Multiagent Systems with Symbiotic Learning and Evolution (Masbiole) has been proposed and studied, which is a new methodology of Multiagent Systems (MAS) based on symbiosis in the ecosystem. Masbiole employs a method of symbiotic learning and evolution where agents can learn or evolve according to their symbiotic relations toward others, i.e., considering the benefits/losses of both itself and an opponent. As a result, Masbiole can escape from Nash Equilibria and obtain better performances than conventional MAS where agents consider only their own benefits. This paper focuses on the evolutionary model of Masbiole, and its characteristics are examined especially with an emphasis on the behaviors of agents obtained by symbiotic evolution. In the simulations, two ideas suitable for the effective analysis of such behaviors are introduced; "Match Type Tile-world (MTT)" and "Genetic Network Programming (GNP)". MTT is a virtual model where tile-world is improved so that agents can behave considering their symbiotic relations. GNP is a newly developed evolutionary computation which has the directed graph type gene structure and enables to analyze the decision making mechanism of agents easily. Simulation results show that Masbiole can obtain various kinds of behaviors and better performances than conventional MAS in MTT by evolution.

A study of evolutionary multiagent models based on symbiosis

Genetic network programming (GNP) has been proposed as a new method of evolutionary computation. Until now, GNP has been applied to various problems and its effectiveness was clarified. However, these problems were virtual models, so the applicability and availability of GNP to the real-world applications have not been studied. In this paper, as a first step of applying GNP to the real-world applications, elevator group supervisory control systems (EGSCSs) are considered. Generally, EGSCSs are complex and difficult problems to solve because they are too dynamic and probabilistic. So the design of a useful controller of EGSCSs was very difficult. Recently, the design of such a controller of EGSCSs has been tried actively using artificial intelligence (AI) technologies. In this paper, it is reported that the design of a controller of EGSCSs has been studied using GNP whose characteristic is to use directed graph as its gene instead of bit strings and trees of GA and GP. From simulations, it is clarified that better solutions are obtained by using GNP than other conventional methods and the availability of GNP to real-world applications is confirmed.

Elevator group supervisory control systems using genetic network programming

A new graph-based evolutionary algorithm named “Genetic Network Programming, GNP” has been proposed. GNP represents its solutions as directed graph structures, which can improve the expression ability and performance. Since GA, GP and GNP already proposed are based on evolution and they cannot change their solutions until one generation ends, we propose GNP with Reinforcement Learning (GNP with RL) in this paper in order to search solutions quickly. Evolutionary algorithm of GNP makes very compact directed graph structure which contributes to reducing the size of the Q-table and saving memory. Reinforcement Learning of GNP improves search speed for solutions because it can use the information obtained during tasks.

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Genetic Network Programming with Reinforcement Learning and Its Performance Evaluation

The Elevator Group Supervisory Control Systems (EGSCS) are the control systems that systematically manage three or more elevators in order to efficiently transport the passengers in buildings. Double-deck elevators, where two cages are connected with each other, are expected to be the next generation elevator systems. Meanwhile, Destination Floor Guidance Systems (DFGS) are also expected in Double-Deck Elevator Systems (DDES). With these, the passengers could be served at two consecutive floors and could input their destinations at elevator halls instead of conventional systems without DFGS. Such systems become more complex than the traditional systems and require new control methods Genetic Network Programming (GNP), a graph-based evolutionary method, has been applied to EGSCS and its advantages are shown in some previous papers. GNP can obtain the strategy of a new hall call assignment to the optimal elevator because it performs crossover and mutation operations to judgment nodes and processing nodes. In studies so far, the passenger's arrival has been assumed to take Exponential distribution for many years. In this paper, we have applied Erlang distribution and Binomial distribution in order to study how the passenger's arrival distribution affects EGSCS. We have found that the passenger's arrival distribution has great influence on EGSCS. It has been also clarified that GNP makes good performances under different conditions.

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Effects of passenger's arrival distribution to double-deck elevator group supervisory control systems using genetic network programming

Adaptive and robust control has attracted increasing attention in the field of artificial intelligence. Adaptive controller makes use of some adaptation mechanisms which are designed to learn explicitly the unknown parameters of the system and the uncertain situation under control. An evolutionary algorithm called “Genetic Network Programming, GNP” has been already proposed to control intelligence systems. In conventional GNP, when they have a certain problems such as the hardware or software of the system, the nodes and connections of GNP may not work well. In this paper, GNP with SARSA Learning is applied to construct the robust GNP for autonomous robots.

Robust Genetic Network Programming using SARSA Learning for autonomous robots

A new evolutionary algorithm named " genetic network programming, GNP" has been proposed. GNP represents its solutions as network structures, which can improve the expression and search ability. Since GA, GP, and GNP already proposed are based on evolution and they cannot change their solutions until one generation ends, we propose GNP with learning and evolution in order to adapt to a dynamical environment quickly. Learning algorithm improves search speed for solutions and evolutionary algorithm enables GNP to search wide solution space efficiently.

Genetic network programming with learning and evolution for adapting to dynamical environments

Residual dried blood spots (rDBS) from newborn screening programmes represent a valuable resource for medical research, from basic sciences, through clinical to public health. In Hong Kong, there is no legislation for biobanking. Parents' view on the retention and use of residual newborn blood samples could be cultural-specific and is important to consider for biobanking of rDBS.To study the views and concerns on long-term storage and secondary use of rDBS from newborn screening programmes among Hong Kong Chinese parents.A mixed-method approach was used to study the views and concerns on long-term storage and secondary use of rDBS from newborn screening programmes among Hong Kong Chinese parents of children 0-3 years or expecting parents through focus groups (8 groups; 33 participants) and a survey (n = 1012, 85% mothers) designed with insights obtained from the focus groups. We used framework analysis to summarise the themes as supportive factors, concerns and critical arguments for retention and secondary use of rDBS from focus group discussion. We used multiple logistic regression to assess factors associated with support for retention and secondary use of rDBS in the survey.Both in focus groups and survey, majority of parents were not aware of the potential secondary use of rDBS. Overall secondary use of rDBS in medical research was well accepted by a large proportion of Hong Kong parents, even if all potential future research could not be specified in a broad consent. However parents were concerned about potential risks of biobanking rDBS including leaking of data and mis-use of genetic information. Parents wanted to be asked for permission before rDBS are stored and mainly did not accept an "opt-out" approach. The survey showed that parents born in mainland China, compared to Hong Kong born parents, had lower awareness of newborn screening but higher support in biobanking rDBS. Higher education was associated with support in rDBS biobanking only among fathers.Long-term storage and secondary use of rDBS from newborn screening for biomedical research and a broad consent for biobanking of rDBS are generally acceptable to Hong Kong parents given their autonomy is respected and their privacy is protected, highlighting the importance of an accountable governance and a transparent access policy for rDBS biobanks. 

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J. Hu

Papers

Genetic network programming with learning and evolution for adapting to dynamical environments

The view of Hong Kong parents on secondary use of dried blood spots in newborn screening program