Scheduling of a class of partial routing FMS in uncertain environments with beam search

2021 ◽  
Author(s):  
G. Cherif ◽  
E. Leclercq ◽  
D. Lefebvre
Keyword(s):  
Beam Search ◽  
Uncertain Environments

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 41-57
Author(s):  
Manisha Mishra ◽  
Pujitha Mannaru ◽  
David Sidoti ◽  
Adam Bienkowski ◽  
Lingyi Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Situational Awareness ◽  
Feature Space ◽  
Human Interaction ◽  
Superior Performance ◽  
Uncertain Environments ◽  
Cognitive States ◽  
Contextual Elements ◽  
Human Operators ◽  
Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

Life-History Evolution in Uncertain Environments: Bet Hedging in Time

2006 ◽  
Vol 168 (3) ◽  
pp. 398
Author(s):  
Wilbur ◽  
Volker H. W. Rudolf
Keyword(s):  
Life History ◽  
Life History Evolution ◽  
Bet Hedging ◽  
Uncertain Environments ◽  
History Evolution

scholarly journals Designing Multi-Agent System Organisations for Flexible Runtime Behaviour

2020 ◽  
Vol 10 (15) ◽  
pp. 5335
Author(s):  
Kathleen Keogh ◽  
Liz Sonenberg
Keyword(s):  
Design Method ◽  
Multi Agent System ◽  
Improve Performance ◽  
Uncertain Environments ◽  
Agent System ◽  
Meta Model ◽  
Design Time ◽  
Organisational Support ◽  
Multi Agent ◽  
Associated Design

We address the challenge of multi-agent system (MAS) design for organisations of agents acting in dynamic and uncertain environments where runtime flexibility is required to enable improvisation through sharing knowledge and adapting behaviour. We identify behavioural features that correspond to runtime improvisation by agents in a MAS organisation and from this analysis describe the OJAzzIC meta-model and an associated design method. We present results from simulation scenarios, varying both problem complexity and the level of organisational support provided in the design, to show that increasing design time guidance in the organisation specification can enable runtime flexibility afforded to agents and improve performance. Hence the results demonstrate the usefulness of the constructs captured in the OJAzzIC meta-model.

scholarly journals Streaming Transformer Asr With Blockwise Synchronous Beam Search

2021 ◽  
Author(s):  
Emiru Tsunoo ◽  
Yosuke Kashiwagi ◽  
Shinji Watanabe
Keyword(s):  
Beam Search

scholarly journals Interacting with volatile environments stabilizes hidden-state inference and its brain signatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Aurélien Weiss ◽  
Valérian Chambon ◽  
Junseok K. Lee ◽  
Jan Drugowitsch ◽  
Valentin Wyart
Keyword(s):  
Cognitive Processes ◽  
Learning Task ◽  
Human Learning ◽  
Sensory Cues ◽  
Uncertain Environments ◽  
Behavioral Difference ◽  
Neural Interaction ◽  
Degree Of Control ◽  
Uncertain Outcomes ◽  
State Inference

AbstractMaking accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalographic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.

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