Modeling performance and information exchange between fishing vessels with artificial neural networks

A fishery is simulated in which 20 artificial vessels learn to make decisions through an artificial neural network in order to search for schools of fish among the available fishing grounds. Three scenarios with different degrees of variability, including uncertainty in the searching process, are considered. The simulation model accounts for the main features commonly observed in a purse seine tuna fishery in a time and a space scale. Vessel strategies are chosen by the artificial neural network, on the basis of the following decision criteria: information concerning time searching in a specific area, previous performance in this area, knowledge of the quality of surrounding fishing grounds, presence of other vessels fishing actively and trip length. An analysis of the effects of sharing information between vessels is done and this was compared to individual artificial fishing vessels. In general, a group of fishing vessels show higher performance than individual vessels. A convex performance comparison curve for several group sizes is found in all scenarios considered. The optimum group size differs according to the variability of the artificial world. At bigger group sizes performance decreases, probably due to competition and depletion effects of some fishing grounds.

[1]  Peter J. Allen,et al.  Dynamics of discovery and exploitation: the case of the Scotian Shelf groundfish fisheries. , 1986 .

[2]  S. Jørgensen,et al.  Movement rules for individual-based models of stream fish , 1999 .

[3]  Daniel Gaertner,et al.  Influence of fishers’ behaviour on the catchability of surface tuna schools in the Venezuelan purse-seiner fishery in the Caribbean Sea , 1999 .

[4]  J.A.E. van Oostenbrugge,et al.  Risk aversion in allocating fishing effort in a highly uncertain coastal fishery for pelagic fish, Moluccas, Indonesia , 2001 .

[5]  Michel Dreyfus-León,et al.  Individual-based modelling of fishermen search behaviour with neural networks and reinforcement learning , 1999 .

[6]  Andrea Bonarini,et al.  Anytime Learning and Adaptation of Structured Fuzzy Behaviors , 1997, Adapt. Behav..

[7]  Carl J. Walters,et al.  A General Model for Simulation of Stock and Fleet Dynamics in Spatially Heterogeneous Fisheries , 1987 .

[8]  Randall M. Peterman,et al.  Movement Dynamics in a Fishery: Application of the Ideal Free Distribution to Spatial Allocation of Effort , 1993 .

[9]  Marc Mangel,et al.  Search and Stock Depletion: Theory and Applications , 1985 .

[10]  Michel Dreyfus-León,et al.  A spatial individual behaviour-based model approach of the yellowfin tuna fishery in the eastern Pacific Ocean , 2001 .

[11]  S. Salas,et al.  Fishing strategies of small-scale fishers and their implications for fisheries management , 2000 .

[12]  David B. Sampson,et al.  Fishing tactics and fish abundance, and their influence on catch rates , 1991 .

[13]  A. D. Rijnsdorp,et al.  Competitive interactions among beam trawlers exploiting local patches of flatfish in the North Sea , 2000 .

[14]  E. F. Edwards,et al.  Effects of Nonrandomness on Line Transect Estimates of Dolphin School Abundance , 2004 .

[15]  C. Clark,et al.  Uncertainty, search, and information in fisheries , 1983 .

[16]  Long Ji Lin,et al.  Reinforcement Learning of Non-Markov Decision Processes , 1995, Artif. Intell..

[17]  Pattie Maes,et al.  Behavior-based artificial intelligence , 1993 .

[18]  James E. Wilen,et al.  An Examination of Fishing Location Choice in the Pink Shrimp Fishery , 1986, Marine Resource Economics.