Social satisficing: Multi-agent reinforcement learning with satisficing agents.

For a reinforcement learning agent to finish trial-and-error in a realistic time duration, it is necessary to limit the scope of exploration during the learning process. However, limiting the exploration scope means limitation in optimality: the agent could fall into a suboptimal solution. This is the nature of local, bottom-up way of learning.

Computational Power of Asynchronously Tuned Automata Enhancing the Unfolded Edge of Chaos.

Asynchronously tuned elementary cellular automata (AT-ECA) are described with respect to the relationship between active and passive updating, and that spells out the relationship between synchronous and asynchronous updating. Mutual tuning between synchronous and asynchronous updating can be interpreted as the model for dissipative structure, and that can reveal the critical property in the phase transition from order to chaos. Since asynchronous tuning easily makes behavior at the edge of chaos, the property of AT-ECA is called the unfolded edge of chaos.

Breaking of the Trade-Off Principle between Computational Universality and Efficiency by Asynchronous Updating.

Although natural and bioinspired computing has developed significantly, the relationship between the computational universality and efficiency beyond the Turing machine has not been studied in detail. Here, we investigate how asynchronous updating can contribute to the universal and efficient computation in cellular automata (CA). First, we define the computational universality and efficiency in CA and show that there is a trade-off relation between the universality and efficiency in CA implemented in synchronous updating.

Robotic action acquisition with cognitive biases in coarse-grained state space.

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.

Multilayered neural network with structural lateral inhibition for incremental learning and conceptualization.

Distributed connectionist networks have difficulty learning incrementally because the representations in the network overlap. Therefore, it is necessary to reduce the overlaps of representations for incremental learning. At the same time, the representational overlaps give these networks the ability to generalize.

Cognitively inspired reinforcement learning architecture and its application to giant-swing motion control.

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).