International Journal of Man-Machine Studies
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Knowledge Representation in Fuzzy Logic
IEEE Transactions on Knowledge and Data Engineering
Brain tumor characterization using the soft computing technique of fuzzy cognitive maps
Applied Soft Computing
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Fuzzy cognitive map architectures for medical decision support systems
Applied Soft Computing
Advanced soft computing diagnosis method for tumour grading
Artificial Intelligence in Medicine
Creating an Immersive Game World with Evolutionary Fuzzy Cognitive Maps
IEEE Computer Graphics and Applications
Robotic test bed for autonomous surface exploration of Titan, Mars, and other planetary bodies
AERO '11 Proceedings of the 2011 IEEE Aerospace Conference
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Future science-driven landing missions, conceived to collect in situ data on regions of planetary bodies that have the highest potential to yield important scientific discoveries, will require a higher degree of autonomy. The latter includes the ability of the spacecraft to autonomously select the landing site using real-time data acquired during the descent phase. This paper presents the development of an Evolutionary Fuzzy Cognitive Map (E-FCM) model that implements an artificial intelligence system capable of autonomously selecting a landing site with the highest potential for scientific discoveries constrained by the requirement of soft landing in a region with safe terrains. The proposed E-FCM evolves its internal states and interconnections as a function of real-time data collected during the descent phase, therefore improving the decision process as more accurate information becomes available. The E-FCM is constructed using knowledge accumulated by planetary experts and it is tested on scenarios that simulate the decision process during the descent phase toward the Hyndla Regio on Venus. The E-FCM is shown to quickly reach conclusions that are consistent with what would be the choice of a planetary expert if the scientist were presented with the same information. The proposed methodology is fast and efficient and may be suitable for on-board spacecraft implementation and real-time decision making during the course of robotic exploration of the Solar System.