Towards a conjoint-based, context-dependent model of task allocation in activity settings: Some numerical experiments

2001 ◽  
Vol 3 (4) ◽  
pp. 347-367
Author(s):  
Aloys Borgers ◽  
Frank Hofman ◽  
Maarten Ponjé ◽  
Harry Timmermans
Keyword(s):  
Task Allocation ◽  
Numerical Experiments ◽  
Activity Settings ◽  
Dependent Model ◽  
Context Dependent

scholarly journals The role of kinship in knowledge about medicinal plants: evidence for context-dependent model-based biases in cultural transmission?

2019 ◽  
Vol 33 (2) ◽  
pp. 370-375 ◽  
Author(s):  
Camilla de Carvalho de Brito ◽  
Washington Soares Ferreira-Júnior ◽  
Ulysses Paulino Albuquerque ◽  
Marcelo Alves Ramos ◽  
Taline Cristina da Silva ◽  
...  
Keyword(s):  
Medicinal Plants ◽  
Cultural Transmission ◽  
Model Based ◽  
Dependent Model ◽  
Context Dependent

scholarly journals Context-dependent expression of the foraging gene in field colonies of ants: the interacting roles of age, environment and task

2016 ◽  
Vol 283 (1837) ◽  
pp. 20160841 ◽  
Author(s):  
Krista K. Ingram ◽  
Deborah M. Gordon ◽  
Daniel A. Friedman ◽  
Michael Greene ◽  
John Kahler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Task Allocation ◽  
Molecular Mechanisms ◽  
Light Exposure ◽  
Genetic Regulation ◽  
Behavioural Plasticity ◽  
Harvester Ants ◽  
Ontogenetic Changes ◽  
Context Dependent ◽  
And Task

Task allocation among social insect workers is an ideal framework for studying the molecular mechanisms underlying behavioural plasticity because workers of similar genotype adopt different behavioural phenotypes. Elegant laboratory studies have pioneered this effort, but field studies involving the genetic regulation of task allocation are rare. Here, we investigate the expression of the foraging gene in harvester ant workers from five age- and task-related groups in a natural population, and we experimentally test how exposure to light affects foraging expression in brood workers and foragers. Results from our field study show that the regulation of the foraging gene in harvester ants occurs at two time scales: levels of foraging mRNA are associated with ontogenetic changes over weeks in worker age, location and task, and there are significant daily oscillations in foraging expression in foragers. The temporal dissection of foraging expression reveals that gene expression changes in foragers occur across a scale of hours and the level of expression is predicted by activity rhythms: foragers have high levels of foraging mRNA during daylight hours when they are most active outside the nests. In the experimental study, we find complex interactions in foraging expression between task behaviour and light exposure. Oscillations occur in foragers following experimental exposure to 13 L : 11 D (LD) conditions, but not in brood workers under similar conditions. No significant differences were seen in foraging expression over time in either task in 24 h dark (DD) conditions. Interestingly, the expression of foraging in both undisturbed field and experimentally treated foragers is also significantly correlated with the expression of the circadian clock gene, cycle . Our results provide evidence that the regulation of this gene is context-dependent and associated with both ontogenetic and daily behavioural plasticity in field colonies of harvester ants. Our results underscore the importance of assaying temporal patterns in behavioural gene expression and suggest that gene regulation is an integral mechanism associated with behavioural plasticity in harvester ants.

Detection of Subtle Context-Dependent Model Inaccuracies in High-Dimensional Robot Domains

Big Data ◽  
2016 ◽  
Vol 4 (4) ◽  
pp. 269-285 ◽  
Author(s):  
Juan Pablo Mendoza ◽  
Reid Simmons ◽  
Manuela Veloso
Keyword(s):  
High Dimensional ◽  
Dependent Model ◽  
Context Dependent

scholarly journals Detection and correction of subtle context-dependent robot model inaccuracies using parametric regions

2019 ◽  
Vol 38 (8) ◽  
pp. 887-909
Author(s):  
Juan Pablo Mendoza ◽  
Reid Simmons ◽  
Manuela Veloso
Keyword(s):  
Autonomous Robots ◽  
Complex Environments ◽  
Contextual Model ◽  
Intelligent Decision Making ◽  
Multiple Observations ◽  
Intelligent Decision ◽  
Dependent Model ◽  
Real Robot ◽  
Context Dependent ◽  
Planning Space

Autonomous robots frequently rely on models of their sensing and actions for intelligent decision making. Unfortunately, in complex environments, robots are bound to encounter situations in which their models do not accurately represent the world. Furthermore, these context-dependent model inaccuracies may be subtle, such that multiple observations may be necessary to distinguish them from noise. This paper formalizes the problem of detection and correction of such subtle contextual model inaccuracies in autonomous robots, and presents an algorithm to address this problem. The solution relies on reasoning about these contextual inaccuracies as parametric regions of inaccurate modeling (RIMs) in the robot’s planning space. Empirical results from various real robot domains demonstrate that, by explicitly searching for RIMs, robots are capable of efficiently detecting subtle contextual model inaccuracies, which in turn can lead to task performance improvement.

Modeling Learning and Evolutionary Adaptation Processes in Activity Settings: Theory and Numerical Simulations

2000 ◽  
Vol 1718 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Harry Timmermans ◽  
Theo Arentze ◽  
C. H. Joh
Keyword(s):  
Conceptual Framework ◽  
Numerical Experiments ◽  
Activity Settings ◽  
Activity Choice ◽  
State Dependent ◽  
Dependent Variables ◽  
Activity Behavior ◽  
Adaptation Processes ◽  
Principles Of Learning

The development of a conceptual framework to build a model of multi-faceted choices underlying activity behavior is described. The conceptual framework views individuals developing stereotype behavior or scripts over time by learning. Various principles of learning as a function of state-dependent variables and as a function of latent behavior and adjustment principles are developed and formalized. The focus is on modeling long-term dynamics in activity choice heuristics. Theory, illustrated by a set of numerical experiments, is developed.

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