scalability, the composition of lower
fidelities, and the integration of human-in-the-loop approaches at the
algorithmic level. We also address critical issues related to benchmarking and
the advancement of open science within the MFO community. Overall, this survey
aims to catalyze further research and foster collaborations in MFO, setting the
stage for future innovations and breakthroughs in the field.Comment: 47 pages, 9 figure
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