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Publisher: International Institute of Informatics and Cybernetics
Journal: Journal of Systemics
Languages: English
Types: Article
Subjects: DOAJ:Computer Science, Artificial Immune System (ais), Electronic computers. Computer science, Reinforcement Learning (rl), Science, P87-96, Computer Science - Robotics, Idiotypic Network Theory, Robot Controller, Mathematics, Behaviour Arbitration, Information technology, Communication. Mass media, QA75.5-76.95, Q, Instruments and machines, DOAJ:Technology and Engineering, QA1-939, QA71-90, Computer Science - Neural and Evolutionary Computing, T58.5-58.64, Computer Science - Artificial Intelligence
Previous work has shown that robot navigation systems that\ud employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control\ud techniques that rely on reinforcement learning only. This is\ud thought to be a result of intelligent behaviour selection on the part of the idiotypic robot. In this paper an attempt is made to imitate idiotypic dynamics by creating controllers that use reinforcement with a number of different probabilistic schemes to select robot behaviour. The aims are to show that the idiotypic system is not merely performing some kind of periodic random behaviour selection, and to try to gain further insight into the processes that govern the idiotypic mechanism. Trials\ud are carried out using simulated Pioneer robots that undertake navigation exercises. Results show that a scheme that boosts the probability of selecting highly-ranked alternative behaviours to 50% during stall conditions comes closest to achieving the properties of the idiotypic system, but remains unable to match it in terms of all round performance.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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