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Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Woc-Bots: An Agent-Based Approach to Decision-Making
Applied sciences, v 9(21), p4653
01 Nov 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We present a flexible, robust approach to predictive decision-making using simple, modular agents (WoC-Bots) that interact with each other socially and share information about the features they are trained on. Our agents form a knowledge-diverse crowd, allowing us to use Wisdom of the Crowd (WoC) theories to aggregate their opinions and come to a collective conclusion. Compared to traditional multi-layer perceptron (MLP) networks, WoC-Bots can be trained more quickly, more easily incorporate new features, and make it easier to determine why the network gives the prediction that it does. We compare our predictive accuracy with MLP networks to show that WoC-Bots can attain similar results when predicting the box office success of Hollywood movies, while requiring significantly less training time.
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Details
- Title
- Woc-Bots: An Agent-Based Approach to Decision-Making
- Creators
- Sean Grimes - Drexel UniversityDavid E. Breen - Drexel University
- Publication Details
- Applied sciences, v 9(21), p4653
- Publisher
- Mdpi
- Number of pages
- 14
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000498058600178
- Scopus ID
- 2-s2.0-85075206985
- Other Identifier
- 991019167859904721
UN Sustainable Development Goals (SDGs)
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Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Chemistry, Multidisciplinary
- Engineering, Multidisciplinary
- Materials Science, Multidisciplinary
- Physics, Applied