13 research outputs found
An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to
solving mobile robot navigation problems is presented and tested in both real
and simulated environments. The LTL consists of rapid simulations that use a
Genetic Algorithm to derive diverse sets of behaviours. These sets are then
transferred to an idiotypic Artificial Immune System (AIS), which forms the STL
phase, and the system is said to be seeded. The combined LTL-STL approach is
compared with using STL only, and with using a handdesigned controller. In
addition, the STL phase is tested when the idiotypic mechanism is turned off.
The results provide substantial evidence that the best option is the seeded
idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS
for the STL. They also show that structurally different environments can be
used for the two phases without compromising transferabilityComment: 13 pages, 5 tables, 4 figures, 7th International Conference on
Artificial Immune Systems (ICARIS2008), Phuket, Thailan
An idiotypic immune network as a short-term learning architecture for mobile robots
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations
that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined
LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferability
Mimicking the Behaviour of Idiotypic AIS Robot Controllers Using Probabilistic Systems
Previous work has shown that robot navigation systems that employ an
architecture based upon the idiotypic network theory of the immune system have
an advantage over control techniques that rely on reinforcement learning only.
This is thought to be a result of intelligent behaviour selection on the part
of the idiotypic robot. In this paper an attempt is made to imitate idiotypic
dynamics by creating controllers that use reinforcement with a number of
different probabilistic schemes to select robot behaviour. The aims are to show
that the idiotypic system is not merely performing some kind of periodic random
behaviour selection, and to try to gain further insight into the processes that
govern the idiotypic mechanism. Trials are carried out using simulated Pioneer
robots that undertake navigation exercises. Results show that a scheme that
boosts the probability of selecting highly-ranked alternative behaviours to 50%
during stall conditions comes closest to achieving the properties of the
idiotypic system, but remains unable to match it in terms of all round
performance.Comment: 7 pages, 2 figures, 6 tables, 13th World Multi-Conference on
Systemics, Cybernetics and Informatics: WMSCI 2009, Orlando, Florida, US
The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms
This paper demonstrates that a set of behaviours evolved in simulation on a
miniature robot (epuck) can be transferred to a much larger scale platform (a
virtual Pioneer P3-DX) that also differs in shape, sensor type, sensor
configuration and programming interface. The chosen architecture uses a
reinforcement learning-assisted genetic algorithm to evolve the epuck
behaviours, which are encoded as a genetic sequence. This sequence is then used
by the Pioneers as part of an adaptive, idiotypic artificial immune system
(AIS) control architecture. Testing in three different simulated worlds shows
that the Pioneer can use these behaviours to navigate and solve object-tracking
tasks successfully, as long as its adaptive AIS mechanism is in place.Comment: 12 pages, 3 figures, 2 tables, 9th International Conference on
Artificial Evolution (EA 09)
Mimicking the behaviour of idiotypic AIS robot controllers using probabilistic systems
Previous work has shown that robot navigation systems that employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control techniques that rely on reinforcement learning only. This is thought to be a result of intelligent behaviour selection on the part of the idiotypic robot. In this paper an attempt is made to imitate idiotypic dynamics by creating controllers that use reinforcement with a number of different probabilistic schemes to select robot behaviour. The aims are to show that the idiotypic system is not merely performing some kind of periodic random behaviour selection, and to try to gain further insight into the processes that govern the idiotypic mechanism. Trials are carried out using simulated Pioneer robots that undertake navigation exercises. Results show that a scheme that boosts the probability of selecting highly-ranked alternative behaviours to 50% during stall conditions comes closest to achieving the properties of the idiotypic system, but remains unable to match it in terms of all round performance
Two-Timescale Learning Using Idiotypic Behaviour Mediation For A Navigating Mobile Robot
A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to
solving mobile-robot navigation problems is presented and tested in both the
real and virtual domains. The LTL phase consists of rapid simulations that use
a Genetic Algorithm to derive diverse sets of behaviours, encoded as variable
sets of attributes, and the STL phase is an idiotypic Artificial Immune System.
Results from the LTL phase show that sets of behaviours develop very rapidly,
and significantly greater diversity is obtained when multiple autonomous
populations are used, rather than a single one. The architecture is assessed
under various scenarios, including removal of the LTL phase and switching off
the idiotypic mechanism in the STL phase. The comparisons provide substantial
evidence that the best option is the inclusion of both the LTL phase and the
idiotypic system. In addition, this paper shows that structurally different
environments can be used for the two phases without compromising
transferability.Comment: 40 pages, 12 tables, Journal of Applied Soft Computin
Immune Network Algorithm applied to the Optimization of Composite SaaS in Cloud Computing
Abstract-In order to serve the different application needs of the different Cloud users efficiently and effectively, a possible solution is the decomposition of the software or so-called composite SaaS (Software as a Service). A composite SaaS constitutes a group of loosely-coupled applications that communicate with each other to form higher-level functionality. The benefits to the SaaS providers are reduced delivery cost and flexible SaaS functions, and the benefit for the users is the decreased cost of subscription. For this to be achieved effectively, the optimization of the process is required in order to manage the SaaS resources in the data center efficiently. In this paper, the optimization task of composite SaaS is investigated using an Immune network optimization approach. The approach makes use of activation and suppression that are mimicked by the natural immune system triggering an immune response not only when antibodies interact with antigens but also when they interact with other antibodies. Experiments are conducted with a series of SaaS configurations and the proposed immune network algorithm is compared with a formerly proposed grouping genetic algorithm. The results show that the immune network algorithm outperforms the grouping genetic algorithm