Woc-Bots: An Agent-Based Approach to Decision-Making

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.

Special Issue “Multi-Agent Systems”: Editorial

“…In [5], an MAS is proposed as a platform for instrumenting a collective of neural network based classifiers by adopting a crowdsourcing metaphor: each classifier is an agent, each classification is an opinion, and the overall prediction delivered by the system is the aggregation of the crowd's opinions. The goal is to improve prediction accuracy and transparency, by letting agents interact socially to exchange knowledge (e.g., new features), gain reciprocal trust, and change opinion when given enough evidence.…”

“…In spite of the heterogeneity of the application domains and the techniques adopted, all the described approaches leverage on MAS central notions to improve delivering of decision support functionalities, either by simulation [3,4] or as an operational platform [5,6].…”