[Paper Review] An Artificial Immune System Model for Multi-Agents Resource Sharing in Distributed Environments

Tejbanta Singh Chingtham, G. Sahoo+1 more|arXiv (Cornell University)|Feb 24, 2011

Artificial Immune Systems Applications5 references3 citations

TL;DR

This paper proposes an Artificial Immune System (AIS) model to enable efficient, adaptive resource sharing among multiple autonomous agents in distributed environments, particularly for multi-robot systems. By mimicking biological immune system principles such as self/non-self recognition and clonal selection, the model enables agents to dynamically allocate resources, maintain energy thresholds, and enhance collective survival and performance in dynamic, unpredictable settings.

ABSTRACT

Natural Immune system plays a vital role in the survival of the all living being. It provides a mechanism to defend itself from external predates making it consistent systems, capable of adapting itself for survival incase of changes. The human immune system has motivated scientists and engineers for finding powerful information processing algorithms that has solved complex engineering tasks. This paper explores one of the various possibilities for solving problem in a Multiagent scenario wherein multiple robots are deployed to achieve a goal collectively. The final goal is dependent on the performance of individual robot and its survival without having to lose its energy beyond a predetermined threshold value by deploying an evolutionary computational technique otherwise called the artificial immune system that imitates the biological immune system.

Motivation & Objective

• To address the challenge of efficient and adaptive resource allocation in distributed multi-agent systems, especially in dynamic environments with energy constraints.
• To develop a bio-inspired solution that enables agents to cooperate while maintaining individual survival by avoiding energy depletion.
• To leverage principles from the human immune system to create a self-regulating, evolutionary computational framework for multi-agent coordination.
• To improve system resilience and adaptability in scenarios where agents must collectively achieve a goal under resource and energy limitations.

Proposed method

• Adopt an Artificial Immune System (AIS) framework inspired by the human immune system’s ability to distinguish self from non-self and respond to threats.
• Implement clonal selection and immune memory mechanisms to allow agents to adaptively respond to environmental changes and resource demands.
• Model agents as immune cells that recognize and respond to resource requests based on self/non-self criteria, ensuring only beneficial interactions occur.
• Integrate energy threshold constraints into the AIS model to prevent agent exhaustion, simulating biological homeostasis.
• Use evolutionary computation techniques to iteratively improve agent behavior and resource allocation strategies through selection and mutation.
• Enable decentralized decision-making where each agent autonomously manages its own resources based on local sensing and immune-like rules.

Experimental results

Research questions

• RQ1 How can an artificial immune system be adapted to manage resource sharing among multiple autonomous agents in a distributed environment?
• RQ2 What mechanisms from the biological immune system can be effectively mapped to multi-agent coordination to ensure robustness and adaptability?
• RQ3 How does the integration of energy constraints into the AIS model affect agent survival and collective performance?
• RQ4 To what extent can immune-inspired mechanisms improve dynamic resource allocation without centralized control?
• RQ5 Can self/non-self recognition principles in AIS enhance security and efficiency in multi-agent resource sharing?

Key findings

• The proposed AIS model successfully enables multi-agent systems to achieve collective goals while maintaining individual agent energy levels within safe thresholds.
• The system demonstrates improved adaptability to dynamic environmental changes through immune-inspired learning and response mechanisms.
• Clonal selection and self/non-self recognition in the model reduce inefficient or harmful resource interactions, enhancing system stability.
• Decentralized operation allows for scalable and robust coordination without reliance on a central authority.
• The model shows enhanced resilience in resource allocation under uncertainty, outperforming non-bio-inspired alternatives in simulation scenarios.
• Energy conservation is effectively maintained across agents, with no agent exceeding the predefined energy threshold during operation.