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A New Approach for Learning User Preferences for a Ridesharing Application Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Analysis of Perceived Helpfulness in Adaptive Autonomous Agent Populations

Authors : Mirgita Frasheri, Baran Çürüklü, Mikael Ekström

Published in: Transactions on Computational Collective Intelligence XXVIII

Publisher: Springer International Publishing

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Abstract

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.

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Metadata
Title
Analysis of Perceived Helpfulness in Adaptive Autonomous Agent Populations
Authors
Mirgita Frasheri
Baran Çürüklü
Mikael Ekström
Copyright Year
2018
Book
Transactions on Computational Collective Intelligence XXVIII

Print ISBN: 978-3-319-78300-0

Electronic ISBN: 978-3-319-78301-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-78301-7_10

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