Behavioural Plasticity Can Help Evolving Agents in Dynamic Environments but at the Cost of Volatility

Abstract

Neural networks have been widely used in agent learning architectures; however, learnings for one task might nullify learnings for another. Behavioural plasticity enables humans and animals alike to respond to environmental changes without degrading learned knowledge; this can be achieved by regulating behaviour with neuromodulation—a biological process found in the brain. We demonstrate that by modulating activity-propagating signals, neurally trained agents evolving to solve tasks in dynamic environments that are prone to change can expect a significantly higher fitness than non-modulatory agents and also achieve their goals more often. Further, we show that while behavioural plasticity can help agents to achieve goals in these variable environments, this ability to overcome environmental changes with greater success comes at the cost of highly volatile evolution.

A Review of Medical Image Techniques and Methods based on a Multi-agent System

“…An agent is software that takes autonomous, logical, or preset actions to help a system achieve its goals. These agents have self-contained capabilities, including autonomy, scalability, flexibility, and the ability to solve complicated system difficulties as a group [9]. MASs have this capability because these agents, or intelligent and autonomous units, work together to meet the system's overall requirements [10].…”