Adaptive Load Balancing: A Study in Multi-Agent Learning

We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We first define a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay between basic adaptive behavior parameters and their effect on system efficiency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, we show that naive use of communication may not improve, and might even harm system efficiency.Comment: See http://www.jair.org/ for any accompanying file

Constraint rule-based programming of norms for electronic institutions

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Association between sensory impairment and suicidal ideation and attempt: a cross-sectional analysis of nationally representative English household data

OBJECTIVES: Sensory impairments are associated with worse mental health and poorer quality of life, but few studies have investigated whether sensory impairment is associated with suicidal behaviour in a population sample. We investigated whether visual and hearing impairments were associated with suicidal ideation and attempt. DESIGN: National cross-sectional study. SETTING: Households in England. PARTICIPANTS: We analysed data for 7546 household residents in England, aged 16 and over from the 2014 Adult Psychiatric Morbidity Survey. EXPOSURES: Sensory impairment (either visual or hearing), Dual sensory impairment (visual and hearing), visual impairment, hearing impairment. PRIMARY OUTCOME: Suicidal ideation and suicide attempt in the past year. RESULTS: People with visual or hearing sensory impairments had twice the odds of past-year suicidal ideation (OR 2.06; 95% CI 1.17 to 2.73; p<0.001), and over three times the odds of reporting past-year suicide attempt (OR 3.12; 95% CI 1.57 to 6.20; p=0.001) compared with people without these impairments. Similar results were found for hearing and visual impairments separately and co-occurring. CONCLUSIONS: We found evidence that individuals with sensory impairments are more likely to have thought about or attempted suicide in the past year than individuals without

The Emergence of Norms via Contextual Agreements in Open Societies

This paper explores the emergence of norms in agents' societies when agents play multiple -even incompatible- roles in their social contexts simultaneously, and have limited interaction ranges. Specifically, this article proposes two reinforcement learning methods for agents to compute agreements on strategies for using common resources to perform joint tasks. The computation of norms by considering agents' playing multiple roles in their social contexts has not been studied before. To make the problem even more realistic for open societies, we do not assume that agents share knowledge on their common resources. So, they have to compute semantic agreements towards performing their joint actions. %The paper reports on an empirical study of whether and how efficiently societies of agents converge to norms, exploring the proposed social learning processes w.r.t. different society sizes, and the ways agents are connected. The results reported are very encouraging, regarding the speed of the learning process as well as the convergence rate, even in quite complex settings

Variability Abstraction and Refinement for Game-Based Lifted Model Checking of Full CTL

One of the most promising approaches to fighting the configuration space explosion problem in lifted model checking are variability abstractions. In this work, we define a novel game-based approach for variability-specific abstraction and refinement for lifted model checking of the full CTL, interpreted over 3-valued semantics. We propose a direct algorithm for solving a 3-valued (abstract) lifted model checking game. In case the result of model checking an abstract variability model is indefinite, we suggest a new notion of refinement, which eliminates indefinite results. This provides an iterative incremental variability-specific abstraction and refinement framework, where refinement is applied only where indefinite results exist and definite results from previous iterations are reused. The practicality of this approach is demonstrated on several variability models