scholarly journals A computational model of task allocation in social insects – ecology and interactions alone can drive specialisation

2018 ◽  
Author(s):  
Rui Chen ◽  
Bernd Meyer ◽  
Julian García
Keyword(s):  
Computational Model ◽  
Social Interactions ◽  
Task Allocation ◽  
Social Insect ◽  
Group Performance ◽  
Dynamic Environments ◽  
Individual Factors ◽  
Single Task ◽  
Performance Counter ◽  
Ecological Success

AbstractSocial insect colonies are capable of allocating their workforce in a decentralised fashion; addressing a variety of tasks and responding effectively to changes in the environment. This process is fundamental to their ecological success, but the mechanisms behind it remain poorly understood. While most models focus on internal and individual factors, empirical evidence highlights the importance of ecology and social interactions. To address this gap we propose a game theoretical model of task allocation. Individuals are characterised by a trait that determines how they split their energy between two prototypical tasks: foraging and regulation. To be viable, a colony needs to learn to adequately allocate its workforce between these two tasks. We study two different processes: individuals can learn relying exclusively on their own experience, or by using the experiences of others via social learning. We find that social organisation can be determined by the ecology alone, irrespective of interaction details. Weakly specialised colonies in which all individuals tend to both tasks emerge when foraging is cheap; harsher environments, on the other hand, lead to strongly specialised colonies in which each individual fully engages in a single task. We compare the outcomes of self-organised task allocation with optimal group performance. Counter to intuition, strongly specialised colonies perform suboptimally, whereas the group performance of weakly specialised colonies is closer to optimal. Social interactions lead to important differences when the colony deals with dynamic environments. Colonies whose individuals rely on their own experience are more exible when dealing with change. Our computational model is aligned with mathematical predictions in tractable limits. This different kind of model is useful in framing relevant and important empirical questions, where ecology and interactions are key elements of hypotheses and predictions.

A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments

2021 ◽  
Author(s):  
Ching-Wei Chuang ◽  
Harry H. Cheng
Keyword(s):  
Bayesian Networks ◽  
Task Allocation ◽  
Low Cost ◽  
Dynamic Environments ◽  
Modern World ◽  
Robot System ◽  
Novel Approach ◽  
Robot Systems ◽  
Robot Task ◽  
Multi Robot

Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed various algorithms or approaches to solve MRTA problems in different multi-robot systems, it is still difficult to overcome certain challenges, such as dynamic environments, changeable task information, miscellaneous robot abilities, the dynamic condition of a robot, or uncertainties from sensors or actuators. In this paper, we propose a novel approach to handle MRTA problems with Bayesian Networks (BNs) under these challenging circumstances. Our experiments exhibit that the proposed approach may effectively solve real problems in a search-and-rescue mission in centralized, decentralized, and distributed multi-robot systems with real, low-cost robots in dynamic environments. In the future, we will demonstrate that our approach is trainable and can be utilized in a large-scale, complicated environment. Researchers might be able to apply our approach to other applications to explore its extensibility.

Real Time Optimal Task Allocation in Highly Dynamic Environments

2005 ◽  
Author(s):  
David R. Schneider ◽  
Mark Campbell
Keyword(s):  
Real Time ◽  
Task Allocation ◽  
Dynamic Test ◽  
Dynamic Environments ◽  
New Method ◽  
Mixed Integer ◽  
Combined Method ◽  
Test Environment ◽  
Time Optimal ◽  
Minimum Spanning Forest

Of the methods developed for Optimal Task Allocation, Mixed Integer Linear Programming (MILP) techniques are some of the most predominant. A new method, presented in this paper, is able to produce identical optimal solutions to the MILP techniques but in computation times orders of magnitude faster than MILP. This new method, referred to as G*TA, uses a minimum spanning forest algorithm to generate optimistic predictive costs in an A* framework, and a greedy approximation method to create upper bound estimates. A second new method which combines the G*TA and MILP methods, referred to as G*MILP, is also presented for its scaling potential. This combined method uses G*TA to solve a series of sub-problems and the final optimal task allocation is handled through MILP. All of these methods are compared and validated though a large series of real time tests using the Cornell RoboFlag testbed, a multi-robot, highly dynamic test environment.

scholarly journals Comprehension of functional support by enculturated chimpanzees Pan troglodytes

2011 ◽  
Vol 57 (4) ◽  
pp. 429-440 ◽  
Author(s):  
Anna M. Yocom ◽  
Sarah T. Boysen
Keyword(s):  
Social Interactions ◽  
Physical Environment ◽  
Group Performance ◽  
Strong Support ◽  
Superior Performance ◽  
Folk Physics ◽  
Causal Understanding ◽  
Support Relationships ◽  
Active Exploration ◽  
Functional Support

Abstract Studies of causal understanding of tool relationships in captive chimpanzees have yielded disparate findings, particularly those reported by Povinelli & colleagues (2000) for tool tasks by laboratory chimpanzees. The present set of experiments tested nine enculturated chimpanzees on three versions of a support task, as described by Povinelli (2000), during which food rewards were presented in different experimental configurations. In Experiment 1, stimulus pairs included a choice between a cloth with a reward on the upper right corner or with a second reward off the cloth, adjacent to a corner, with the second pair comprised of a cloth with food on the upper right corner, and a second cloth with the reward on the substrate, partially covered. All subjects were successful with both test conditions in Experiment 1. In a second study, the experimental choices included one of two possible correct options, paired with one of three incorrect options, with the three incorrect choices all involving varying degrees of perceptual containment. All nine chimpanzees scored significantly above chance across all six conditions. In Experiment 3, four unique conditions were presented, combining one of two possible correct choices with one of two incorrect choices. Six of the subjects scored significantly above chance across the four conditions, and group performance on individual conditions was also significant. Superior performance was demonstrated by female subjects in Experiment 3, similar to sex differences in tool use previously reported for wild chimpanzees and some tool tasks in captive chimpanzees. The present results for Experiments 2 & 3 were significantly differed from those reported by Povinelli et al. (2000) for laboratory-born, peer-reared chimpanzees. One contribution towards the dramatic differences between the two study populations may be the significant rearing and housing differences of the chimpanzee groups. One explanation is that under conditions of enculturation, rich social interactions with humans and conspecifics, as well as active exploration of artifacts, materials, and other aspects of their physical environment had a significant impact on the animals’ ability to recognize the support relationships among the stimulus choices. Overall, the present findings provide strong support for the hypothesis that our chimpanzee subjects based their responses on an understanding of functional support which represented one facet of their folk physics repertoire.

scholarly journals Asynchronous Decentralized Task Allocation for Dynamic Environments

2011 ◽  
Author(s):  
Luke Johnson ◽  
Sameera Ponda ◽  
Han-Lim Choi ◽  
Jonathan How
Keyword(s):  
Task Allocation ◽  
Dynamic Environments

scholarly journals Performance of small group majorities in a single task

2020 ◽  
Author(s):  
Henrik Olsson
Keyword(s):  
Small Group ◽  
Small Groups ◽  
Deception Detection ◽  
Group Performance ◽  
Psychiatric Morbidity ◽  
Single Task ◽  
Violent Recidivism ◽  
Performance Patterns ◽  
Average Individual ◽  
Proportion Correct

Aggregating decisions from larger groups typically results in outcomes with higher accuracy than decision outcomes from single individuals or smaller groups. Here I argue that it is important to consider not only overall proportion of correct decisions, but also individual competencies in terms of hits (h) and correct rejections (cr). I show that small groups can perform better than randomly selected individuals and larger groups in a single task when the average individual proportion correct is above .5, h and cr are asymmetric around .5, and h+cr>1. If the average individual proportion correct is below .5 and h+cr<1, small groups perform worse than individuals and larger groups. I also demonstrate that these two performance patterns can occur in empirical data from studies on violent recidivism, psychiatric morbidity, anxiety, and deception detection. I also show that the presence of correlations between decisions in a single task has both beneficial and detrimental effects when it comes to small group performance.

scholarly journals Continual Lifelong Learning for Intelligent Agents

2021 ◽  
Author(s):  
Ghada Sokar
Keyword(s):  
Machine Learning ◽  
Intelligent Agents ◽  
Deep Neural Networks ◽  
Dynamic Environments ◽  
Training Data ◽  
Similar Distribution ◽  
Single Task ◽  
Learning Tasks ◽  
New Knowledge ◽  
Real World Applications

Deep neural networks have achieved outstanding performance in many machine learning tasks. However, this remarkable success is achieved in a closed and static environment where the model is trained using large training data of a single task and deployed for testing on data with a similar distribution. Once the model is deployed, it becomes fixed and inflexible to new knowledge. This contradicts real-world applications, in which agents interact with open and dynamic environments and deal with non-stationary data. This Ph.D. research aims to propose efficient approaches that can develop intelligent agents capable of accumulating new knowledge and adapting to new environments without forgetting the previously learned ones.

German senior citizens’ capabilities for physical activity: a qualitative study

2018 ◽  
Vol 34 (6) ◽  
pp. 1117-1129 ◽  
Author(s):  
Alexandra Sauter ◽  
Janina Curbach ◽  
Jana Rueter ◽  
Verena Lindacher ◽  
Julika Loss
Keyword(s):  
Older Adults ◽  
Social Interactions ◽  
Capability Approach ◽  
Senior Citizens ◽  
Close Contact ◽  
Individual Factors ◽  
Active Lifestyle ◽  
Physically Active ◽  
Health Promoting ◽  
Active Lifestyles

Abstract Sen’s capability approach (CA) has found its way into health promotion over the last few years. The approach takes both individual factors as well as social and environmental conditions into account and therefore appears to have great potential to explore opportunities for (‘capabilities’) and barriers to active lifestyles. Thus, our objective in this study was to investigate which capabilities senior citizens perceive to have available to them in order to be physically active. In Southern Germany, we conducted 26 semi-standardized interviews with senior citizens aged 66–97, as well as 9 interviews with key persons who have close contact to senior citizens in their work life. We identified 11 capabilities which the interviewees considered as important in leading an active lifestyle. They could be grouped into four domains: (1) individual resources, (2) social interactions and norms, (3) living conditions and (4) organizational environment. Results highlight the need for health-promoting interventions that widen the range of capabilities on social and environmental levels in a way that individuals can freely choose to be as physically active as they like. The results make clear that interventions should not only target and involve older adults themselves, but also their families, nursing home staff or community representatives, because these groups are important in shaping older adults’ capabilities for an active lifestyle.

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