Curious Characters for Games in Complex, Dynamic Environments

2009 ◽  
pp. 151-170
Author(s):  
Kathryn E. Merrick ◽  
Mary Lou Maher
Keyword(s):  
Dynamic Environments ◽  
Complex Dynamic

Transforming organizational culture in complex, dynamic environments for safety

2009 ◽  
Author(s):  
Sallie J. Weaver ◽  
Rebecca Lyons ◽  
Eduardo Salas ◽  
David A. Hofmann
Keyword(s):  
Organizational Culture ◽  
Dynamic Environments ◽  
Complex Dynamic

Human Problem Solving in Complex Dynamic Environments

1987 ◽  
Author(s):  
William B. Rouse ◽  
Richard L. Henneman
Keyword(s):  
Problem Solving ◽  
Dynamic Environments ◽  
Complex Dynamic ◽  
Human Problem

scholarly journals Uncertainties in complex dynamic environments

2014 ◽  
Vol Volume 2 ◽  
Author(s):  
Hasmik Atoyan ◽  
Jean-Marc Robert ◽  
Jean-Rémi Duquet
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
User Interfaces ◽  
Dynamic Environments ◽  
Decision Making Process ◽  
Uncertain Information ◽  
Complex Environments ◽  
Complex Dynamic ◽  
Different Types ◽  
Machine Systems

The utilization of Decision Support Systems (DSS) in complex dynamic environments leads the human operator almost inevitably to having to face several types of uncertainties. Thus it is essential for system designers to clearly understand the different types of uncertainties that could exist in human-machine systems of complex environments, to know their impacts on the operator's trust in the systems and decision-making process, and to have guidelines on how to present uncertain information on user interfaces. It is also essential for them to have an overview of the different stages, levels, and types of system automation, and to know their possible impacts on the creation of different types of uncertainties. This paper investigates these topics and aim at helping researchers and practitioners to deal with uncertainties in complex environments.

scholarly journals Stimulus-Driven Orienting of Visuo-Spatial Attention in Complex Dynamic Environments

Neuron ◽  
2011 ◽  
Vol 69 (5) ◽  
pp. 1015-1028 ◽  
Author(s):  
Davide Nardo ◽  
Valerio Santangelo ◽  
Emiliano Macaluso
Keyword(s):  
Spatial Attention ◽  
Dynamic Environments ◽  
Complex Dynamic

scholarly journals Tuning environmental timescales to evolve and maintain generalists

2020 ◽  
Vol 117 (23) ◽  
pp. 12693-12699 ◽  
Author(s):  
Vedant Sachdeva ◽  
Kabir Husain ◽  
Jiming Sheng ◽  
Shenshen Wang ◽  
Arvind Murugan
Keyword(s):  
Neutralizing Antibodies ◽  
Environmental Changes ◽  
Dynamic Environments ◽  
Affinity Maturation ◽  
Natural Environments ◽  
Broadly Neutralizing Antibodies ◽  
Complex Dynamic ◽  
Changing Environments ◽  
Cell Affinity ◽  
Do So

Natural environments can present diverse challenges, but some genotypes remain fit across many environments. Such “generalists” can be hard to evolve, outcompeted by specialists fitter in any particular environment. Here, inspired by the search for broadly neutralizing antibodies during B cell affinity maturation, we demonstrate that environmental changes on an intermediate timescale can reliably evolve generalists, even when faster or slower environmental changes are unable to do so. We find that changing environments on timescales comparable with evolutionary transients in a population enhance the rate of evolving generalists from specialists, without enhancing the reverse process. The yield of generalists is further increased in more complex dynamic environments, such as a “chirp” of increasing frequency. Our work offers design principles for how nonequilibrium fitness “seascapes” can dynamically funnel populations to genotypes unobtainable in static environments.

PLANNING AND REACTING ACROSS SUPERVENIENT LEVELS OF REPRESENTATION

1992 ◽  
Vol 01 (03n04) ◽  
pp. 411-449 ◽  
Author(s):  
LEE SPECTOR ◽  
JAMES HENDLER
Keyword(s):  
Control Systems ◽  
Intelligent Systems ◽  
Hierarchical Control ◽  
Dynamic Environments ◽  
Ai Planning ◽  
Planning Systems ◽  
Complex Dynamic ◽  
Strong Constraint ◽  
External Agents

For intelligent systems to interact with external agents and changing domains, they must be able to perceive and to affect their environments while computing long term projection (planning) of future states. This paper describes and demonstrates the supervenience architecture, a multilevel architecture for integrating planning and reacting in complex, dynamic environments. We briefly review the underlying concept of supervenience, a form of abstraction with affinities both to abstraction in AI planning systems, and to knowledge-partitioning schemes in hierarchical control systems. We show how this concept can be distilled into a strong constraint on the design of dynamic-world planning systems. We then describe the supervenience architecture and an implementation of the architecture called APE (for Abstraction-Partitioned Evaluator). The application of APE to the HomeBot domain is used to demonstrate the capabilities of the architecture.

Adaptation of Robot Skills Models to New Task Contraints

2015 ◽  
Vol 12 (03) ◽  
pp. 1550024 ◽  
Author(s):  
Daniel H. García ◽  
Concepción A. Monje ◽  
Carlos Balaguer
Keyword(s):  
Humanoid Robots ◽  
Dynamic Environments ◽  
Experimental Results ◽  
Robotic Systems ◽  
Learning Approaches ◽  
Learning Mechanisms ◽  
Complex Dynamic ◽  
Skills Learning

Humanoid robots are required to perform a wide repertoire of tasks working beside humans in complex dynamic environments. Learning mechanisms are important for building up these types of repertoires of robot skills. However, despite the clear advantages of these approaches, it would be impractical to teach the robot skills for every needed task and for every foreseen situation. Robot skills learning approaches to develop humanoid robotic systems would have greater impact if the models of the skills can be operated upon to generate new behaviors of increasing levels of complexity. A framework that allows the adaptation of previously learned motion skills to new unseen contexts is necessary. In this work, we present different modalities for the adaptation and generation of new skill models based on the already learned models of the skills. Experimental results are presented to validate this approach.

Augmenting RRT∗-planner with local trees for motion planning in complex dynamic environments

2014 ◽  
Author(s):  
Ahmed Hussain Qureshi ◽  
Saba Mumtaz ◽  
Wajeeha Khan ◽  
Abdul Ahad Ashfaq Sheikh ◽  
Khawaja Fahad Iqbal ◽  
...  
Keyword(s):  
Motion Planning ◽  
Dynamic Environments ◽  
Complex Dynamic
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