Redistributing Animats Between Groups
Possible methods of implementing aggression as one of the mechanisms in the formation of social behavior in groups of robots are discussed. Aggression is seen as a way to resolve conflicts over resources. The features of the aggressive behavior of eusocial insects (ants) are used as a basis. The aggressive component is integrated into the need-emotional architecture of the robot control system, which is presented as a hybrid neuro-production system. The proposed mechanism can be used as a base for implementing various models of social behavior in group robotics.
Bayesian Belief Networks (BBN) provide a comprehensible framework for representing complex systems that allows including expert knowledge and statistical data simultaneously. We explored BBN models for estimating risky behavior rate and compared several network structures, both expert-based and data-based. To learn and evaluate models we used generated behavior data with 9393 observations. We applied both score-based and constraint-based structure learning algorithms. The score-based structures represented better quality scores according to BIC and log-likelihood, prediction quality was almost the same for databased models and lower but sufficient for expert-based models. Hence, in case of limited data we can reduce computations and apply expert-based structure for solving practical issues.
The key regulator in the control of aggressive behavior is dopamine receptors. Association of variants in these genes with aggression has been shown in modern populations. However, these studies have not been conducted in traditional cultures. The aim of our study was to investigate population features in distributions of allele and genotype frequencies of DRD2 rs1800497, DRD4 120 bp Ins, and DRD4 exon III polymorphisms and their associations with aggressive behavior in the traditional African populations of Hadza and Datoga, which display a contrast in their culturally permitted aggression. Overall, 820 healthy unrelated Hadza and Datoga individuals were studied. Self‐rated scores of aggression were collected using Buss and Perry's Aggression Questionnaire. Polymerase chain reaction‐Restriction fragment length polymorphism (PCR‐RFLP) was used to determine the genotype of each individual. We show that the Hadza and the Datoga differed significantly in allele and genotype frequencies of all studied loci. Our association analysis detected that only ethnicity and sex of individuals significantly influenced their aggression rank, but we failed to identify any associations of DRD2 rs1800497, DRD4 120 bp Ins, or DRD4 exon III polymorphisms with aggression. Thus, our data have no strong evidence to support the involvement of polymorphisms of DRD2 and DRD4 in controlling aggressive behavior.
One of the widespread approaches to the issues of control in the group robotics is application of the social behavior models in the groups of robots. In this paper the author proposes to use this approach to fulfill the tasks of foraging. As a role model a Formicidae ant is proposed. This task is considered as a combination of three stages: finding food, returning to the ant hill and repeating the way to the place where food was found. It was proven that in order to come back home and repeatedly walk the way the Formicidae ants were navigated predominantly by the visual means using vector navigation (path integration) and landmark-guidance mechanisms. The basis of the proposed method is formed by the principles of memorizing the way by the visual landmarks and fuzzy control. The model of describing the way is introduced to the robot, which can define colors of the landmarks and approximately sense the direction to the landmark in respect to itself. A pattern for formation of a succinct way description was created, with the help of which the scout robot memorizes the way to the “food”. Certain regulations were developed, which let the follower robot transfer from the description of the route to the actions of its reproduction and in many ways copy an ant’s behavior. The actions are presented as elementary behavioral procedures, and each behavioral procedure is realized as a finite state automata. The results of the simulation modeling, which was conducted with the help of the framework of ROS based modeling system, are presented. Experiments were conducted in polygons with barriers and without them, with regular and irregular placing of the landmarks. As a quality criterion for the proposed method the author offers to consider a successful passing of the route by the follower robot, and this indicator in different series of experiments varies from 92 up to 98 %. The proposed method does not require robot’s great computation capacity and advanced sensory abilities. The developed method can also be applied to the tasks of reconnaissance and patrolling.
This paper discusses a problem of distributed data processing in mobile robot’s group. The robot’s group is typified as a static swarm. Static swarm is a model, which is characterized by the absence of a control сenter and is represented by the network with fixed topology at some time interval which consists of locally interacting agents. The main features that distinguish the robot’s group general database from the classic distributed database are described. It is shown that the database does not require the global data dictionary storing information about the location of the database fragments. The data structure on each node is the same and can be described in the reference table. This table is loaded into robot’s memory when robot is initialized and, in fact, is the data dictionary. This database does not require distributed transactions, because data is written in the general database locally. The approach of logical queries organization in general database is offered. Definitions of imprecise and inconsistent data conformably to data which robots in the group are exchanging with are given. The approach of processing imprecise and inconsistent data which come to robot is proposed. This approach is based on elements of multisets theory, fuzzy sets theory and on evaluation of data reliability degree. The reliability degree is based on the experience of the previous data exchanges between robots. An important feature of the proposed method of imprecise and inconsistent data processing is that it is computationally simple and does not require much memory to store auxiliary information.