Idiotypic Immune Networks in Mobile Robot Control

TL;DR

This paper explores how idiotypic immune networks can enhance mobile robot control by integrating them with reinforcement learning, demonstrating their effects through comparative experiments with increasing complexity.

Contribution

It introduces a method to incorporate idiotypic immune networks into reinforcement learning for robot control and provides experimental evidence of their benefits.

Findings

Idiotypic networks improve robot navigation performance.

Hybrid AIS-RL systems outperform basic RL in maze tasks.

Complexity of the network correlates with better control outcomes.

Abstract

Jerne's idiotypic network theory postulates that the immune response involves inter-antibody 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 non-idiotypic systems. This paper aims to address these issues. A method for integrating an idiotypic ais network with a Reinforcement Learning based control system (rl) 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.