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Analysis of Perceived Helpfulness in Adaptive Autonomous Agent Populations


Publication Type:

Journal article


LNCS Transactions on Computational Collective Intelligence


Adaptive autonomy allows agents to change their autonomy levels based on circumstances, e.g. when they decide to rely upon one another for completing tasks. In this paper, two configurations of agent models for adaptive autonomy are discussed. In the former configuration, the adaptive autonomous behavior is modeled through the willingness of an agent to assist others in the population. An agent that completes a high number of tasks, with respect to a predefined threshold, increases its willingness, and vice-versa. Results show that, agents complete more tasks when they are willing to give help, however the need for such help needs to be low. Agents configured to be helpful will perform well among alike agents. The second configuration extends the first by adding the willingness to ask for help. Furthermore, the perceived helpfulness of the population and of the agent asking for help are used as input in the calculation of the willingness to give help. Simulations were run for three different scenarios. (i) A helpful agent which operates among an unhelpful population, (ii) an unhelpful agent which operates in a helpful populations, and (iii) a population split in half between helpful and unhelpful agents. Results for all scenarios show that, by using such trait of the population in the calculation of willingness and given enough interactions, helpful agents can control the degree of exploitation by unhelpful agents.


@article{Frasheri 4884,
author = {Mirgita Frasheri and Baran {\c{C}}{\"u}r{\"u}kl{\"u} and Mikael Ekstr{\"o}m},
title = {Analysis of Perceived Helpfulness in Adaptive Autonomous Agent Populations},
volume = {10780},
pages = {221--252},
month = {January},
year = {2018},
journal = {LNCS Transactions on Computational Collective Intelligence},
url = {}