Artificial immune systems: application to autonomous agents

Based on the analogies between multi autonomous robots system (MARS) and immune system, a synthesized immune network is proposed, and used to solve the navigation and coordination problem on MARS. Individual robot was regarded as small-scaled immune networks (SN). Task was regarded as antigen, and behavior tactics were deemed to the antibodies respectively. Behavior tactic to a robot sensor was taken as B cell. Navigation and coordination problem is transformed into the interaction mechanism among antibody, antigen and small-scaled immune networks. The pursuit problem was used to validate the hypothesis. Simulation results suggest that the proposal is promising.

show abstract

“…The basic concept of immune theory is discussed in [6] [8]. The relevant immune concepts used in this paper are given here.…”

Section: Synthesized Immune Networkmentioning

confidence: 99%

“…Multi-agent Systems [8] : Deal with behavior management in collections of several independent entities, or agents. Multi autonomous robotics system (MARS) has a significant feature, in which each agent regards the environment, and the other robots' behaviors as objects.…”

Section: Analogymentioning

confidence: 99%

Artificial immune-network based autonomous mobile robots navigation and coordination

Duan

Wang

2005

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…On the base of Jerne's idiotypic network hypothesis, Ishiguro et al [3] built a behavior controller based on the artificial immune network and fmished the garbage collection. Meshref et al [4] solved the dog and sheep problem in a distributed autonomous robotics system (DARS) by using Farmer's model. Wang et al [5] converted the path planning into the space search in antibody network by using the stimulation and suppression between the antigen and antibody, and presented a new simple immune network algorithm (SINA).…”

Section: Introductionmentioning

confidence: 99%

A novel immune algorithm for mobile robot path planning based on multi-population competition

Shen

Jiang

Yuan

et al. 2013

2013 Sixth International Conference on Advanced Computational Intelligence (ICACI)

View full text Add to dashboard Cite

In order to avoid trapping into local minimum and further improve optimization ability of simple immune genetic algorithm, a novel mUlti-population competition-based immune genetic algorithm (MPCIGA) is presented firstly. In the proposed MPCIGA, there are three populations, two of them play the role of learners and evaluators, and they stimulate each other's surviving ability through the relative fitness; the remaining population plays the role of hall of fame and evolves according to the absolute fitness and population concentration. Finally, in order to solve the path planning in mobile robot autonomous navigation, the MPCIGA is used to finish the path search in complicated environments. Compared with basic immune genetic algorithm (BIGA), the simulation results show that the proposed MPCIGA not only solves the mobile robot path planning, but also the length of planned path is shorter, which shows the validity of the proposed MPCIGA.

show abstract

“…Models based on the immune system principles, such as the clonal selection theory [8][9][10], the immune network model [11,12] or the negative selection algorithm [13], have been finding increasing application in fields of science and engineering [14][15][16][17][18][19][20][21][22], such as computer security, virus detection, process monitoring, fault diagnosis, and pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

An immune memory clonal algorithm for numerical and combinatorial optimization

Liu

Jiao

et al. 2010

Front. Comput. Sci. China

View full text Add to dashboard Cite

Inspired by the clonal selection theory together with the immune network model, we present a new artificial immune algorithm named the immune memory clonal algorithm (IMCA). The clonal operator, inspired by the immune system, is discussed first. The IMCA includes two versions based on different immune memory mechanisms; they are the adaptive immune memory clonal algorithm (AIMCA) and the immune memory clonal strategy (IMCS). In the AIMCA, the mutation rate and memory unit size of each antibody is adjusted dynamically. The IMCS realizes the evolution of both the antibody population and the memory unit at the same time. By using the clonal selection operator, global searching is effectively combined with local searching. According to the antibody-antibody (Ab-Ab) affinity and the antibody-antigen (Ab-Ag) affinity, The IMCA can adaptively allocate the scale of the memory units and the antibody population. In the experiments, 18 multimodal functions ranging in dimensionality from two, to one thousand and combinatorial optimization problems such as the traveling salesman and knapsack problems (KPs) are used to validate the performance of the IMCA. The computational cost per iteration is presented. Experimental results show that the IMCA has a high convergence speed and a strong ability in enhancing the diversity of the population and avoiding premature convergence to some degree. Theoretical roof is provided that the IMCA is convergent with probability 1.

show abstract

Artificial immune systems: application to autonomous agents

Cited by 24 publications

References 7 publications

Artificial immune-network based autonomous mobile robots navigation and coordination

Artificial immune-network based autonomous mobile robots navigation and coordination

A novel immune algorithm for mobile robot path planning based on multi-population competition

An immune memory clonal algorithm for numerical and combinatorial optimization

Contact Info

Product

Resources

About