Title: Neuroevolution of a multi-agent system for the dynamic pickup and delivery problem
Authors: Merlevede, Jonathan
van Lon, Rinde RS
Holvoet, Tom
Issue Date: May-2014
Conference: International Joint Workshop on Optimisation in Multi-Agent Systems and Distributed Constraint Reasoning (co-located with AAMAS) location:Paris, France date:May 2014
Article number: 4
Abstract: Automated transportation and logistics create particularly challenging problems for planners and schedulers. Besides their computational hardness, such systems need to cope with the dynamic and distributed nature of the problems. This article describes an agent-based approach to the dynamic pickup and delivery problem (PDP). We investigate the feasibility of using the neuroevolution of augmenting topologies (NEAT) algorithm to create and optimize a multi-agent system (MAS) for dynamic PDPs. A thorough feasibility study requires a significant effort since a platform is required that facilitates a comparison of MAS and centralized algorithms. We implemented an existing benchmark dataset for the dynamic pickup and delivery problem with time windows (PDPTW) in the RinSim multi-agent simulator. We supplied the NEAT algorithm with training data derived from this dataset and we deployed the resulting neural network in a homogenous MAS that uses a blackboard coordination model. Our preliminary results show that our approach is a double-edged sword, the resulting MAS responds in realtime (response time in ms) but the solution quality is worse compared to that of the benchmark dataset.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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