Journal d'informatique et de biologie des systèmes

Multi-Agent Systems and Distributed Constraint Satisfaction for Decision Support in Marine Ecosystem Management

Abstract

Ngisiange NN, Rimiru R, Okeyo G, Wambiji N and Aura C

Real-world problems can be formulated as distributed constraint satisfaction problems. Marine resources are subject to certain constraints relating to their physical design, their interactions and legal requirements. Decision making is a major problem since the resource management is distributed and threatened by socio economic activities and environmental factors. A target species with high consumption demand (rabbitfish) is modelled using a system dynamic model builder as a prey agent of the system and predators as the predator agents, other agents including primary production and fishing and aggregation gears are also used. Primary productivity and Predators through aggregation, exploitation and predation affects the population dynamics of the prey agent. Modelling and simulation helps increase the understanding of the behaviour of the prey fish and help explore the potential effect of different management scenario on the exposure of the prey fish to such constraint violation. This study proposes a multi-agent system (MAS) model simulated to explore the impact of different management decision strategies on a marine ecosystems management problem involving several environmental agents. Focussing on the multiple agents within a dynamic environment facing several distributed environmental constraints. A population growth curve is used to identify the initial state (problem) of the marine ecosystem based on available data and how different decision strategies affect the state as they are implemented (problem solving using constraints). A simulation toolkit (Netlogo V5) is used to model different environmental dynamics, interactions and constraints satisfaction to realise an increase in population for the target species. The proposed model examines distributed data among different government agencies databases and publications. The proposed model is analysed and produce results confirming improved decision support through the use of multi agent systems and distributed constraint satisfaction.