An investigation of the use of past experience in single and multiple agent learning classifier systems
Creators: name: Foster, Kate Y.
Publisher: name: Swinburne
Year: 2024

description: The field of agent control is concerned with the design and implementation of components that form an agent's control architecture. The interaction between these components determines how an agent's sensor data and internal state combine to direct how the agent will act. Rule-based systems couple sensing and action in the form of condition-action rules and one class of such systems, learning classifier systems, has been extensively used in the design of adaptive agents. An adaptive agent explores an often unknown environment and uses its experience in its environment with the aim of improving its performance over time. The data an adaptive agent receives regarding the current state of its environment might be limited and ambiguous. In learning classifier systems, three different approaches to the problem of limited and ambiguous data from the environment have been: (1) to enable the agent to learn from its past experience, (2) to develop sequences of rules (in which rules may be linked implicitly or explicitly) and (3) multiagent LCSs. This thesis investigates the use of an adaptive agent's past experience as a resource with which to perform a number of functions internal to the agent. These functions involve developing explicit sequences of rules, detecting and escaping from infinite loops, and firing and reinforcing rules. The first part of this thesis documents the design, implementation and evaluation of a control system that incorporates these functions. The control system is realised as a learning classifier system and is evaluated through experiments in a number of environments that provide limited and ambiguous stimuli. These experiments test the impact of explicit sequences of rules on the performance of a learning classifier system more thoroughly than previous research achieved. The use of explicit sequences of rules results in mixed performance in these environments and it is shown that while the use of these sequences in simple environments enables the rule space to be more effectively explored, in complex environments the behaviours developed by these sequences result in the agent stagnating more often in corners of the environment. Rather than endowing the rule-base with more rules, as in previous research with explicit sequences of rules, it is proposed that multiple interacting agents may enhance the exploration of the rule space in more complex environments. This approach is taken in the second part of this thesis, where the control system is used with multiple agents that interact by sharing rules. The aim of this interaction is to enhance the rule discovery process through cooperation between agents and thus improve the performance of the agents in their respective environments. It is shown that the benefit obtained from rule sharing is dependent on the environment and the type and amount of rule sharing used and that rule sharing is generally more beneficial in complex environments compared to simple environments. The properties of the rule-bases developed in these environments are examined in order to account for these results and it is shown that the type and amount of rule sharing most useful in each environment are dependent on these properties.