Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
IEEE Computational Intelligence Magazine
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ant colony optimization for routing and load-balancing: survey and new directions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Peer review, our current system for determining which papers to accept for journals and conferences, has limitations that impair the quality of scientific communication. Under the current system, each paper receives an equal amount of attention regardless of how good the paper is. We propose to implement a new system for conference peer review based on ant colony optimization (ACO) algorithms. In our model, each reviewer has a set of ants that goes out and finds articles. The reviewer assesses the paper that the ant brings and the reviewer's ants deposit pheromone that is proportional to the quality of the review. Subsequent ants select the next article based on pheromone strength. We used an agent-based model to determine that an ACO-based paper selection system will direct reviewers' attention to the best articles and correctly rank them based on the papers' quality.