Cognitive biases explaining human deviation from formal logic have been broadly studied. We here try to give a step toward the general formalism still missing, introducing a probabilistic formula for causal induction. It has symmetries reflecting human cognitive biases and shows extremely high correlation with the experimental results. We apply the formula… (More)
Some of the authors have previously proposed a cognitively inspired reinforcement learning architecture (LS-Q) that mimics cognitive biases in humans. LS-Q adaptively learns under uniform, coarse-grained state division and performs well without parameter tuning in a giant-swing robot task. However, these results were shown only in simulations. In this… (More)
Traffic jam caused by self-propelled particles is a research topic of broad interest. Cellular automata models are used for the simulation and analysis. One of the popular models is the floor field model. We add the rule of avoiding high density among particles to the model and show that it enables simulating a more realistic situation.
Many algorithms and methods in artificial intelligence or machine learning were inspired by human cognition. As a mechanism to handle the exploration-exploitation dilemma in reinforcement learning, the loosely symmetric (LS) value function that models causal intuition of humans was proposed (Shinohara et al., 2007). While LS shows the highest correlation… (More)
In an uncertain environment, decision-making meets two opposing demands. One is to explore new information, while the other is to exploit already acquired information. The opposition is long called the exploration-exploitation dilemma. In brain science, it is known that human brain estimates options comparatively, and the average behavior correlates to the… (More)