In this paper, genetic algorithms for multi-objective optimization problems with uncertainty, which attract attention for applications to simulation-based and experiment-based optimization of real systems, are discussed. First, difficulties faced by conventional multi-objective GAs in their application to multi-objective optimization of noisy fitness functions are described. Second, to cope with these problems, a multi-objective GA that has a fitness estimation method and a new selection operator is proposed. The effectiveness of the proposed method is demonstrated by numerical simulations and real-world experiments.
Recently, Wang and Ray introduced a signed measure for formal languages, called a language measure, to evaluate performance of strings generated by discrete event systems and a synthesis method of an optimal supervisor based on the language measure has been studied. In order to apply the method, exact information about a language measure of a controlled discrete event system is needed. From the practical point of view, it is not always possible to know the information a priori. In such a situation, a learning-based approach is useful to obtain an optimal supervisor with respect to the language measure. This paper considers a synthesis method of an optimal supervisor with respect to a language measure. First, we clarify the relationship between the Bellman equation in reinforcement learning and performance of the language generated by the controlled discrete event systems. Next, using the relationship, we propose a learning method of the optimal supervisor where costs of disabling events are taken into consideration. Finally, by computer simulation, we illustrate an efficiency of the proposed method.
This paper discusses a relationship between symbol emergence and embodiment. Symbol emergence is an alternative approach to overcome symbol grounding problem. We believe human beings' symbols constructed and held internally are originally based on our own embodiment. Therefore, autonomous social robots will also have to construct their own symbols by themselves. In addition to this, we have a nonlinear hypothesis about symbol emergence. To realize symbol emergence inside of autonomous robots, we propose Dual-Schemata model. This model enables an autonomous robot to generate its own symbols, called perceptional schemata, depending on its embodiment. In the end, we show that this symbol emergence has a nonlinear relationship with embodiment.
In this paper we discuss a system design method that involves the dynamic separation of interaction. Multi-agent systems, which model the ubiquitous computing environment where elements behave autonomously, are used as the experimental platform to compare the proposed method with traditional ones. The results show the effectiveness of this design method for systems including chaos.
Recently, as performance of computers improves, it is possible to simulate larger-scale and more detailed traffic flows. In this paper, a road traffic system is modeled as a hierarchical autonomous decentralized system by constructing three kinds of the model which correspond to roads, vehicles and drivers respectively. By modeling the vehicles and drivers separately, it is possible to consider independently a physical dynamics of the system and issues related to information and decision makings. By comparing the results of simulation using the proposed model with those obtained by the Underwood model, the validity of the proposed model has been confirmed. Moreover, the typical phenomenon in actual traffic flows in sag has been also observed in the simulation. Finally, as an example of the utilization of the simulation model, the effects of the introduction of speed limit on traffic flows have been examined.