UML 2001: a standardization odyssey
Communications of the ACM
Enhancing multi-agent based simulation with human-like decision making strategies
MABS 2000 Proceedings of the second international workshop on Multi-agent based simulation
Cognitive schema and naturalistic decision making in evidence-based practices
Journal of Biomedical Informatics
Extending the recognition-primed decision model to support human-agent collaboration
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Editorial: Computationally intelligent agents in economics and finance
Information Sciences: an International Journal
International Journal of Intelligent Systems
A fuzzy logic-based computational recognition-primed decision model
Information Sciences: an International Journal
Abducing chances in hybrid humans as decision makers
Information Sciences: an International Journal
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Human–Agent Collaboration for Time-Stressed Multicontext Decision Making
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Information Technology in Biomedicine
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Analytical decision making strategies rely on weighing pros and cons of multiple options in an unbounded rationality manner. Contrary to these strategies, recognition primed decision (RPD) model which is a primary naturalistic decision making (NDM) approach assumes that experienced and professional decision makers when encounter problems in real operating conditions are able to use their previous experiences and trainings in order to diagnose the problem, recall the appropriate solution, evaluate it mentally, and implement it to handle the problem in a satisficing manner. In this paper, a computational form of RPD, now called C-RPD, is presented. Unified Modeling Language was used as a modeling language to represent the proposed C-RPD model in order to make the implementation easy and obvious. To execute the model, RoboCup Rescue agent simulation environment, which is one of the best and the most famous complex and multi-agent large-scale environments, was selected. The environment simulates the incidence of fire and earthquakes in urban areas where it is the duty of the police forces, firefighters and ambulance teams to control the crisis. Firefighters of SOS team are first modeled and implemented by utilizing C-RPD and then the system is trained using an expert's experience. There are two evaluations. To find out the convergence of different versions developed during experience adding, some of the developed versions are chosen and evaluated on seven maps. Results show performance improvements. The SOS team ranked first in an official world championship and three official open tournaments.