Idiotypic Immune Networks in Mobile-Robot Control

Abstract

Abstract-Jerne's idiotypic-network theory postulates that the immune response involves interantibody stimulation and suppression, as well as matching to antigens. The theory has proved the most popular artificial immune system (AIS) model for incorporation into behavior-based robotics, but guidelines for implementing idiotypic selection are scarce. Furthermore, the direct effects of employing the technique have not been demonstrated in the form of a comparison with nonidiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic AIS network with a reinforcement-learning (RL)-based control system is described, and the mechanisms underlying antibody stimulation and suppression are explained in detail. Some hypotheses that account for the network advantage are put forward and tested using three systems with increasing idiotypic complexity. The basic RL, a simplified hybrid AIS-RL that implements idiotypic selection independently of derived concentration levels, and a full hybrid AIS-RL scheme are examined. The test bed takes the form of a simulated Pioneer robot that is required to navigate through maze worlds detecting and tracking door markers.Index Terms-Artificial immune system (AIS), behavior arbitration mechanism, idiotypic-network theory, reinforcement learning (RL).

The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms

“…Idiotypic systems in particular exploit Jerne's notion of an idiotypic network [7], where antibodies are capable of recognizing and being stimulated or suppressed by other antibodies via their paratopes and idiotopes, see [15] for further details. Idiotypic AIS algorithms are often based on Farmer et al's computational model [1] of Jerne's theory and are characterized by a decentralized behaviour-selection mechanism.…”

“…Previous papers have provided evidence that the idiotypic AIS architecture has advantages over a reinforcement learning scheme when applied to mobile robot navigation problems [15] and have shown that the idiotypic LTL-STL architecture permits transference from the simulator to a number of different real-world environments [16]. The chief aim of this paper is, therefore, to supply further support for the robustness, scalability and adaptability of the architecture by showing that it can be extended to the much larger scale Pioneer P3-DX robots.…”

An Idiotypic Immune Network As a Short-Term Learning Architecture for Mobile Robots

“…This paper will hence also extend the research in [2] to include a different type of robot (e-puck), more environments, different problems, the real domain, an alternative RL strategy (see section 4.4), and a variable idiotope (see section 4.2).…”

“…For example, [3]- [7] all use GAs but evolve only the network links between the antibodies, which are hand-designed, fixed, and small in number. Reference [2] also uses a fixed set of pre-engineered antibodies. In contrast, the LTL phase of this research [1] uses a GA where six basic antibody-types are encoded with a set of six variable attributes that can take many different values, meaning that the system can evolve complete sets of simple but very diverse antibodies.…”

A Multi-Learning Immune Algorithm for Numerical Optimization

“…The most commonly used mechanisms of the biological immune response are clonal proliferation [7], negative selection [8], immune network [9], danger theory [10], and dendritic cell model [11]. Until now, AIS has been successfully applied on various complex problems, such as traveling salesman problems [12], [13], automatic clustering [14], pattern classification [15], graph drawing problems [16], mobil robot control [17], numerical optimization problems [18], and so on.…”